18f‐fdg‐pet/ct for very early response evaluation predicts ct...

1

18F‐FDG‐PET/CT for very early response evaluation predicts CT response in erlotinib treated NSCLC patients – A comparison of assessment methods

Authors:

Joan Fledelius1, Anne Winther‐Larsen2, Azza A. Khalil 3, Catharina M. Bylov4, Karin Hjorthaug 5, Aksel Bertelsen1, Jørgen Frøkiær5 and Peter Meldgaard3

1 Department of Nuclear Medicine, Herning Regional Hospital, Herning, Denmark 2 Department of Clinical Biochemistry, Aarhus University Hospital, Aarhus, Denmark.

3 Department of Oncology, Aarhus University Hospital, Aarhus, Denmark. 4 Department of Radiology, Aarhus University Hospital, Aarhus, Denmark.

5 Department of Nuclear Medicine and PET Centre, Aarhus University Hospital, Aarhus, Denmark.

No disclaimers No financial support Corresponding author: Joan Fledelius, MD Department of Nuclear Medicine, Herning Regional Hospital, DK‐7400 Herning, Denmark. E‐mail: [email protected] Fax: +45 78437361 Telephone: +45 31603316

Word count: 4998

Running title: Early FDG‐PET predicts CT response

Journal of Nuclear Medicine, published on May 10, 2017 as doi:10.2967/jnumed.117.193003by on April 5, 2020. For personal use only. jnm.snmjournals.org Downloaded from

http://jnm.snmjournals.org/

2

ABSTRACT

The purpose of this study was to determine which method for early response

evaluation with 18F‐fluoro‐D‐glucose positron emission tomography combined

with whole body computed tomography (18F‐FDG‐PET/CT) performed most

optimal for prediction of response on a later CT‐scan in erlotinib treated non‐

small cell lung cancer (NSCLC) patients.

Methods: 18F‐FDG‐PET/CT scans were performed prior to and after 7‐10 days

of erlotinib treatment in 50 NSCLC patients. The scans were evaluated using a

qualitative approach and various semi‐quantitative methods including

percentage change in Standardized Uptake values, lean body mass corrected

(SUL) SULpeak, SULmax and Total Lesion Glycolysis (TLG).

The PET parameters and their corresponding response categories were

compared to the percentage change in the sum of the longest diameter (SLD)

in target lesions and the resulting response categories from a CT scan

performed after 9‐11 weeks of erlotinib treatment using Receiver Operating

Characteristics (ROC) analysis, linear regression and quadratic weighted kappa.

Results: TLG delineation according to the PET evaluation response criteria in

solid tumors (PERCIST) showed the strongest correlation to SLD (R = 0.564,

p<0.001), compared to SULmax (R=0.298, p=0.039) and SULpeak (R=0.402,

p=0.005). For predicting progression on CT, ROC analysis show area under the

curves (AUCs) between 0.79 and 0.92 with the highest AUC of 0.92 (95%

by on April 5, 2020. For personal use only. jnm.snmjournals.org Downloaded from


3

confidence interval (CI): 0.84‐1.00) found for TLG (PERCIST). Furthermore,

using a cut‐off of 25% change in TLG (PERCIST) for both partial metabolic

response (PMR) and progressive metabolic disease (PMD), which is the best

predictor of the CT response categories showed a kappa value of 0.53 (95%CI:

0.31‐0.75). This method identifies 41% of the later PDs on CT, with no false

positives. Visual evaluation correctly identified 50% with a kappa value of 0.47

(95%CI: 0.24‐0.70). Conclusion TLG (PERCIST) was the optimal predictor of

response on later CT scans outperforming both SULpeak and SULmax. Using

TLG (PERCIST) with a 25% cut‐off after 1‐2 weeks of treatment allows us to

safely identify 41% of the patients who will not benefit from erlotinib and stop

the treatment at this time.

Key words: FDG‐PET/CT, Lung cancer, PERCIST 1.0, Early response evaluation,

TLG.



4

INTRODUCTION

Treatment with tyrosine kinase inhibitors (TKIs) has proven to be effective in

NSCLC patients with response rates of 10‐20% in unselected populations.

Subgroups of patients have been identified with good and sometimes

prolonged results (1‐4).

