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MSc Physics and Astronomy
Physics of Life and Health
Master Thesis
Knowledge-based radiotherapy treatment planning for
stage III lung cancer patients
by
Evgenia Tourou
11128879
June 2018
60EC
Supervisor/Examiner: Examiner:
Wilko F.A.R. Verbakel, PhD Geert J. Streekstra, PhD
Daily Supervisor:
Alexander R. Delaney, MSc
Radiotherapy Department, VUmc medical center
ABSTRACT
Treatment of large volume lung cancer is carried out mostly using two techniques, the full-RapidArc
(f-RA) or the hybrid-RapidArc (h-RA). The choice between the two methods depends on the
individual characteristics of the patient, while the treatment planners often have to make both plans in
order to choose for the optimal treatment technique for the patient. However, manual treatment
planning is a labor-intensive and time consuming process which, in many cases, does not
yield consistent or optimal plans. RapidPlan (Varian Medical Systems, Palo Alto, USA), a
knowledge-based-planning solution, uses the dosimetry and geometry of previous treatment
plans to model and predict organ-at-risk (OAR) dose-volume-histograms (DVHs) for future
patients based solely on their geometry. The present study investigates the possibility of utilizing
RapidPlan, as a tool for selecting f-RA or h-RA technique for individual lung cancer patients, without
the requirement of creating actual treatment plans. A f-RA and a f-RA model were created,
consisting of 50 clinical plans each, and were used to generate dose predictions and
subsequently to optimize model-based plans (MBPs) for a group of 10 patients. MBPs quality
was analyzed by benchmarking MBPs against the manual plans (MPs) made by experienced
radiotherapy treatment planners. DVH prediction accuracy was analyzed by comparing predicted
vs achieved OAR dose metrics. Finally, the number of patients that would have been selected
for f-RA or h-RA based solely on OAR predictions was compared to the corresponding
number of patients that would have been selected based on the achieved OAR doses in
MBPs. MBPs improved contralateral lung (CL) and total lung (TL-PTV) mean dose compared to
the manual plans in both techniques. However, CL V5 in the f-RA MBPs increased compared
to the MPs. The target coverage was inferior in the MBPs compared to the MPs. RapidPlan
was able to accurately predict the mean dose of CL, but it consistently underestimated the
amount of sparing that could be achieved for TL-PTV. Based only on comparing single OAR
dose volumes, RapidPlan can accurately predict which technique gives the lower dose in 7-9
/10 cases. The results showed that RapidPlan is able to generate MBPs of comparable quality
to the MPs for f-RA and h-RA techniques, nevertheless, it requires further validation with a
more wise selection of priorities and using generated point-objectives instead of line
objectives.
CONTENTS
1. INTRODUCTION ................................................................................................................. 1
2. METHODS AND MATERIALS ........................................................................................... 5
2.1 Treatment planning of large volume lung cancer patients .......................................... 5
2.1.1 Full-RapidArc ...................................................................................................... 5
2.1.2 Hybrid-RapidArc ................................................................................................. 6
2.2 RapidPlan .................................................................................................................... 8
2.2.1 Data extraction ..................................................................................................... 9
2.2.2 Model Training .................................................................................................. 11
2.2.3 Generation of DVH Estimations ........................................................................ 13
2.2.4 Placement of Optimization Objectives .............................................................. 13
2.3 Model Libraries ......................................................................................................... 14
2.3.1 Patient geometries .............................................................................................. 15
2.3.2 Dosimetry ........................................................................................................... 16
2.3.3 Field set-up......................................................................................................... 16
2.4 Evaluation of Model Training ................................................................................... 17
2.4.1 RapidPlan-provided statistical metrics .............................................................. 17
2.4.2 Outlier analysis .................................................................................................. 18
2.4.3 Field Geometry .................................................................................................. 19
2.5 Evaluation of model-based plans .............................................................................. 19
2.5.1 Evaluation group geometries and field-set up ................................................... 20
2.5.2 Assigning Optimization objectives .................................................................... 21
2.5.3 Evaluation of prediction accuracy ..................................................................... 22
2.5.4 Using predictions to select treatment technique ................................................ 23
3. RESULTS ............................................................................................................................ 24
3.1 Evaluation of Model Training .................................................................................... 24
3.1.2 Outlier analysis .................................................................................................. 25
3.1.2 Field Geometry .................................................................................................. 26
3.2 Evaluation of Model-Based Plans .............................................................................. 27
3.3 Evaluation of prediction accuracy .............................................................................. 32
3.3.1 Contralateral Lung ............................................................................................. 32
3.3.2 Total Lung – PTV .............................................................................................. 36
3.3.3 Esophagus .......................................................................................................... 37
3.4 Individualized analysis .............................................................................................. 38
3.5 Using predictions to select treatment technique ........................................................ 43
4. DISCUSSION AND CONCLUSION.................................................................................. 47
References ................................................................................................................................ 50
APPENTIX A .......................................................................................................................... 54
APPENTIX B........................................................................................................................... 58
1
1. INTRODUCTION
Lung cancer is the most often diagnosed cancer and the first cause of death amongst
cancer patients, leading to 1.6 million deaths worldwide every year 1 . The major treatments
for lung cancer are surgery, radiotherapy, and chemotherapy. In treatment of large volume
lung cancer, radiotherapy, usually in combination with chemotherapy plays an important
role2. Radiotherapy treatment of large volume lung cancer is challenging because it requires
delivery of high dose levels to large tumor volumes, while sparing the proximal critical
organs-at-risk (OARs) such as the esophagus, spinal cord, and the heart. High dose levels to
the healthy tissues increases toxicity3 and can lead to side effects such as symptomatic
pneumonitis4–6 and esophagitis7.
Radiation therapy makes use of ionizing radiation to kill cancer cells by absorbed
energy. Thus, the aim is to deliver maximum dose to the tumor and as low dose as possible to
the surrounding normal tissue. The radiation therapy process starts with a computed
tomography (CT) scan of the patient to locate the tumor. Then, the physician delineates the
relevant targets/tumor volumes and the surrounding OARs that need to be spared. The
delivery of the treatment is done by a linear accelerator or a cobalt machine. In order to
minimize the dose to normal tissue while ensuring sufficiently high dose to the target,
multiple field directions are used. Additionally, a multileaf collimator (MLC) is utilized to
shape the radiation beams. The MLC consists of multiple metal leaves that move
independently. The leaves are placed such as the aperture of the MLC forms the shape of the
tumor, and thus shields the surrounding tissue from radiation.
Three-dimensional conformal radiotherapy (3D-CRT)8 treatment technique involves
the use of flattened radiation beams with fixed MLC leave configuration, to deliver uniform
radiation dose, while the contribution of each feild to the final dose can vary. Intensity
modulated radiotherapy (IMRT)9 delivers non-uniform radiation beam intensities per field by
computer-controlled movement of the MLC leaves. The summation of all fields leads to
relatively homogenous dose in the target, while the dose in the surrounding healthy structures
is minimized. It was proven that IMRT can reduce the dose to the healthy lung, esophagus,
and heart in lung cancer patients compared to 3D-CRT 10,11. Volumetric modulated arc
therapy (VMAT)12 is an advanced form of IMRT, where the gantry is rotated with
simultaneous movement of the MLC leaves and dose rate modulation. VMAT decreases the
2
treatment time and, with the use of full gantry rotation, generates highly conformal treatment
plans13.
The planning of 3D-CRT plans is done manually by the planner, who defines the
gantry angles, collimator angles, MLC configurations and relative weights of individual
fields. In IMRT and VMAT treatment planning, optimization algorithms are needed to
determine the MLC leaf movements. During a process called inverse treatment planning, the
planner specifies a desirable dose distribution to the target and the normal tissue by a set of
dose-volume objectives and priority factors for each delineated structure. These dose-volume
objectives include the minimum required dose to the target and the maximum required dose
to the OARs. The optimization algorithm tries to find the MLC configurations and dose rates
that will approximate the desired dose distribution by minimizing a cost function which
weights all dose-volume objectives 14.
Defining the appropriate dose-volume objectives and priorities is an essential part of
the process since these will define the final dose distribution and help to achieve the clinical
goal. Optimal selection of dose-volume objectives depends on the geometrical characteristics
of the patient such as the location and volume of the tumor and its proximity to OARs15,
therefore each patent requires special attention. The current practice is that the treatment
planner has to evaluate the plan and interactively maneuver the optimization objectives
during the optimization process until the best possible set of optimization objectives, and
consequently dose distribution- is achieved. This process is time-consuming, and leads to
inconsistencies and inter-planner and inter-institutional variability in plan quality16–18.
In recent years, there has been wide interest in the development and application of
automated treatment planning solutions, aiming to improve the consistency and quality of
radiotherapy treatment plans. Knowledge-based planning utilizes a large number of prior
treatment plans to create a model based on the dose distribution and the geometrical
characteristics of the patients19–22. This model is used to predict achievable OAR dose-
volume histograms (DVH) for prospective patients, based on its individual anatomical
characteristics. Then, for the optimization process, a line objective is generated for each OAR
below the range of the predicted OAR DVHs. Knowledge-based planning offers patient-
specific optimization objectives and thereby semi-automates the optimization process. It is
not a fully automated process because the user needs to manually define the OAR structures
and target, the prescription dose and the field set-up.
RapidPlan (Varian Medical Systems, Palo Alto, USA) is a knowledge-based treatment
solution which was developed based on the work of the groups of the Duke University15,23,24
3
and Washington University19,25. Pre-clinical evaluation of RapidPlan suggested that it is
capable of generating clinically acceptable plans for lung, head and neck, esophageal, breast,
hepatocellular and prostate cancer 20,21,26–31. Particularly for large volume lung cancer, only
Fogliata et al.26 have evaluated the use of RapidPlan on VMAT technique with promising
results.
At the VUmc radiotherapy department, treatment of large volume lung cancer is
carried out mostly using two techniques, the full-RapidArc (f-RA) or the hybrid-RapidArc (h-
RA). RapidArc is the trademark used by Varian for VMAT optimization. In f-RA plans, the
radiation fields are composed of two VMAT arcs, while h-RA is a combination of multiple
conventional 3D-CRT fields and a VMAT field32. H-RA technique usually provides better
planning target volume (PTV) coverage and reduced dose to the healthy contralateral
lung32,33, but it delivers high dose levels outside the PTV within the 3D-CRT fields. On the
other hand, f-RA can spare better the spinal cord and the heart but increases the volume of
contralateral lung receiving low dose32–34.The choice between the two methods is critical and
depends on the individual characteristics of the patient. It often happens that the treatment
planners have to make both plans in order to choose for the optimal treatment technique for
the patient. This is apparently a time-consuming process.