At present, selection of patients is done by detecting mutations in the

epidermal growth factor receptor (EGFR) genes. EGFR mutation positive

patients (EGFR‐mut) have higher response rates than EGFR wild type (EGFR‐

wt) patients (5‐9). However, a subgroup of EGFR‐wt patients (1,5,10) also

benefit from TKI treatment, suggesting that selecting patients depending

solely on mutation‐status will exclude some patients from a potential

treatment benefit.

At our institution, erlotinib treatment is offered to non‐operable EGFR‐

mut, NSCLC patients as first‐line treatment and as second‐ or third‐line

treatment to EGFR‐wt patients. An important challenge with this treatment is

how to evaluate the response, since TKIs are known to have mostly cytostatic

effects (11,12) as opposed to cytotoxic effect. Therefore, a follow‐up CT scan is

routinely performed 9‐11 weeks into the treatment for response evaluation.

Considering that stage III – IV NSCLC patients in general have a short remaining

life expectancy (4,13,14), it is essential to discontinue a futile treatment course

as early as possible.



5

Preclinical studies have shown that TKI sensitive cells downregulate

their glucose uptake very early after exposure to TKI treatment (1,11).

Clinically 18F‐FDG‐PET/CT performed very early during TKI treatment has

shown promise for predicting both anatomical response and survival

(1,5,15,16), and to predict the histopathologic response (7,17).

The many parameters used for response evaluation with 18F‐FDG‐PET

include visual evaluation, change in standardized uptake value corrected for

body weight (SUV) both for maximum intensity voxels and the mean value in a

standardized volume of 1.2 cm in diameter VOI (SUVmax and SUVpeak), as

well as more complex volume‐based parameters such as TLG with various

ways of delineating the lesions.

PERCIST 1.0 from 2009 (18) uses the SULpeak as the standard, but also

suggest TLG as a supplementary analysis. We have previously shown that

percentage change in TLG is a promising predictor of CT response in a

subgroup of EGFR‐wt patients (10).

The aim of this study is therefore to identify which specific method of a

selection of commonly used methods (including the PERCIST 1.0 methods) is

the optimal for predicting the CT response, early during erlotinib treatment.

We hypothesize that an evaluation of the total disease burden will improve

response evaluation compared to single “hottest” lesion evaluation. We focus

on safely selecting patients after 7‐10 days of treatment, who will not have a



6

treatment effect, enabling us to discontinue futile treatment at this early time‐

point.

MATERIALS AND METHODS

The results are reported as according to “Standards for Reporting

Diagnostic Accuracy studies 2015”.

Patients

This retrospective study evaluated 18F‐FDG‐PET/CT scans, in

compliance with the PERCIST 1.0 criteria to ensure comparability of images,

from 50 patients enrolled in prospective single‐center study originally

including 67 consecutive patients with stage III‐IV NSCLC between April 2013

and August 2015. The study was approved by the Central Denmark Region

Committee on Biomedical Research Ethics (no. 1‐10‐72‐19‐12) and subjects

signed an informed consent form. The study was reported to ClinicalTrials.gov

(NCT02043002). All patients were candidates for palliative erlotinib treatment.

Detailed inclusion criteria were previously published (19). The selection of

patients included in the present study is presented in Figure 1.

All patients had an 18F‐FDG‐PET/CT scan performed before (baseline)

and 7‐10 days after initiation of erlotinib treatment (follow‐up). CT scans of

the chest and abdomen were acquired before and after 9‐11 weeks of



7

treatment, or earlier on clinical indication. Testing for epidermal growth factor

receptor (EGFR) mutations had been performed in all patients with

adenocarcinoma as part of the routine diagnostic work‐up by use of the

Therascreen EGFR RGQ PCR kit (QIAGEN, Manchester, UK) according to the

manufacturer’s protocol, and based on this, patients were categorized as

either EGFR‐wt or EGFR‐mut.

18F‐FDG‐PET/CT acquisition and evaluation

All 18F‐FDG‐PET/CT scans were performed on a combined PET/CT

scanner (Siemens Biograph TruePoint 40, Siemens Healthcare GMbH,

Erlangen, Germany) at the Department of Nuclear Medicine and PET‐Centre,

Aarhus University Hospital, Denmark, using the same scanner type,

acquisition‐ and reconstruction protocol as previously published in detail (19).

In brief, all patients had a fasting period of at least 6 hours, a blood glucose

concentration of < 11 mM and an uptake time of 60 +/‐ 10 minutes between

injection of 5 MBq pr. kg ±10% of FDG and scan start (3 minutes per bed

position). A whole‐body low‐dose CT scan (50 mAS, 120kVp) was performed.



8

All 18F‐FDG‐PET/CT scans were evaluated by one experienced Nuclear

Medicine Physician using Siemens Syngovia software (Siemens Healthcare

GMbH, Erlangen, Germany). The evaluator was blinded to the outcome and

the result of the following CT scan. Evaluation of response was performed as

1) Visual evaluation described by Mac Manus et al (20) considering both the

overall change in FDG‐uptake and appearance of new FDG‐avid lesions (visual),

2) The percentage change in SULpeak in the lesion with the highest uptake at

baseline and follow‐up (not necessarily the same lesion) (SULpeak) according

to PERCIST 1.0 (18), and 3) The percentage change in global TLG with various

delineation methods: At SULmean + 2 standard deviations (SD) in a spherical 3

cm ROI in the right lobe of the liver (SULmean(liver)) (TLG (PERCIST)) and at

30% (TLG (30)), 40% (TLG (40)) and 50% of SULmax (TLG (50)). A lesion was

considered evaluable for all semi‐quantitative methods if SULpeak was 1.5 x

SULmean(liver) + 2SD. Delineation was performed semi‐automatically after a

manual rough outlining of each lesion, resulting in an SULmean and a

metabolic tumor volume for the delineated area. TLG for each lesion was

calculated as SULmean x metabolic tumor volume. Global TLG was the sum of

all measurable lesions TLGs.



9

All the methods allocate patients into 4 different response categories:

1) PMD, 2) stable metabolic disease (SMD), 3) PMR and 4) complete metabolic

response. When using the classification methods, for SULpeak, all patients

were categorized as PMD, if new lesions had appeared, independent of

SULpeak. This was not the case for the TLG categories, since any new lesion

was included in the TLG calculations, if measurable. Multiple cut‐offs for these

response categories were tested. An overview of the methods is presented in

Supplementary Table 1. SULmax was measured for comparison, but not used

for classification into response categories.

CT evaluation

The radiological response was evaluated by one experienced

radiologist on the first CT scan performed after initiation of erlotinib according

to Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 criteria.

Response evaluation included reporting of percentage change in SLD and the

resulting response categories: 1) progressive disease (PD), 2) stable disease

(SD), 3) partial response (PR) and 4) complete response. We chose to

dichotomize into PD vs non‐PD (SD+PR) when appropriate, since this is used as

the basis criteria to decide whether to continue or discontinue erlotinib

treatment.

Statistical analysis



10

Comparison of SULpeak, SULmax and the TLGs to the SLD was

performed using linear regression analysis, a significance level of 0.008

(Bonferroni correction for 6 methods) was used.

The predictive accuracy of SULpeak, SUL max and the TLGs was

evaluated by ROC analyses, predicting PD versus non‐PD on CT. The optimal

cut‐off, considering sensitivity and specificity equally important, was identified

by visually locating the data point nearest the top left corner on the ROC

curve.

To compare the 18F‐FDG‐PET response categories to the CT response

categories quadratic weighted kappa analyses were performed (21). As many

patients as practically possible was included, a power calculation was not

performed.

Statistical analyses were performed with SPSS statistics version 23.0 for

Macintosh (IBM SPSS Statistics, Chicago, Il).

RESULTS

Scan times and standardization parameters

The time between scans and treatment start is presented in Table 1.

The indication for an early CT scan was in all cases suspected clinical

progression. 10 showed PD and 4 SD, the four scanned earlier than 4 weeks all

showed PD on CT and PMD on 18F‐FDG‐PET/CT (TLG(PERCIST(25%)). Data on



11

injected FDG‐activity, glucose level and uptake time on population basis is

presented in Table 2. A full description of glucose, uptake time and injected

FDG activity is available in Supplementary Table 2.