RapidPlan has been proven not only to generate good quality plans but also to provide
accurate achievable OAR dose predictions. Tol et al.35 showed that RapidPlan predictions
only could be used as for quality assurance of head and neck plan. Furthermore, Delaney et
al.36 suggested that RapidPlan can provide accurate predictions to be used for selecting
patients for proton therapy for head and neck cancer patients. The present study investigated
the possibility of utilizing RapidPlan as a tool for selecting f-RA or h-RA technique for
individual lung cancer patients, without the requirement of creating actual treatment plans. It
must be noted the RapidPlan is designed only for IMRT and VMAT plans, therefore, there
have been no studies which investigated the application of RapidPlan on a h-RA method.
To conduct the research, two RapidPlan models were created, one for h-RA and one
for f-RA, consisting of clinical plans of patients treated at VUmc. Both models were
validated on an initial set of patients, and then dosimetry and geometric outliers were
removed. Next, the two models were used to generate dose predictions and subsequently
optimize model-based plans (MBPs) for a group of 10 patients. To evaluate the quality of the
MBPs, they were benchmarked against the manual plans (MPs) made by experienced
radiotherapy treatment planners. Then, to evaluate the accuracy of the predictions, the
generated MBPs where compared to the predicted DVHs. Finally, the number of patients that
4
would have been selected for f-RA or h-RA based solely on OAR predictions was compared
to the corresponding number of patients that would have been selected based on the achieved
OAR doses in MBPs.
5
2. METHODS AND MATERIALS
2.1 Treatment planning of large volume lung cancer patients
Treatment planning of large volume lung cancer at our department has been detailed
previously in the studies of Verbakel 32 and Blom 37. Treatment plans are optimized using the
Progressive Resolution Optimizer (PRO) algorithm version 10.0.28 in the Eclipse treatment
planning system and dose calculation is carried out using either ACUROS 11.0.31 or the
Anisotropic Analytical Algorithm (AAA) 10.0.28 using a 2.5mm grid resolution.
The targets are the internal target volume (ITV) and the planning target volume
(PTV). ITV consists of the primary tumor and regional lymph nodes with metastatic disease
and PTV includes the ITV and a margin of 10mm to compensate for any geometric
inaccuracies. Prescription dose (PD) to the ITV and PTV is typically 50-66 Gy and is
delivered in 23-33 fractions. Treatment plans aim to deliver 97% of the PD to at least 95% of
the PTV, while V107%(the volume receiving at least 107% of the PD) should be lower than
5%.
The spared OARs typically include the contralateral lung (CL), the total lung minus
PTV (TL-PTV) (the summation of the two lungs from which the PTV volume is subtracted),
the esophagus (ESO), the spinal cord (SC), and the SC plus a 3mm margin (SC+3mm).
Generally, the CL is constrained such that the total volume receiving 5Gy (V5) is lower than
40%6. Meanwhile, objectives for the TL-PTV are V20<35%, and V5<60%. For SC and
SC+3mm, a maximum point dose objective is applied: <50Gy and <54Gy respectively. The
maximum dose for ESO should, in general, be less than 100% of the PD, but in cases where
the ESO overlaps with the PTV, a higher dose is acceptable. In order to avoid hotspots
outside the PTV, a control region (OAR-control) is created which surrounds the PTV and
contains most of the body in the planes of the PTV. Additionally, a maximum dose objective
is applied for the OAR-control at 100% of the PD.
2.1.1 Full-RapidArc
For f-RA plans, typically two full-arcs (gantry rotates from 179º to 181º) VMAT
fields with 6 MV beams are optimized simultaneously, using avoidance sectors (control
6
points where the beam is off) to avoid direct irradiation to the contralateral lung. Typically
the avoidance sectors are 90-100° long. However, there is a limitation in their use: each
beam-on and beam-off sector must be at least 15 degrees. Thus, if the beam needs to be off
from the starting angle of the rotation, partial-arcs have to be used instead. Collimator angles
are typically 10° and 15°.
The optimizer uses a simple dose calculation algorithm that does not model well
lateral electron transport, and overestimates the dose in low-density PTV regions. In the final
dose calculation, which takes into account this lateral electron transport, the dose in that
region is lower. To overcome this problem, the PTV is divided into the part that overlaps with
the lungs (PTVinLung) and the part that is out of the lung (PTVoutLung). Then, we typically
apply a PTVinLung lower objective which is placed a few Gy higher than the PTVoutLung
lower objective. Subsequently, a ‘continue previous optimization’ (CPO) is performed with
increased PTV priorities to improve PTV homogeneity38.
Typically two optimization objectives are used for each of the following treatment
planning aims : CL V5 , TL-PTV V5 and TL-PTV V20. These objectives are interactively
placed below the DVH line displayed during optimization and adapted until the lowest
possible dose is achieved, whilst maintaining good PTV dose coverage/homogeneity. For the
ESO three optimization objectives are placed around V40, V50,V60 and one maximum dose
objective of 66Gy.
2.1.2 Hybrid-RapidArc
H-RA plans consist of a conventional and a VMAT component. The conventional
component consist of typically three 3D-CRT fields of 15MV, and delivers 90% of the PD.
Field orientation generally consists of one anterior-posterior (AP) field, one posterior-anterior
(PA) field and one oblique-posterior field, with field-weights roughly set to 0.5, 0.25 and
0.25, respectively. Thus, the AP and PA fields spare the contralateral lung, while the oblique
field decreases direct irradiation through the spinal cord. The dose distribution achieved using
the conventional fields is calculated and used as a “base dose plan” for the optimizer, which
is subsequently configured to optimize the RapidArc component.
The RapidArc component delivers the remaining 10% of the dose, using 6MV beams.
Since the base dose plan delivers an inhomogeneous dose to the PTV, the RapidArc
component is meant to homogenize the dose to the PTV. A single partial-arc is typically used
7
from 181° to 30° for left-sided tumors or from 330° to 179° for right-sided tumors. The
partial-arc length can vary depending on the size and location of the tumor and, alternatively,
in some cases a full-arc with avoidance sector is used. The RapidArc component contributes
most to the outer part of the PTV, improving the dose homogeneity32.
Similar optimization objectives to the f-RA plans are used for CL and TL-PTV, while
the maximum dose objectives for the ESO, SC and SC+3mm are usually 1-2Gy lower. The
ESO is not always actively spared. The division of the PTV between in- and out-of-lung and
a CPO is not needed in h-RA plans as the majority of the dose is delivered using the
conformal-fields.
Figure 2.1 shows the field set-up and the resulting dose distribution of a left-sided
cancer patient, treated with f-RA (a,c) and h-RA (b,d). In the h-RA plan, the dose is
distributed to the anterior-posterior direction, while f-RA results in a more conformal dose
distribution. However, f-RA produces a larger low-dose volume in the surrounding normal
tissue39, which also covers a larger part of the CL.
8
Figure 2.1: Field geometry of f-RA and h-RA plans of the same patient and the
corresponding dose distributions. The PTV is depicted with red color. a) Field set-up f-RA
plan, b) Field set-up of h-RA plan, c) Dose distribution of f-RA plan, d)Dose distribution of
h-RA plan.
2.2 RapidPlan
RapidPlan is a commercial knowledge-based treatment planning system that utilizes
previous treatment plans to create DVH-estimation models which can predict a range of
DVHs for the organs-at-risk of prospective patients. It is integrated into the Eclipse treatment
planning system (Varian Medical Systems, Palo Alto, CA) and can be used to generate dose-
volume objectives which subsquently guide the optimization process of a new treatment plan.
RapidPlan is comprised of a model configuration component and a DVH estimation
component. In the model configuration component, the information of the planned patients is
9
extracted and used to train the DVH estimation model. The DVH estimation component uses
the trained model to make predictions for the OARs of a prospective patient and
automatically place dose-volume objectives on the lower boundary of these predictions. This
semi-automates the treatment planning process and these steps are further explained below.
2.2.1 Data extraction
First, a number of treatment plans (minimum 20) are selected to populate the model
library. Then, a model structure set is created by the user containing the targets and the
relevant OARs. Each structure of the treatment plans is then matched to the corresponding
structure of the model structure set. During the data extraction phase, the geometry and dose
of each structure, along with the field geometry and the prescription dose of the plan, are
extracted and converted into some characteristic metrics. Each OAR structure is divided into
the following regions (Figure 2.2):
In-field region: The part of the structure that overlaps with target projection at least
from one field view. This is the most heavily modulated region, since it receives
direct irradiation and its dose is minimized by the movement of the MLC leaves.
Overlap region: The part that is anatomically overlapping the target. This part has
dose level comparable to the target dose.
Leaf-transmission region: The part that is visible from the jaw aperture of at least one
field but is not overlapping with the target projection from any field. It receives some
dose through the closed leaves of the MLC but it does not strongly affect the
optimization.
Out-of-field region: The volume of the structure that is not visible from jaw aperture
of any field direction. This part does not receive direct irradiation.
10
Figure 2.2: Schematic represention of volume partition in transversal view40.
The relative volume of each OAR region is calculated, as well as the cumulative DVH
based on the extracted dose, sampled into 2.5mm resolution. Additionally, RapidPlan
calculates the cumulative DVH of the Geometry Expected Dose (GED) for each OAR region.
The GED is a metric that calculates the dose that would be expected in a voxel at a
certain distance from the target given the patient anatomy, prescribed dose to the target, and
position and orientation of the concerned fields. The GED does not take into account the
different levels of sparing of the OARs; it only considers general sparing of the tissue outside
the target while delivering the desired dose to the target. The dose expected in an OAR voxel
depends on the distance from the target and the fields, the orientation of the fields, the
nominal field energy, and the physical characteristics of photons15. The GED calculation also
includes heuristics about the optimal inter-field and intra-field modulation that lead to sparing
of the normal tissue. Meaning that each field can be weighted differently and each beamlet
within a field can deliver different dose by the MLC modulation, based on the shape and
orientation of the target. Therefore, if two or more fields have the same geometry, the
duplicates are not taken into account. Furthermore, the jaw position is not considered in the
calculation41.
For the whole OAR structures, the following geometric features are calculated: OAR
volume in cm3, overlap volume percentage with the joint targets, out-of-field volume
percentage and joint target volume in cm3.
11
2.2.2 Model Training
For each of the above-mentioned OAR regions, a separate DVH estimation model is
constructed during the model training phase. The in-field-region uses a combination of
Principal Component Analysis (PCA) and regression analysis, while the other three regions
use a simpler method that calculates the mean DVH and the standard deviation. A schematic
representation of training phase for the in-field region model is shown in Figure 2.3.
The PCA is applied to the DVHs of all OARs in the training set, to describe the
variance of DVH shapes in the population and to select the most significant parameters [Sohn
2007]. The methodology is based on the assumption that each DVH can be reconstructed
from the sum of the mean DVH and a few weighted Principal Components (PC). First, the
mean DVH over all the DVHs in the training set is calculated, and then subtracted from each
DVH in the training set. Then, the first PC (PC1) curve is calculated such that it explains the
most amount of variation in the training set. The projection of this PC is subtracted from each
DVH of the set and then the next PC is calculated by maximizing the variance of the
remaining curves. This process continues until at least 95% of the variance is explained by
the PCs 41. Each particular DVH of the training set is parametrized by subtracting the mean
DVH and then projecting the residual curve to each of the PCs to find the corresponding PC
coefficients or PC scores (PCS). The same principal component analysis is applied to the
GEDs of the training set and the respective GED-PCs are calculated.
The correlation between the PC scores of the DVHs (DVH-PCS) and the geometrical
features (absolute OAR volume, absolute target volume, overlap volume percentage, out-of-
field volume percentage, and GED-PCSs) in the training set is determined by stepwise
regression analysis. For each PC a separate model is used, which also includes the second
order terms of the parameters to account for the non-linear effect between two features15. The
stepwise regression procedure follows an iterative forward and backward method. First, the
most significant geometrical parameter is added in the model, and more parameters are added
in each step only if they have a significance level higher than 5%. Then, the parameters that
have a level of significance less than 5% are removed41. This results in a regression model
including the most significant geometrical parameters, whose coefficients are stored in a
matrix that can be used to estimate the DVH PCS from the geometric features. Additionally,
the standard error of each DVH PCS is calculated in order to determine the upper and lower
boundary of the estimation DVH range.
12
Figure 2.3: Schematic representation of the training phase and PCSs estimation. First, the
PCs are extracted from the DVHs. Then, each individual DVH is parameterized based on the
PCs. The regression model finds the relation between the PCSs and the geometrical
parameters. Finally, the PCSs of a new plan are estimated from the regression model and the
geomtrical parameters of patient.
13
2.2.3 Generation of DVH Estimations
The trained DVH estimation model is used to generate predictions for a new patient.
The patient's anatomy (targets and OARs), the field geometry and the prescription dose are
used as the input for the DVH estimation model. The algorithm calculates the same features
as in the data extraction phase (volume partition, geometric features, GED histograms),
except for the DVHs. For the in-field region, the GED histograms are parameterized using the
GED principal components calculated in the training phase. Then, the regression model is
applied to estimate the DVH PCSs from the geometric features. The estimated PCSs and the
stored principal components are then combined to calculate the most probable DVH curve.
The DVH estimation range is calculated from the standard error related to the regression
model. For other regions, the stored mean DVH is obtained and the estimation range is
calculated by adding and subtracting on standard deviation. Finally, the estimated DVHs
from the different OAR regions are weighted based on the relative volume of each region and
are summed together to construct the final DVH estimate for the OAR (Figure 2.4).
2.2.4 Placement of Optimization Objectives
When the DVH estimation range is generated, RapidPlan converts it into optimization
objectives to semi-automate the optimization process. A line of optimization objectives is
placed just below the lower boundary of the prediction range as seen in Figure 2.4. The
objectives pull all parts of the DVH curve with the same strength, aiming to produce a
resultant DVH which is representative of the DVH-prediction. However, if an OAR structure
overlaps with the target, the objective line corresponding to the volume that overlaps with the
target is placed horizontally so that it does not conflict with the targets lower objectives
(Figure 2.4). RapidPlan users may also specify dose-volume objectives with a certain volume
value for which the corresponding dose will be generated by the DVH-prediction range. This
can be utilized, for example, to lower specific parameters such as the V5 of an OAR, rather
than the dose to the entire OAR, which the line objective caters to.
14
Figure 2.4: Predicted DVH ranges of various organs-at-risk with the
generated optimization objective lines.
2.3 Model Libraries
Fifty-five patients previously treated with f-RA and 55 patients previously treated
with h-RA (planned according to the protocol described in section 2.1) were selected to
populate two models, the f-RA model and the h-RA model. The clinical plans were added to
the models without any modification.
In the f-RA model-library the PD for the ITV and PTV varies from 50Gy to 66Gy: 19
plans with 66Gy, 17 plans with 65Gy, 15 plans with 60Gy, 1 with 57Gy, 1 with 56Gy, 1 with
52Gy and 1 with 50Gy. Dose was delivered with fractions of 2.0-2.6Gy. The variation in PD
was not expected to affect the performance of the model as this has been previously reported
without any apparent degradation in resulting plan quality26.
In the h-RA model, the PD was 66Gy for 27 plans, 65Gy for 13 plans, and 60Gy for 15
plans. One difference between the two model-libraries was that only 16 of the 55 plans in the
h-RA model used optimization objectives for V40,V50,V60, while all plans in the f-RA model
included esophageal sparing for dose lower than 60Gy.
The following structure set was created for each model: ITV, PTV (PTVinLung and
PTVoutLung for the f-RA model), CL, TL-PTV, ESO, SC, SC+3mm, OAR-control, and
ipsilateral lung (IL). The corresponding structures were matched for each patient.
15
2.3.1 Patient geometries
A heterogeneous patient population in terms of tumor size and location was selected
in order to make the model applicable to a large variety of patient geometries. Furthermore, it
was intended that both models have patients with a similar range of volumes. Of the 55 plans
of the f-RA model, 35 had right-sided tumors while the remainder were left-sided cases, and
the PTV volume ranged from 205-1361 cm3 with an average of 589 cm3. In the h-RA model,
36/55 patients had right-sided tumors and the rest were left-sided cases, while the PTV
volume ranged from 201-1236 cm3, with a mean of 596 cm3. The geometric characteristics
of the PTV and OARs in the f-RA and h-RA models are listed in Table 2.1 and 2.2
respectively.
Table 2.1: Geometric features of structures in the f-RA model
Structure
Mean
volume
(𝐜𝐦𝟑)
Minimum
volume
(𝐜𝐦𝟑)
Maximum
volume
(𝐜𝐦𝟑)
Overlap with
the target
%
In-field
volume
%
PTV 589 ± 314 205 1361 - -
CL 2023 ± 604 769 3503 0.51 ± 1.23 73.1 ± 17.2
TL-PTV 36701 ± 1114 1564 7254 0.03 ± 0.05 70.9 ± 17.6
ESO 29.1 ± 15.7 6.8 91.1 21.0 ± 17.4 64.8 ± 21.0
SC 37.0 ± 17.6 11.4 82.7 0.22 ± 1.66 77.3 ± 23.1
OAR-control 5653 ± 2686 527.9 13967.4 0.08 ± 0.11 98.1 ± 5.7
IL 1869 ± 627 679.1 3868.6 11.52 ± 6.53 60.1 ± 15.4
Table 2.2: Geometric features of structures in h-RA model library
Structure
Mean
volume
(𝐜𝐦𝟑)
Minimum
volume
(𝐜𝐦𝟑)
Maximum
volume
(𝐜𝐦𝟑)
Overlap with
the target
%
In-field
volume
%
PTV 596 ± 262 201 1236 - -
CL 2044 ± 656 1016 3413 0.14 ± 0.27 75.0 ± 14.2
TL-PTV 3672 ± 1230 1473 6461 0.03 ± 0.06 71.9 ± 15.1
ESO 27.9 ± 12.5 5.9 78.9 27.3 ± 22.3 62.8 ± 22.3
SC 34.6 ± 15.4 14.1 74.9 0.01 ± 0.10 79.6 ± 22.9
OAR-control 4942 ± 2158 1365 11673 0.07 ± 0.11 97.3 ± 4.9
IL 1860 ± 672 657 3434 13.10 ±6.62 59.3 ± 14.5
16
2.3.2 Dosimetry
Table 2.3 contains the average dosimetry of plans in both the f-RA model and h-RA
model.
Table 2.3: Dosimetric features in the model libraries
Structure
f-RA model h-RA model
Mean
dose
(%)
Minimum
dose
(%)
Maximum
dose
(% )
Mean
dose
(%)
Minimum
dose
(%)
Maximum
dose
(% )
PTV 101.2 ± 1.3 99.0 103.9 100.7±1.2 98.6 104.0
CL 9.0 ± 6.2 1.1 27.5 6.06 ± 4.17 0.96 15.6
TL-PTV 19.4 ± 6.5 2.9 31.9 20.9 ±5.4 6.5 33.09
ESO 48.6 ± 19.8 8.6 84.7 55.0 ± 23.6 5.02 96.9
SC 34.1 ± 13.8 5.0 63.1 34.2 ± 16.2 5.2 71.5
OAR-control 39.6 ± 9.7 27.8 64.9 46.9 ± 15.2 27.5 74.5
IL 40.8 ± 13.0 7.5 68.9 48.0 ± 12.6 15.2 75.0
2.3.3 Field set-up
Field set-ups varied largely between plans of both the f-RA model and h-RA model,
as seen in Table 2.4 and Table 2.5, respectively. In the f-RA model, 48/55 plans used 2 full-
arcs, whilst using an avoidance sector to prevent irradiation through the healthy contralateral
lung (full-arc plans). 7/55 plans used 2 partial arcs for the same purpose (partial-arc plans).
The irradiation-arc length for the full-arc plans ranged from 240° to a maximum of 310° for a
centrally located tumor, with a mean of 270 ± 14°. Partial arcs were used in cases where the
tumors were relatively small (< 400 cm3) and the irradiation-arcs covered relatively shorter
angles, with their length ranging from 200° to 260° with mean 223 ± 23°.
Evaluation of plans in the h-RA model showed that the AP field was placed between
355°and 10° (at 0° in 51/55, at 10° in 1/55, at 8° in 1/55 and at 355 in 1/55 of plans), while
the PA field was placed between 170° and 195° (at 180° in 52/55, at 170° in 1/55 and at 195°
in 1/55). For right-sided tumors an oblique field was typically placed between 195 - 215°
with 210° and 200° being the most common field angles. In the left-sided cases, the oblique
field was placed at 150° in 9/19 cases, at 160° in 5/19 and at 155 in the remainder of cases.
Two plans used an additional oblique field: one at 210° for a left-sided tumor and one at 270°
for a right-sided tumor, while 2 other plans did not use any oblique field. In one particular
patient, with a centrally located tumor, 3 oblique fields were used at 340º, 160º, and 210º but
17
no AP or PA field was included. Regarding the RapidArc component, a full-arc was used in
12/55 plans with the irradiation-arc length ranging from 206° to 290°, with a mean of 251° ±
27°. The remainder 43 plans consisted of partial-arcs with the irradiation-arc length ranging
between 210-240°.
Table 2.4: Field geometry of plans in the f-RA model
Field set up # Plans Irradiation-arc length
Range Mean
Full-arc 48 240-310° 270 ± 14°
Partial-arc 7 200-260° 223 ± 23°
Table 2.5: Field geometry of plans in the h-RA model
Conventional component
Tumor
position # Plans
AP field PA field Oblique field
Angle range Angle range Angle range
Right 36 355-10° 170-195°
195-215°
Left 19 150-160°
RapidArc component
Field set up # plans Irradiation-arc length
Range Mean
Full-arc 12 206-290° 251 ± 27°
Partial-arc 43 210-240° 215 ± 8°
2.4 Evaluation of Model Training
2.4.1 RapidPlan-provided statistical metrics
Subsequent to model training, RapidPlan provides the user with certain quality
metrics regarding the goodness of fit and the goodness of estimation of the model. Amongst
others, RapidPlan provides the user with residual, regression, geometric and DVH-plots so
that the quality of the model can be visually assessed, as described by Delaney et al. 42.