All patients were analyzable by visual evaluation and SULpeak (and

SULmax), whereas the TLG measurements were not feasible to perform in 3‐7

patients depending on the delineation method. When using the PERCIST

delineation method three patients were not analyzable; in one patient owing

to an uptake at follow‐up close to the background level, another patient had

myriads of very small FDG avid lesions, and the last patient showed intense,

diffuse uptake in the lung tissue impairing delineation of the tumor. TLG (50,

40 and 30) showed in total a further four non‐evaluable patients owing to

relatively low uptake or proximity to the liver (higher background level). For

two patients SLD was not available (non‐measurable lesions). None of the

patients were classified as complete responders on either CT or 18F‐FDG‐

PET/CT.

Comparing of SULpeak, SULmax, and TLGs to SLD

We found that all the measured parameters except SULmax were

significantly correlated to SLD, but TLG (PERCIST) showed the strongest

correlation with R = 0.564 (p < 0.001). Plots for SULpeak, TLG (PERCIST) and



12

SULmax are presented in Figure 2, all variables are available in Supplementary

Figure 1.

Dichotomizing the CT response into progression (PD) and non‐PD

resulted in 28 PD and 22 non‐PD patients. For the PET‐methods involving a

continuous variable, the ROC curves for the 43 patients analyzed by all

methods are presented in Figure 3. As can be seen, TLG (PERCIST) has the

highest AUC 0.923 (95% CI:0.840‐1.00) (Table 3), although the confidence

intervals overlap between methods. TLG (30) had the lowest AUC of 0.790

(95%CI: 0.698‐0.949).

The highest sensitivity and specificity for predicting PD on CT when

considering the sensitivity and the specificity equally important (sensitivity of

0.89 and specificity of 0.88) was seen for TLG (PERCIST). For specificity = 1.00,

the sensitivity for TLG (PERCIST) was 0.50, while SULpeak had a slightly higher

sensitivity of 0.58. Again, TLG (30) showed the lowest values. Interestingly,

SULpeak identified more anatomical responders than TLG (PERCIST), but fewer

anatomical progressions, as shown in Figure 4. Plots for SULmax and TLG (30‐

50) are found in Supplementary Figure 2.

Metabolic response categories compared to RECIST

TLG (PERCIST (25%)) resulted in correct classification in 25 of 47

patients (53%) according to the CT response. One patient was classified more



13

than one level different (early PMR and later PD on CT). Noticeably, all of the

PMDs identified were classified as PD on later CT scans, identifying 11 of 27

PDs (41%). Even though SULpeak provided the highest sensitivity at specificity

= 1 in the ROC analysis and all patients were analyzable by this method, it only

identified 23 of 50 patients correctly (46%) when the response categories for

the optimal cut‐off (20%) was used. The visual method identified 25 of 50

patients correctly (50%) and both SULpeak and visual found one (the same

patient) more than one level different, possibly owing to bone‐flare. The

results for TLG (PERCIST (25%)), SULpeak (20%) and visual evaluation are

presented in Table 4.

In general, the kappa values were rather low ranging from 0.23 to 0.53,

presented in Table 5. The method with the highest value 0.53 (95%CI: 0.31‐

0.75) was TLG (PERCIST(25%)).

Dichotomizing the 18F‐FDG‐PET classification into PMD and non‐PMD

(PMR+SMD) for comparison to the similar dichotomization for CT (PD versus

non‐PD) resulted for TLG (PERCIST (25%)) in a sensitivity of 0.41, a specificity of

1.00, a positive predictive value of 1.00 and a negative predictive value of 0.56.

For the SULpeak (20%), a slightly lower sensitivity of 0.29, a specificity of 1.00,

a positive predictive value of 1.00 and an negative predictive value of 0.52 was

found. The categorization tables, sensitivity, specificity and predictive values

for all methods are presented in Supplementary Table 3.



14

DISCUSSION

This study was performed to identify which PET assessment method

was the most optimal for predicting the later CT response early during

erlotinib treatment in unselected NSCLC patients. Early assessment of

progression would enable discontinuation of non‐beneficial treatment after a

few days of treatment (7‐10 days).

The main result of this study was that TLG (PERCIST) had the strongest

correlation to SLD, statistically significant even when applying a significance

level of 0.008. It was the best predictor of PD versus non‐PD on CT, though the

difference between methods were not statistically significant. A 25% change

was optimal cut‐off and avoided false PMDs. It performed better than the

PERCIST suggested 75% increase and 45% decrease, possibly because of the

early time point for evaluation.