According to the manufacturer, these metrics, as well as plots, should be evaluated before
model validation.
18
The goodness of fit statistics describe how well the DVH estimation model represents
the data in the training set. The coefficient of determination of the regression model
parameters, R2, describes how much of the variance is explained by the regression model. It
is scaled from 0 to 1, with a large value indicating a better fit. High values may also indicate
that the model is over-fitting the data, meaning that model overreacts to minor fluctuations in
the data. The regression model parameters such as the average chi square, 𝜒2, describes the
quality of the regression model. It is measured from the residual difference between the
original data and the estimated data. The closer the value is to 1, the better the quality of the
regression model. However, if it is very close to 1 it is possible that the model is being over-
fitted.
RapidPlan performs an internal cross-validation for the trained models. The plans are
divided into 10 groups. The plans of the 9 groups are used for model training, while the
remaining group is used for validating the model. This process is repeated 10 times until all
groups are used for validation. The average results of the validation processes represent the
ability of the model to estimate the DVH of a plan that is part of the training set. The mean
squared error between the original and the estimated data measures the distance between the
original DVH and the mean of the upper and lower bounds of the estimated DVH-range.
2.4.2 Outlier analysis
An outlier in the model can be a structure whose dosimetry or geometry differs from
the rest of the population, or it has a substantial effect on the model fit. RapidPlan provides
some statistical metrics and the reporting thresholds for the structures in the training set,
namely: cook’s distance (CD) which indicates influential data points, modified Z-score (mZ)
which indicates geometrical outliers and studentized residual (SR) which indicates dosimetric
outliers.
In order to examine if the model would be improved by removing the outliers, the
indicated outliers were removed from the model and the model was re-trained. The resulting
“cleaned model” and the initial “uncleaned model” were used to generate plans for 4 patients
not included in the models. The MBPs were then compared to decide which model provides
better plans.
19
2.4.3 Field Geometry
Since field set-ups in both models varied quite substantially, it was investigated
whether this variation was visible amongst the provided RapidPlan plots and statistical
metrics for these models. Furthermore, since both partial arcs and avoidance sectors were
used to limit irradiation of the contralateral lung, it was examined whether RapidPlan could
appropriately model both of these techniques. This was done by observation of the RapidPlan
provided CL regression plot: the principal component score 1 of the DVH (DVH-PCS1)
versus the principal component score 1 of the GED (GED-PCS1) (Figure 2.5); and the
residual plot: the DVH-PCS1 versus the estimated PCS1 for the DVH. In these plots, each
plan of the training set is represented by one data point. The lines represent one standard
deviation away from the regression line. Points falling outside of the lines are possible
outliers.
Figure 2.5 Regression plot: DVH-PCS1 versus the GED-PCS1 of a particular
OAR
2.5 Evaluation of model-based plans
A 10 patient evaluation group was selected arbitrarily, and already had respective f-RA
and h-RA MPs created according to the protocol mentioned in section 2.1. The PD for all the
plans was 66Gy delivered in 33 fractions. These MPs were optimized using PRO v10.0.28
and dose calculation was carried out with AAA v10.0.28. To investigate the performance of
both models, MBPs were created for this evaluation group and compared with respective
20
MPs. MBPs were optimized using the PO 13.6.23 optimization algorithm and the AAA
v10.0.28 for dose calculation, using a 2.5mm grid resolution. Field set-ups for MBPs were
the same as those for the respective MPs. For comparison reasons, the MBPs were
normalized such that the mean dose of the PTV is equal to the PTV mean dose of the MPs.
To assess the quality of f-RA and h-RA MBPs, MBPs were compared with respective MPs
on the basis of the following dosimetric parameters:
For the PTV: V95%(%) , V107%(%) , the maximum dose at the PTV in Gy Dmax(Gy),
homogeneity index (HI) defined as the minimum dose in 2% of the PTV (D2) minus
D98 divided by D50 , conformity index (CL) calculated as the absolute PTV volume
receiving 95% of the PD divided by absolute total body volume receiving 95% of the
PD.
For the OARs: V5(%) , Dmean(Gy) for the CL; V5(%) , V20(%), and Dmean(Gy) for
the TL-PTV; Dmax(Gy) for SC and SC+3mm; Dmax(Gy) and Dmean(Gy) for ESO;
and Dmax(Gy) for the OAR-control.
Paired, 2-sided student t-tests were performed to identify significant differences between the
MPs and MBPs with p<0.05.
2.5.1 Evaluation group geometries and field-set up
Table 2.6 shows the geometries of the 10 patient evaluation group.
Table 2.6 Geometric features of test patients
Patient
#
Tumor
side
PTV Contralateral lung Total lung-PTV Esophagus Volume
(𝐜𝐦𝟑)
Volume
(𝐜𝐦𝟑)
Overlap
%
Volume
(𝐜𝐦𝟑)
Overlap
%
Volume
(𝐜𝐦𝟑)
Overlap
%
1 L 1065 3186 0.04 4964 0.0 49.8 22.4
2 R 1017 3665 0.01 5893 0.0 63.9 11.3
3 L 936 1396 0.39 1913 0.0 75.1 27.9
4 L 987 1934 0.00 2971 0.0 60.5 48.1
5 R 861 2056 0.00 3830 0.17 33.9 10.6
6 R 1210 1920 0.54 3876 0.1 11.2 90.9
7 R 888 2510 0.27 4969 0.0 27.6 10.7
8 L 1250 1947 0.66 3090 0.0 43.2 37.6
9 R 789 1975 0.00 3858 0.18 46.8 0.4
10 L 1119 2581 0.01 3983 0.0 63.2 26.2
Average: 1012±150 2317±680 0.19±0.26 3935±1140 0.05±0.08 47.5±19.3 28.6±26.1
21
f-RA plans for all 10 evaluation patients utilized a full-arc with avoidance sector such
that the irradiation-arc length varied from 240º to 280 º. This length was within the range of
the f-RA model.
h-RA plans for 8/10 evaluation patients used partial-arcs with length between 210 º
and 220º. The remaining 2 plans (patient 6 and 8) utilized full-arc set-up with avoidance
sector, with a beam-on arc length of 250º. Regarding the conventional component, the
majority of the evaluation patients had a field set-up which was within the range of field-
geometries/characteristics of the h-RA model. However, 2 patients had a largely differing
field set-up: (1) patient 3 had a plan with an oblique field set to 140º, while in the model it is
between 150º and 160º ; (2) patient 6, although the PTV was mainly located in the right lung,
had 2 oblique fields, set on 200 º and 160 º with equal weight.
2.5.2 Assigning Optimization objectives
Both the f-RA and h-RA models were used to generate objectives for the following
OARs of the evaluation group: CL (line-objective), TL-PTV (line-objective) and ESO (line-
objective and a generated maximum point dose objective at 0% volume). The priorities for
the h-RA MBPs were reflective of those in the h-RA model. The f-RA model priorities were
adjusted after validation testing on 3 patients. It was found that the f-RA model did not
appropriately spare the contralateral lung, thus incrementally increasing the priority of this
structure, and re-testing, led to the resultant priorities in Table 2.7. Line-objectives for CL
and TL-PTV were chosen over point-objectives after validation testing on 3 patients: the line-
objectives found to reduce the mean dose of the lungs, while not affecting V5 and V20.
For certain OARs manual dose-volume objectives, stipulated during model creation,
were used for all plans: SC (maximum point dose-objective), SC+3mm (maximum point
dose-objective) and OAR-control (maximum point dose-objective). These manual dose-
volume objectives, and their respective priorities, were derived from averaging the same
values of the plans in the model libraries.
Additionally, for the f-RA and h-RA MBPs, the automatic normal tissue objective
(NTO) provided by RapidPlan which guides the dose fall-off outside the target was used with
a priority of 80 and 50 respectively. For CPOs of f-RA MBPs and MPs, the priorities of the
PTVinLung and PTVoutLung were increased to 200 and 180, respectively.
22
Table 2.7 Optimization objectives and priorities for the evaluation group MPs and MBPs
Structure
f-RA h-RA
MP MBP MP MBP
Objective
(Gy)
Priority Objective
(Gy)
Priority Objective
(Gy)
Priority Objective
(Gy)
Priority
ITV lower 66 110 66 110 66 100 66 110
PTV lower in lung: 70
out of lung: 65
120
120
in lung: 66
out of lung: 65
120
120 65 130 65 130
PTV upper in lung: 74
out of lung: 69
130
130
in lung: 69
out of lung: 68
130
130 67 120 67 120
CL a 130 Line 200 a 100 Line 110
TL-PTV b 130 Line 120 b 100 Line 110
SC 45-46 130-140 43.5 140 44 110-130 43.5 120
SC+3mm 47-48 130-150 45.7 150 46 120-140 45.7 120
OES c 90 Line 90 - - Line 90
OES max 66 120 Generated 150 65 130 Generated 130
OARcontrol 67 130 66 150 66 130 66 130
NTO - - Auto 80 - - Auto 50
a: Two objectives on V5, below the DVH line that was displayed during the optimization.
b: Two objectives on V5 and two on V20 , below the DVH line that was displayed during the optimization.
c: Objectives were placed on V40, V50, V60.
2.5.3 Evaluation of prediction accuracy
RapidPlan provides a prediction range of DVHs for each OAR. In order to evaluate
the accuracy of the predictions the mid-prediction DVH line was created running through the
middle of the prediction range as described by Tol et al.35 and can be seen in Figure 2.5.
Consequently the following parameters were calculated:
- Predicted Dmean: mean dose of the mid-prediction DVH line.
- Upper boundary Dmean: mean dose of the upper boundary of the prediction range.
- Lower boundary Dmean: mean dose of the lower boundary of the prediction range.
- Predicted Dmean range = Upper boundary Dmean - Lower prediction Dmean
Besides the mean dose of the OARs, the following point-volume predictions were extracted
from the mid-prediction DVH and upper and lower boundaries: Predicted V5, Upper
boundary V5, Lower prediction V5 for CL and Predicted V20, Upper boundary V20, Lower
prediction V20 for TL-PTV as well as the corresponding predicted ranges.
23
The prediction accuracy was investigated by comparing the achieved MBP dosimetry
metrics to the predicted aforementioned parameters. The difference between predicted and
achieved dose metrics ΔV5, ΔV20 and ΔDmean was calculated for example as Predicted V5
minus achieved MBP V5. Linear regression analysis using the least square method was
performed between the predicted and achieved dose parameters.
Figure 2.5: The DVH prediction range (shaded region) and the mid-
prediction DVH line running through the middle of it (dashed line).
2.5.4 Using predictions to select treatment technique
DVH-predictions were used to assess the possibility that RapidPlan can be used to
select which modality should be used for a prospective patient. It was examined if RapidPlan
can accurately predict which technique results in lower dose for the following parameters; CL
Dmean , TL-PTV V20 , TL-PTV Dmean and ESO Dmean. These parameters were found to be
mostly associated with high risk of pneumonitis and esophagitis after radiation treatment5,6.
CL V5 has been proven to be significantly lower in the h-RA plans compared to MPs33,34, and
was therefore excluded from this analysis. The number of patients for whom the predicted f-
RA parameter was lower than the predicted h-RA parameter (for ex. f-RA Predicted CL
Dmean < h-RA Predicted CL Dmean ), was compared to the numbers of patients for whom f-
RA MBP parameter was lower than h-RA MBP parameter (f-RA MBP CL Dmean < h-RA
MBP CL Dmean).
24
3. RESULTS
3.1 Evaluation of Model Training
3.1.1 RapidPlan-provided statistical metrics
Table 3.1 shows all the structures trained in the model, and the respective R2 and χ2,
the mean squared error between the original and the estimated data (MSE) and the number of
DVH-PCs and GED-PCs extracted during model training. Regression models for both f-RA
and h-RA showed good correlation between the geometric and dosimetric features of the CL,
TL-PTV, and ESO with R2>0.8. However, the TL-PTV of the h-RA model had a slightly
inferior R2 of 0.65. 𝜒2 values were close to 1 for all OAR structures, showing that residuals
from the regression model are independent43. MSE values for the CL, TL-PTV and ESO were
0.0013, 0.0011 and 0.0063 respectively for the f-RA model, and 0.0010, 0.0016 and 0.0045
for the h-RA model, respectively, showing good estimation capability of both models.
SC, SC+3mm, OARcontrol models were not used for generating optimization-
objectives because training results showed poor quality. However, the results are reported for
analytical purposes. SC and SC+3mm models resulted in very low R2 values. There is also
large variance in the DVH shapes, as indicated by the large number of DVH-PCs needed to
describe the dosimetric variability. The IL model training results are comparable to the CL
although it was not spared in the MPs. Nevertheless, the TL-PTV, which includes the IL, was
spared, and thus the IL DVHs shapes did not vary a lot.
Table 3.1: Summary of training results
Model
structure
f-RA model h-RA model
𝐑𝟐 𝐱𝟐 DVH-
PCs
GED-PCs
MSE 𝐑𝟐 𝐱𝟐 DVH-
PCs GED-PCs
MSE
CL 0.834 1.082 2 2 0.0013 0.807 1.099 2 2 0.0010
TL-PTV 0.848 1.116 3 2 0.0011 0.647 1.068 3 3 0.0016
IL 0.800 1.102 2 3 0.0015 0.706 1.099 2 3 0.0022
ESO 0.841 1.094 3 3 0.0063 0.886 1.102 2 3 0.0045
SC 0.586 1.024 4 4 0.0135 0.710 1.078 3 3 0.0187
SC+3mm 0.636 1.066 3 3 0.0120 0.712 1.059 3 3 0.0146
OARcontrol 0.932 1.056 2 2 0.0015 0.667 1.084 1 1 0.0071
R2: Coefficient of determination for the regression model parameters.
x2: Average chi square for the regression model parameters.
MSE: Mean square error between original and estimated data
25
3.1.2 Outlier analysis
In the f-RA model, 5 plans where indicated as outliers for the CL structure by the
model statistics: 3 plans had high CD value, indicating possible influential data points, while
2 plans had large mZ value, indicating possible geometrical outliers. Three TL-PTV
structures were indicated as outliers, one of them had high CD value and the rest high mZ
value. The same holds for the ESO model. After visual observation of the residual and
regression plots, only one plan from the suggested outliers could be noticed laying more than
2 sd away from the regression line for CL and TL-PTV, while it was close to identity line in
the residual plot. However, the geometry of this patient did not largely differ from the rest of
the patients. In the h-RA model, there was only one geometric outlier for the esophagus
structure. Since RapidPlan statistics did not suggest any outliers for CL and TL-PTV for the
h-RA model, the uncleaned h-RA model was used for the rest of the study.
After removing the suggested outliers from the f-RA model and retraining the model,
the resulting “cleaned model” had 3 new CL outliers and the R2 value was considerably
reduced from 0.834 to 0.580. There were also 3 new outliers for the TL-PTV model structure
and the R2 value was slightly decreased from 0.848 to 0.826.
The “cleaned” and “uncleaned” models were tested on 4 patients not included in the
model libraries. The dosimetric results, normalized to the mean PTV dose, are shown in
Table 3.2. Predicted Dmean range for CL were comparable for both models, while TL-PTV
predictions of the cleaned model were considerably narrower than those of the uncleaned
model. However, the uncleaned model provided lower CL and TL-PTV plan doses than that
of the cleaned model plans, while the PTV coverage was comparable between the two
models. Therefore, the uncleaned model including all the 55 plans was selected to guide the
optimization of the 10 test patients plans.
Table 3.2: Results of f-RA MBPs made using uncleaned model and cleaned model.
Patient
#
CL V5 (Gy) TL–PTV 𝐕𝟓 (Gy) TL–PTV 𝐕𝟐𝟎(Gy) PTV 𝐕𝟗𝟓%(%) Uncleaned
model
Cleaned
model
Uncleaned
model
Cleaned
model
Uncleaned
model
Cleaned
model
Uncleaned
model
Cleaned
model
1 31.9 32.9 38.9 39.5 16.2 17.5 97.3 98.7 2 33.2 44.4 50.0 56.9 26.1 27.9 97.3 97.7 3 60.6 60.5 71.3 71.2 29.4 29.3 92.8 93.0 4 39.0 43.5 60.3 63.2 27.2 27.3 99.2 98.9
Average 41.2 45.3 55.1 57.7 24.7 25.5 96.7 97.1
26
3.1.2 Field Geometry
Figure 3.1 shows the regression and residual plots for the CL and TL-PTV for the f-
RA model. The same plots of the h-RA model are shown in Figure 3.2. In Figure 3.1, the
partial-arc plans in the f-RA model are marked with red circles, while the rest of the plans
used full-arc incorporating an avoidance sector (full-arc plans). In Figure 3.2, for the h-RA
model, the full-arc plans are marked with red squares, while the rest is partial-arc plans.
The library of the f-RA model contains 7 partial-arc plans having typically low GED-
PCS1. However, in regression and residual plots, the partial-arc plans fall within 1 standard
deviation. In the h-RA model, the full-arc plans form a group on the right side of the
regression plot, having high GED-PCS1 value. Further analysis of the relationship between
the GED-PCS1 and the geometrical features showed that during the calculation of the GED
the field geometry is not modeled correctly when the avoidance sector is used (see details in
Appendix A). This problem caused the predictions of CL and TL-PTV to be higher.
However, the problem was partially solved by optimizing the plan twice (see Appendix A).
The h-RA model library contains two plans with 2 oblique fields instead of one like
the rest of the population. The first plan is also an avoidance sector plan and does not deviate
from the avoidance sector group. In the second plan, the direction of the extra oblique field is
at 270º, causing an unusually high exiting dose to the CL, for the relatively small PTV
volume of the patient (209 cm3). Thus, the plan lies above the regression and the residual
lines, meaning that the estimated CL dose is much lower than the actual dose, as expected.
Even though it can be considered as a negative outlier for the model, the CD value is 4.5,
while the threshold for outliers is 10, thus it does not have a significant effect on the
regression line. For the plan that had 3 oblique fields, the fields do not deviate more than 20º
from the AP and PA directions and thus the plan does not deviate from the rest of the training
set.
27
Figure 3.1: Regression and residual plots of the f-RA model. In red circles are the plans with partial-arc
field set-up. (a) Regression plot for CL. (b) Residual plot for CL.
Figure 3.2: Regression and residual plots of the h-RA model. In red squares are the plans with
avoidance sector field set-up. (a) Regression plot for CL. (b) Residual plot for CL.
3.2 Evaluation of Model-Based Plans
Dosimetry of the MPs and MBPs for both f-RA and h-RA method is summarized in
Table 3.3. The results are averaged over the 10-patient evaluation group. The dosimetric
results for individual patients are shown in Figure 3.3 and Figure 3.4.
In f-RA, 5/10 MBPs (#1, 2, 4, 9 and 10) and 6/10 MPs (# 1, 2, 5, 7, 9, 10) satisfied all
the clinical objectives for the OARs and the PTV. For h-RA, also 5/10 MBPs (#2, 4, 7, 9, 10)
were clinically acceptable and 6/10 MPs (# 1, 2, 4, 7, 9, 10). For the rest of them, the PTV
V95% was lower than 97%, while in some, one or more OAR requirements was also not
achieved. These plans were examined individually, and after adjusting some parameters and
repeating the optimization, it was possible to improve the plan quality (see Section 3.4).
The PTV coverage was sufficient in 5/10 MBPs and 9/10 MPs using the f-RA
method, and in 5/10 MBPs and 8/10 MPs using the h-RA method. Quantitatively, both f-RA
and h-RA MBPs resulted in lower PTV V95% on average when compared to the MPs: from
97.50±0.67 to 96.39±1.90 for the f-RA plans, and from 97.25±1.95 to 96.56±1.48 for the h-
28
RA plans. However, the difference was not statistically significant. The conformity index and
PTV V107% were slightly improved, while the PTV HI slightly increased in the MBPs.
F-RA plans were able to achieve the aim of CL V5<40% in 6/10 MBPs and 6/10 MPs.
TL-PTV V20 was clinically acceptable in 9/10 MBPs and 9/10 MPs, while TL-PTV V5 was
lower than 60% in 5/10 MBPs, and 6/10 MPs. In f-RA MBPs, CL V5 and TL–PTV V5 and
V20 increased by 4.1%, 2.5% and 1.1% on average respectively over MPs, with the increase
in TL–PTV V20 being statistically significant. However, 4/10 f-RA MBPs resulted in lower
CL V5 and TL-PTV V5 , while two of them also improved TL-PTV V20 over the respective
MPs.
Regarding the clinical aims in h-RA method, MPs and MBPs performed similarly
achieving CL V5 <40% in 7/10 patients, TL-PTV V20<35% in 9/10 patients and TL-PTV
V5<60% in 8/10 cases. In actual numbers, CL V5 and TL–PTV V5 were slightly increased -by
0.9% and 0.6% respectively- in MBPs compared to MPs, while TL–PTV V20 remained
almost the same.
MBPs improved the CL Dmean in the f-RA technique, as well as the CL Dmean
(p<0.05) and TL-PTV Dmean (p<0.05) of the h-RA technique, possibly due to the use of a
line-objective throughout the whole dose range instead of separate point objectives.
Furthermore, MBPs have a considerably lower ESO Dmax and Dmean, and SC, SC+3mm, and
OAR-control Dmax for both techniques. The MBP objectives for spinal cord and spinal cord +
3mm were stricter compared to the objectives used for the MPs and resulted in lower Dmax
for these structures. The lower ESO Dmean is attributed to the use of line objectives over the
entire dose range instead of only a few point objectives in large doses. The improved
conformity index is due to the use of the automatic NTO during the optimization of MPBs.
29
Table 3.3: Average values of dosimetric data for 10 evaluation patients
f-RA h-RA
MP MBP MP MBP
PTV
V95%(%) 97.5 ± 0.7 96.4 ± 1.9 97.3 ± 2.0 96.6 ± 1.5
V107%(%) 3.1 ± 1.5 2.8 ± 1.9 1.6 ± 2.4 0.8 ± 1.1
Dmax(Gy) 74.5 ± 0.6 74.7 ± 1.5 72.6 ± 1.6 72.0 ± 1.1
HI 0.13 ± 0.01 0.13 ± 0.02 0.11 ± 0.03 0.12 ± 0.02
CI 0.74 ± 0.06 0.78 ± 0.07ᵃ 0.50 ± 0.08 0.54 ± 0.11
CL
V5(%) 40.2 ± 13.2 44.3 ± 12.6 25.6 ± 15.0 26.5 ± 16.5
Dmean(Gy) 7.1 ± 2.4 6.8 ± 1.9 5.9 ± 2.6 5.7 ± 2.7ᵃ
TL – PTV
V5(%) 55.6 ± 11.9 58.1 ± 12.1 47.5 ± 11.6 48.1 ± 12.4
V20(%) 26.2 ± 6.3 27.3 ± 6.2ᵃ 26.3 ± 6.3 26.2 ± 6.3
Dmean(Gy) 15.6 ± 3.3 15.8 ± 3.0 16.6 ± 3.7 16.6 ± 3.8ᵃ
SC
Dmax(Gy) 49.2 ± 1.4 49.0 ± 1.7 48.4 ± 2.5 47.8 ± 2.4
SC + 3mm
Dmax(Gy) 55.6 ± 2.8 53.9 ± 3.3ᵃ 53.6 ± 3.9 53.1 ± 3.7
ESO
Dmax(Gy) 69.9 ± 0.8 68.6 ± 2.8 68.7 ± 1.3 67.8 ± 1.8
Dmean(Gy) 33.2 ± 10.4 31.8 ± 10.0 34.3 ± 11.8 33.1 ± 11.5ᵃ
OAR -control
Dmax(Gy) 74.2 ± 1.3 72.8 ± 1.1ᵃ 72.4 ± 2.6 71.6 ± 1.6
ᵃ Indicates a statistically significant difference (p<0.05) between MBPs and MPs
30
Figure 3.3: Dosimetric results of f-RA and h-RA MPs and MBPs per patient for PTV and
CL.
31
Figure 3.4: Dosimetric results of f-RA and h-RA MPs and MBPs per patient for TL-PTV,
ESO and SC.
32
3.3 Evaluation of prediction accuracy
The dose metrics predicted by the f-RA and h-RA models plotted against the achieved
MBP dose metrics for multiple organs-at-risk is shown in Figure 3.5, along with the linear fit
through all data points and the corresponding coefficient of determination R2 and the
standard error of the estimate σ. The difference between predicted and achieved dose
metrics ΔV5, ΔV20 and ΔDmean for each patient is shown in Table 3.4 and Table 3.5 for the f-
RA and the h-RA plans respectively. Furthermore, the achieved MBP metrics were visualized
in relation to the prediction ranges in Figure 3.6: the predicted Dmean, the upper boundary
Dmean and the lower boundary Dmean along with the achieved MBP Dmean for CL, TL-PTV,
and esophagus. The volume predictions for CL V5 and TL-PTV V20 with the corresponding
V5 and V20 of the upper and lower boundaries of the prediction range, and the achieved V5
and V20 are shown in Figure 3.7. The DVH plots along with the prediction ranges can be
found in the Appendix B.
3.3.1 Contralateral Lung
For f-RA plans, predicted and achieved CL Dmean shows good correlation with R2
value of 0.89 and σ of 0.70 Gy, while for the h-RA the correlation is slightly poorer with R2
of 0.84 and σ of 1.2 Gy. f-RA model was able to predict the mean dose with ΔDmean of less
than 1 Gy for all the cases (Table 3.4) and also achieved Dmean was between the predicted
range (Figure 3.6). The h-RA model was accurate in predicting the mean CL dose with
ΔDmean less than 1 Gy only for 6 of the patients (Table 3.5), while two of them were also out
of the predicted range (Figure 3.6). Although the predicted Dmean range of h-RA model
(2.4±1.2 Gy) is on average relatively narrow compared to that for the f-RA model (3.6±0.5
Gy), the predictions are less accurate.
When observing the V5 predictions, the f-RA model predicts generally lower V5 than
what is achieved by the MBPs and the standard error is 5.5 %. The predicted CL V5 is on
average 6.1% lower than the achieved (Table 3.4). On the other hand, h-RA model predicts
either higher or lower CL V5, yet with a higher standard error of 8%. The predicted V5 range
depicted in Figure 3.7 is on average 14.7±3.3% for the f-RA model and 11.9±2.1% for the h-
RA model. Nevertheless, 7/10 f-RA plans and 6/10 h-RA plans fall within the predicted
range.
33
Figure 3.5: The correlation between predicted and achieved DVH metrics of h-RA and f-RA
MBPs for multiple organs-at-risk. The solid lines represent linear regression fits (with R2 the
coefficient of determination), while the dashed lines represent one standard error of the
regression σ. The dashed black line represents the identity line (y=x).
34
Table 3.4: Difference between predicted and achieved MBP dose metrics for the f-RA model
Patient # Contralateral lung Total lung-PTV Esophagus
𝚫𝐕𝟓(%) 𝚫𝐃𝐦𝐞𝐚𝐧(𝐆𝐲) 𝚫𝐕𝟐𝟎(%) 𝚫𝐃𝐦𝐞𝐚𝐧(𝐆𝐲) 𝚫𝐃𝐦𝐞𝐚𝐧(𝐆𝐲)
1 -5.6 -0.1 8.2 2.8 0.3
2 -3.6 0.3 5.3 1.7 1.1
3 -8.2 0.5 1.8 0.4 0.9
4 1.2 0.7 1.5 1.4 -1.4
5 -11.2 -0.7 -2.7 -1.9 0.6
6 -15.4 -0.9 -0.2 -0.6 -1.1
7 -1.1 0.9 1.0 1.1 3.2
8 -14.5 -0.7 -0.6 -1.3 -1.4
9 -0.2 0.7 0.4 -0.4 4.1
10 -2.5 0.7 3.5 1.0 -1.0
Average -6.1±6.0 0.1±0.7 1.8±3.1 0.4±1.5 0.5±1.9
Negative values mean that the predicted value is lower than the achieved MBP value.
Table 3.5: Difference between predicted and achieved MBP dose metrics for the h-RA model
Patient # Contralateral lung Total lung-PTV Esophagus
𝚫𝐕𝟓(%) 𝚫𝐃𝐦𝐞𝐚𝐧(𝐆𝐲) 𝚫𝐕𝟐𝟎(%) 𝚫𝐃𝐦𝐞𝐚𝐧(𝐆𝐲) 𝚫𝐃𝐦𝐞𝐚𝐧(𝐆𝐲)
1 1.2 -0.46 2.9 0.9 -0.6
2 8.5 0.75 -0.8 -1.5 -2.5
3 -16.89 -2.07 0.6 0.1 -4
4 1.2 -0.8 5.6 2.4 -3.1
5 -3.7 -1.5 -1.2 -1.6 -0.5
6 -1.8 -0.26 -0.5 -0.8 2
7 8.4 1.16 -0.1 1.1 -7.1
8 -0.1 1.9 -3.6 -3.5 0
9 8 0.75 1.3 0.3 4.9
10 5.9 0.5 0.7 -0.6 -2.6
Average 1.1±7.7 0.0±1.2 0.5±2.5 -0.3±1.7 -1.4±3.3
Negative values mean that the predicted value is lower than the achieved MBP value.
35
Figure 3.6: Predicted and achieved Dmean for CL, TL-PTV, and ESO for f-RA and h-RA
techniques. Error bars represent the upper boundary Dmean and the lower boundary Dmean.
36
Figure 3.7: Predicted and achieved MBP CL V5, and TL-PTV V20 for both f-RA and h-RA
techniques. The error bars represent V5 and V20 of the upper boundary the lower boundary of
the prediction range.
3.3.2 Total Lung – PTV
The two models showed comparable quality of linear correlation between predicted
and achieved TL-PTV mean dose (Figure 3.5). The slope is higher than 1 for both methods,
while R2 and σ values are 0.80 and 1.4 Gy for the f-RA model respectively, and 0.85 and 1.5
37
Gy for the h-RA model. For the f-RA model, the prediction range was on average 3.1±0.4 Gy
and the achieved Dmean was within the predicted range in 7/10 cases. For the h-RA model,
the predicted range was slightly wider; 3.6±1.8 Gy on average, and it was representative for
8/10 patients.
Predicted and achieved V20 showed poor correlation for f-RA plans with R2 value of
0.77 and σ of 3.1 %. Although achieved MBP V20 was within the prediction range for 7/10
patients, achieved V20 was lower than the mid prediction most of the cases. The h-RA plans
showed stronger correlation and smaller standard error with R2 and σ values of 0.90 and
2.2% respectively, and achieved MBP V20 was within the prediction range for 7/10 patients.
The predicted V20 range was the same on average for both techniques; 5.0±1.2% for f-RA
and 5.0±0.9% for h-RA.
3.3.3 Esophagus
The linear regression analysis revealed a strong correlation between predicted and
achieved ESO Dmean , for both techniques, with R2 values of 0.97 and 0.93 for f-RA and h-
RA respectively, while the slope was close to 1. However, σ was 1.8Gy for the f-RA plans
and 3.2Gy for the h-RA. The average predicted Dmean range was 6.3±3.1 Gy for the f-RA
model and 5.0±1.7 for the h-RA model. Achieved esophagus Dmean was within the predicted
range in 9/10 f-RA plans, while only in 5/10 h-RA plans. Moreover, the h-RA model
predicted lower Dmean than the achieved for 7/10 patients.
38
3.4 Individualized analysis
PTV
PTV homogeneity and V95% of the MBPs were inferior compared to the MPs. In
particular, HI was increased in 8/10 of f-RA MBPs and in 6/10 h-RA MBPs. A more detailed
analysis revealed the reasons for that. The line objectives used for the CL and TL-PTV in the
MBPs, especially the objectives on doses higher than 66 Gy compete with the lower target
objective resulting in lower target coverage. An example of this can be seen in Figure 3.8:
when line objective is used for the lungs (a), the 95% isodose line covers less part of the PTV
at the side which is adjacent to the lung. When point objectives -generated by the model at
3Gy and 4Gy for CL and at 2Gy, 3Gy, 16Gy, 17Gy for TL-PTV -with the same priorities as
the line-objectives in the original MBPs- are used (b), PTV coverage is improved, while CL
V5 and TL-PTV V20 is not affected. The CL and TL-PTV line objective was found to affect
the PTV homogeneity also in h-RA plans, an example of which can be seen in Figure 3.9:
when using point objectives for the lungs the V95% was increased from 96.1% to 97.4%. The
effect of line objective for ESO was also examined and shown in Figure 3.9 for comparison.
If the line objective for ESO is not used, the PTV V95% is improved to 97.1%, however the
ESO V60Gy increases substantially.
On a patient-specific level, the lower PTV coverage is attributed to the sparing of the
esophagus (patients 3 and 8) and spinal cord (patient 3). After re-optimizing these plans using
lower priority for ESO, the PTV coverage considerably increased. Especially, for patient 3,
the spinal cord is adjacent to the PTV. In the MP, the SC+3mm dose exceeds the clinical
criterion of 54Gy while in the MBPs it is lower. In such case, the clinician can choose to
allow higher dose to the spinal cord for a more homogenous PTV dose, or the opposite.
39
Figure 3.8: f-RA MBPs of the same patient. (a) Plan optimized using line objective for CL
and TL-PTV. (b) Plan optimized using point objectives for CL and TL-PTV. Red line
represents the PTV outline. Green, pink and orange lines represent the 62,7Gy (95% of
prescribed dose), 20Gy and 5Gy isodose line respectively.
Figure 3.9: h-RA MBP DVHs of the same patient using: 1) Line objectives for CL, TL-PTV,
and ESO. 2) Point objectives for CL and TL-PTV, and line objective for ESO. 3) Line
objectives for CL and TL-PTV and only maximum objective for ESO.
Contralateral Lung
To understand the observed difference between MPs and MBPs, we have to also look
at the predictions, and consequently the model-generated objectives, in relation to the MPs.
In the f-RA MBPs of three patients (3, 5, 7) the CL V5 increased by more than 5%
compared to the MPs. This is attributed to the fact that, in the MPs the objectives for CL V5
40
are placed at very low volume in order to achieve the lowest possible V5. On the other hand,
in the MBPs, the objective line is placed automatically below the lower boundary of the
prediction range, and thus the objectives around 5Gy are placed always higher compared to
the manual objectives (Figure 3.10a). In particular, the manual objectives in the 10 evaluation
patients were set to dose 2-4 Gy and volume 7.9 – 23.5 % while the generated objective line
for the same dose was placed at volume 21.1% - 64.7%. To compensate for that, a priority of
200 was used for the objective-line in all f-RA MBPs, while the priority in MPs was 130.
Nevertheless, it was not possible to achieve the same V5 for the majority of the plans.
Regarding the f-RA MBP of patient 9, although V5 is clinically acceptable (33.7%),
there is a large deviation from the MP (20.6%). This happened because the model-generated
objective line was placed above the manual DVH-line making it impossible to achieve dose
as low as in the MP (Figure 3.10b).
Figure 3.10: CL DVHs of the f-RA MPs and MBPs along with the respective objectives.
(a)Patient 5: manual objectives are placed considerably lower than the model-generated
objective line. (b) Patient 9: the model-generated line objective is placed higher than the CL
DVH-line of the MP.
An alternative approach (MBP-alt) was investigated for the cases that the f-RA MBP
had increased CL V5. Instead of line-objective, point-objectives were generated per 1 Gy,
from 2Gy to 7Gy for the CL, and between 2-7 Gy and 16-22Gy for the TL-PTV as seen in
41
Figure 3.11a. Furthermore, the priority of CL and TL-PTV was increased to 300 and 150
respectively. The results for patients 3, 5 and 7 are shown in Table 3.6 along with the results
of the MPs and the original MPBs. The use of point objectives with higher priority for the
lungs improved the CL V5 and TL-PTV V5 compared to the original MBPs, while for patient
5 CL V5 and TL-PTV V5 were even lower than the MP. However, TL-PTV V20 was not
improved because the TL-PTV predictions were too high as explained below.
Figure 3.11: (a) f-RA generated point objectives for CL in the dose region 2-7 Gy and for
TL-PTV in dose 2-7Gy and 16-22Gy. Generated line objective for ESO. (b) f-RA plans of
patient 5.
42
Table 3.6: Results of f-RA plans using the alternative method for patient 3,5 and 7, and
comparison with MPs and MBPs. Patient 3 Patient 5 Patient 7
MP MBP MBP-alt MP MBP MBP-alt MP MBP MBP-alt
PTV
V95%(%) 97.4 92.8 96.3 97.5 96.3 97.1 97.1 95.5 95.6
CL
V5(%) 44.8 60.6 50.2 39.9 47.9 31.8 31.9 37.4 32.5
Dmean(Gy) 7.6 8.1 8.6 6.5 6.1 6.1 6.6 6.4 7.2
TL – PTV
V5(%) 59.8 71.3 63.8 63.0 66.7 58.3 49.8 54.0 51.4
V20(%) 28.4 29.4 31.1 29.5 30.8 30.2 24.2 27.6 28.4
Dmean(Gy) 17.1 17.7 18.0 17.4 17.7 17.5 13.7 14.8 15.1
When observing the h-RA predictions for CL, the DVH that deviates most from the
predicted DVH-range is that of patient 3 (Appendix B). That plan has an unusual field set up
of the conventional fields. The oblique field is set to 140 degrees, while the field direction in
the model library is between 150 and 160 degrees for left-sided tumors (Table 2.5).
Therefore, a larger part of the lung is located in the beam direction, resulting in an unusually
high exiting dose for the CL that the model is not able to predict. This part of the lung is
already in the in-field region of the RapidArc component, thus the in-field region is the same
whether the conventional fields are used or not. Furthermore, the model takes into account
the final field set-up including the RapidArc component and only takes into account the
direction of the fields, not how many beams are there. Consequently, the conventional fields
do not play any role in the estimation algorithm. After excluding this plan, the correlation
coefficient between predicted and achieved Dmean increases to 0.90 and σ drops to 0.9 and
are now comparable to the figures for the f-RA model. The same happens for the V5
predictions; R2increaces to 0.90 and σ is 5.0%, while also the slope decreases to 1.05.
The MBP CL mean dose of the h-RA plan of patient 8 deviated from the predicted by
1.9Gy. For the RapidArc component of this plan, a full-arc with avoidance sector was used
containing a 250° irradiation arc. That caused the GED PC1 to be higher as explained before.
Full-arc was used also for patient 6. However, an additional oblique field was used on 160°,
even though the tumor is located in the right lung, causing direct irradiation to the CL. The
extra dose delivered by this field, compensated for the higher GED PC1.
43
TL-PTV
The dose of TL-PTV in the f-RA MBPs was generally increased in comparison to the
MPs. When looking at the DVH predictions (Appendix B) the objective line is placed higher
than the MP DVH-line in 7/10 cases (patients 1,2,3,4,7,9,10) in the low dose region, showing
that the model underestimated the amount of sparing that could be achieved.
A possible cause for the high predictions, despite that model statistics indicate that the
model training has been successful, is the presence of an outlier in the model that it may
overfit the data. There is a plan with high CD value (CD=26.8, while the threshold is 10), that
is away from the regression line but close to identity line in the residual plot. To further
investigate that, the cleaned model, from which the suggested outliers were removed (Section
3.1.2), was used to generate predictions for the TL-PTV. The predictions were again
consistently higher than the MPs. More specifically, in 8/10 patients the objective line was
placed above the manual TL-PTV DVH-line.
The largest deviation between the predicted and achieved TL-PTV DVH was
observed for patients 1 and 2. The TL-PTV structure of patient 2 was indicated as an outlier
by the f-RA model because of large volume (Table 2.1 and Table 2.6). The TL-PTV structure
of patient 1, although not indicated as an outlier by the DVH estimation algorithm, has
volume higher than mean+1sd of the model library structures. Consequently, it is possible
that the model dose not perform well for large structure volumes.
It should be noted that RapidPlan was able to pull the DVH-line much lower than the
optimization objective line for patient 1, 2 and 10 as can be seen in Appendix B. This shows
that the TL-PTV sparing during the optimization process was mainly driven by the CL lung
objective, as the CL volume accounts for around 60% of the TL-PTV volume. To confirm
that, the plan of patient 1 was optimized again, without the use of dose-volume objectives for
the TL-PTV. The resultant plan had only minor differences in mean doses: CL Dmean was
0.09 Gy lower, TL-PTV Dmean was 0.15Gy higher and IL Dmean increased by 0.55Gy, while
TL-PTV V5 and V20 remained the same.
3.5 Using predictions to select treatment technique
Achieved MBP f-RA dose parameters plotted against the achieved h-RA dose
parameter for CL, TL-PTV and ESO are shown in Figure 3.12. The cases where the
44
predicted f-RA parameter was lower than the predicted h-RA parameter (for ex. f-RA
Predicted CL Dmean < h-RA Predicted CL Dmean ) are marked in red.
Data points on the left side of the identity line show that the f-RA MBP gave lower
dose than the h-RA MBP, while data points highlighted with red indicate that the f-RA
method would result in lower dose based on the predictions. There are 8/10 correct
predictions for CL Dmean, and 7/10 for TL-PTV Dmean, 9/10 for TL-PTV V20, and 8/10 for
ESO Dmean.
Based on the MBPs results, the h-RA technique is almost always better for CL Dmean
and TL-PTV V20, and f-RA method is better for TL-PTV Dmean and ESO Dmean. However,
considering that the f-RA TL-PTV V20 predictions are consistenlty high as described above,
we need to take a closer look at the TL-PTV V20 resaults. When examining the results of the
MPs in Figure 3.13, f-RA MPs resaulted in lower TL-PTV V20 in 5/10 patients. In that case,
RapidPlan gave correct prediction for 7/10 cases.
45
Figure 3.12: MBP f-RA dose parameters plotted against the achieved h-RA dose parameter
for CL, TL-PTV and ESO. Marked in red are the cases where the predicted f-RA parameter
was lower than the predicted h-RA parameter. Solid line represents the identity line.
46
Figure 3.13: Manual f-RA TL-PTV V20 plotted against the manual h-RA TL-PTV V20.
Marked in red are the cases where the predicted f-RA TL-PTV V20 was lower than the
predicted h-RA TL-PTV V20.
47
4. DISCUSSION AND CONCLUSION
The purpose of this thesis was to evaluate the performance of the RapidPlan
knowledge-based planning solution in providing f-RA and h-RA plans for large volume lung
cancer patients. Additionally, it was examined whether RapidPlan predictions can serve as a
tool for selection of the optimal treatment technique for a new patient.
The results showed that RapidPlan can generate MBPs of comparable quality to the
MPs made by experienced treatment planners. The use of line-objectives throughout the
whole dose range improved CL and TL-PTV Dmean, while in some cases MBPs also reduced
the CL V5 and TL-PTV V20 for specific patients. The most notable difference between MBPs
and MPs for both techniques was the decreased PTV V95%. The use of line-objective for CL
and TL-PTV negatively affected the PTV coverage. On top of that, the use of somewhat
higher priorities for ESO and SC also prevented the dose to be equally distributed in the PTV,
reducing its homogeneity. Another possible reason for the lower homogeneity it might be the
use of NTO in the MBPs, which on the other hand led to increased conformity.
Another remarkable result is the increased CL V5 in the f-RA MBPs, caused by the
fact that manual optimization objectives are placed at very low dose in order to pull down the
DVH as much as possible. However, with a quite accurate model, the predictions will never
be low enough to generate objectives in a similar position to the manual ones. Since
increasing the priority of the whole objective-line would affect the PTV coverage, the only
solution seems to be the use of generated point-objectives with increased priorities, at the low
dose region. This could also solve the issue with low PTV V95%. This approach was tested in
three patients and showed a reduction in CL V5. RapidPlan does have a feature to
automatically generate the priorities. Ideally, the suitable priorities that ensure sufficient
OAR sparing while maintaining good homogeneity of the PTV should be generated by
RapidPlan itself. However, generated priorities still do not seem to work in this way.
Fogliata et al.26 have shown that RapidPlan VMAT plans improved IL Dmean, CL
Dmean, and CL V20. The last is contrary to the findings of the present study. However,
Fogliata et al. have used line-objective on IL instead of TL-PTV, while no objectives were
used around the CL V5 in the manual plans. On the other hand, at the VUmc reducing the CL
V5 as low as possible is of high clinical importance, and manual plans have very low V5.
Therefore is difficult to further reduce V5 in the MBPs. Furthermore, in Fogliata et al. paper,
48
RapidPlan improved PTV homogeneity. However, their study involved smaller PTV
volumes.
A limitation of this study was the problem in the GED calculation algorithm which is
incorrectly modeling the avoidance sector, and this could be a contributing reason to
inaccuracies and poor results. Because of this, the optimization of f-RA plans had to be done
twice, consuming more time as explained in the Appendix A. However, the problem is not
completely solved as it also exists in the model training algorithm. The avoidance sector
plans are considered as having beamlets in the avoidance sector directions, and therefore
some radiation is expected from these directions, while the partial-arc plans are correctly
modeled. This leads to inconsistencies in a model containing both avoidance sector plans and
partial-arc plans. The problem was communicated to a Varian representative who stated that
the bug will be corrected in the next version of RapidPlan.
RapidPlan was able to accurately predict the mean dose of CL, but it consistently
underestimated the amount of sparing that could be achieved for TL-PTV. After excluding
the possibility that this is caused by any outliers in the model, the reasoning may be found in
the modeling of TL-PTV structure. TL-PTV structure has a large variation in dose
distribution because CL is spared as much as possible whereas IL receives much more dose.
RapidPlan solves the problem of varying dose within a structure by using volume partitioning
and constructing distinct models for the parts of the organ that is out-of-field or overlapping
the target. However, in case there is large dose variation within one model structure, it is
possible that this is more difficult to present with the PC’s. This hypothesis is further
supported by the observation that predictions for the IL, although not used for the
optimization, are of good quality (Figure B5 in Appendix B). If this is the cause of the
problem, a possible solution could be to utilize the predictions of the individual lungs to be
sync to the total lung predictions. However, there is no such mechanism in RapidPlan.
Furthermore, it is possible that the problem with not modeling correctly the avoidance
sector causes the inaccuracy in TL-PTV predictions. The GED-PCS1 seems to have a more
important role in the TL-PTV predictions since its partial coefficient of determination is
0.837 for the TL-PTV, while for the CL and IL is considerably lower: 0.737 and 0.663
respectively.
The h-RA MBPs, apart from the decreased PTV V95%, were of very good quality, and
resembled well the MPs since 90% of the dose is delivered by the conventional fields, which
were the same as in the MPs. RapidPlan is not designed for hybrid techniques and thus does
not take into account the conventional component of the field. However, because the model
49
consisted mostly of fields having a similar arrangement, the model incorporated correctly the
dosimetry of the training set, and thus generated quite accurate predictions for CL and TL-
PTV Dmean. On the contrary, the model was not able to predict correct dose in plans with
different field set-up than the standard. Nevertheless, the h-RA predictions for ESO were
lower than the achieved MBPs in 7/10 patients. That is because the esophagus is a small
structure, compared to the PTV, and the MLC movement and dose modulation in VMAT, can
sufficiently reduce its dose.
RapidPlan can better predict CL and TL-PTV mean doses, instead of dose-specific
volumes such as V5 and V20. When it comes to comparison between f-RA and h-RA, based
on the simple method used in this study, by only comparing single OAR dose volumes,
RapidPlan can accurately predict which technique gives the lower dose in 7-9 /10 cases.
To conclude, this study demonstrated that RapidPlan is capable of generating
clinically acceptable f-RA and h-RA plans for lung cancer patients. However, the plans could
be improved after a more wise selection of priorities and using generated point-objectives
instead line objectives. Furthermore, the bug in the algorithm which is not incorporating the
avoidance sector should be corrected for more accurate results. This study could serve a
starting point for further validation of the use of RapidPlan for large volume lung cancer
patients with the aim to be implemented in the clinical practice.
50
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APPENDIX A
Geometry Expected Dose calculation algorithm not incorporating the avoidance sector.
To investigate the reason of full-arc plans in the h-RA model have higher GED-PC1, the
GED-PC1 for CL was ploted against the following geometrical features: overlap with target,
in-field volume, target volume and mean dose (Figure A1). Full-arc with avoidance sector
plans (red squares) deviated from the rest of the plans, despite not having different
geometrical features.
Figure A1: Overlap with target, in-field volume, target volume and mean dose of CL plotted
against GED-PC1 for h-RA model. Red squares include the full-arc with avoidance sector
plans.
To further examine the handling of avoidance sectors by the model, for two patients
three treatment plans were created and interactively optimized. The first treatment plan
incorporated a full-arc and avoidance sector (a), the second a partial-arc covering the same
irradiative angles of the first plan (b) and the third using full irradiating arc without any
avoidance sector. (c).These plans were subsequently added to the f-RA model, and the model
was re-trained to visualize where these plans lie in the generated regression plot for the CL
(Figure A2). The full-arc without avoidance sector has higher GED-PCS compared to the
other two plans, as expected. One would expect that when the irradiating field directions are
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the same for both plans, the GED and consequently the GED-PCS1 would be the same for the
two plans. However, after model training, the avoidance sector plan had a considerably
higher GED-PCS1, suggesting that the GED is not accurately calculated. The RapidPlan
provided geometric plots (Figure A2 (B) and (C)) show that the geometrical features (CL
volume, overlap with target, out-of-field volume and target volume) are the same for the three
plans. The only other factor that could cause the GED to be different is the field set-up.
Figure A2: (A) Regression plot of CL of the f-RA model. (B) Geomteric plots for patient 1,
for the (a) partial-arc plan, (b) full-arc with avoidance sector plan (c) full-arc plan. (C)
Geometric plots for patient 2 for the corresponding plans.
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This bug in the algorithm, had an effect on the generation of predictions. The DVH
estimation algorithm does not take into account the avoidance sector in the optimization
window, and thus the predictions for the CL and TL-PTV are high (1st prediction in Figure
A3). After optimizing the plan incorporating the avoidance sector, and then using the
optimized plan to generate new predictions, the predictions appear to be lower (2nd prediction
in Figure A3). The 1st prediction corresponds to the case c/full-arc without avoidance sector
in Figure A2, while the 2nd prediction corresponds to the case b/full-arc with avoidance
sector. If we use partial-arcs, generate the predictions and optimize the plan, the predictions
seem to be far lower than the achieved DVH.All the f-RA MBPs in the study were optimized
two times to incorporate the avoidance sector. However, since when using partial-arc the
predictions are even lower, the problem is not completely solved.
The problem was communicated to a Varian representative who gave the explanation
to our observations. The GED calculation algorithm does not recognize the avoidance sectors
and ‘discretize’ the arc fields. If the difference in gantry angle is less than 5 degree between
two control points -beam directions-, the control points are used for the discretization, but if
the difference is more than 5 degree, additional control points are interpolated between the
two control points. The new control points are equally spaced and the amount is deduced so
that the new spacing is less or equal than 5 degree. When we start with new fields to generate
predictions for a plan (1st prediction), the control points are spaced in 5 degree along the full
arc, so the predictions are generated as if there is no avoidance sector. When optimizing the
plan, we manually set the control points in around 2 degree interval in the irradiation-arc and
no control points in the avoidance sector. When using the optimized plan -whose fields have
already control points- to generate predictions (2nd prediction), any avoidance sector is
considered as a long jump between two control points and additional control points are
generated with 5 degree spacing. Therefore, the 2nd prediction is generated as if in the
avoidance sector the ‘density’ of control points has been dropped from one control point in
every 2 degree into one control point in every 5 deg. This is actually affecting the GED since
it assumes less dose from those directions. If we use partial arcs, there are no control
points/beamlets at all in the avoidance sector directions. The correct functionality would be
that the partial-arc fields and the full-arc fields with avoidance sector should give the same
results.
However, the problem is not encountered only in the predictions, it is also in the
models since the same GED calculation algorithm is used for the model training. Using
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partial-arc instead of avoidance sector in the MBPs does not solve the problem because our
models is mainly consisted of avoidance sector plans.
Figure A3: DVH predictions for a new plan (1st prediction) and for an already optimized
plan (2nd prediction) of the same patient, with full-arc with avoidance sector, for CL and TL-
PTV.
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APPENDIX B
Supplementary material
Figure B1: Predicted DVH-ranges (shaded areas), achieved MBP (black) and manual plan
DVH-lines (red) of Contralateral Lung with the f-RA method for the 10 test patients.
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Figure B2: Predicted DVH-ranges (shaded areas), achieved MBP (black) and manual plan
DVH-lines (red) of TL-PTV with the f-RA method for the 10 test patients.
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Figure B3: Predicted DVH-ranges (shaded areas), mid-prediction DVH-lines (dashed lines),
and achieved MBPs (solid line) of CL with the h-RA method for the 10 test patients.
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Figure B4: Predicted DVH-ranges (shaded areas), mid-prediction DVH-lines (dashed lines),
and achieved MBPs (solid line) of TL-PTV with the h-RA method for the 10 test patients.
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Figure B5: Achieved MBP Dmean plotted against predicted Dmean of IL, for f-RA and h-RA
MBPs.