There are many assessment methods for evaluating the treatment

response with 18F‐FDG‐PET/CT. The most commonly used parameter is

SUVmax, but SULpeak is also frequently used, especially since this was

recommended as the standard method in the PERCIST 1.0 from 2009(18), and

is also recommended for response evaluation in the latest European

guidelines, together with the mentioning of the increasing interest of TLG(22).



15

However, few studies have compared the performance of these methods

simultaneously.

Previously, a comparison of six different parameters including

SUVpeak, SUVmax and SUVmeans using different delineation methods was

performed for residual activity after 1 week of erlotinib treatment (23). In this

study other SUVs than SUVmax were not superior at predicting progression on

CT but they did not consider the percentage change, moreover, they did not

include TLG.

In another study including 34 erlotinib treated patients it was

demonstrated that percentage change in SUVpeak after 1 week of treatment

was predictive of PD vs non‐PD on CT after 6 weeks of treatment (5).

However, there was no comparison to changes in TLG or other parameters.

Moon et al compared SUVmax, SUVpeak and TLG in 52 stage IV NSCLC

patients before and after 4 cycles of platinum‐based chemotherapy (24),

consistent with the results of the present study, they found that change in TLG

was a predictor of PFS while change in SUVpeak and SUVmax were not, thus

supporting our finding, that TLG outperforms SULpeak and SULmax.

We found that a 25% change in TLG separated the response categories

better than the 45/75% cut‐off from PERCIST 1.0. This is consistent with

previous findings by Kahraman demonstrating that a cut‐off between 20 and

30% change is superior for predicting PFS (25). Our results suggest that the



16

same is true for predicting PD/non‐PD on CT. When using 25% change in TLG

(PERCIST), we still identified a large group of SMD patients who later showed

PD on CT. In order to reduce the size of this category, we could use the ROC

determined cut‐off value of 9.2 % increase (or 10%), which reduced this group

from 15 to 12. However, since the cut‐off values are determined from the data

one must expect the estimated sensitivity and specificity to be too optimistic,

and the day‐to‐day variation (26‐28) should also be considered. Taking this

into account, we prefer to use the 25% cut‐off.

The visual evaluation method performed well and was comparable to

the more sensitive of the semi‐quantitative methods, SULpeak and TLG

(PERCIST). We have previously demonstrated in a chemotherapy treated

population of locally advanced NSCLC patients, that there is a strong inter‐

observer agreement for this method, but the agreement is stronger for

SULpeak change, however, TLG was not included in that study (29). This

suggests that visual evaluation is a reliable alternative method for evaluation

of the cases that are “non‐evaluable” with the TLG methods.

The strengths of the present study are the strict adherence to

standardization as according to PERCIST, and the head‐on comparison of 6

different methods for evaluating response, including the PERCIST 1.0

recommendations and the analysis on categorization using various cut‐off

levels, not only dichotomizing by ROC optimization.



17

The main limitation is the large variation in the interval between

treatment and CT scans both at baseline and at follow‐up. We find the

variation in the time intervals for 18F‐FDG‐PET/CT scans acceptable, and

comparable to other studies (5). The very early progressions on CT within 4

weeks after initiating treatment were considered true progressions and as

such should not significantly influence the PET results. The five cases of

prolonged interval (> 4 weeks) between the baseline CT and treatment would

potentially attenuate the CT response and thereby increase the risk for PET to

show a better response than reflected on CT, but in four of the five cases TLG

(PERCIST(25%) agreed with the CT response. For comparison of methods we

consider it usable since it applies to all methods. However, it needs closer

attention in future, since standardization is particularly important in the early

response evaluation setting.

CONCLUSION

The present study demonstrates that using percentage change in

global TLG delineated according to PERCIST tends to be a more sensitive

method for early response evaluation of NSCLC patients during erlotinib

treatment than highest intensity lesion evaluation (SULpeak) and visual

evaluation. The method allows for prediction of a later PD on CT identifying

41% of the PDs. We intend to use this finding in future studies and in the



18

clinical setting, supplementing with visual evaluation in the few cases not

evaluable by the TLG method.

Disclosures

We have nothing to disclose.



19

REFERENCES 1. Tiseo M, Ippolito M, Scarlattei M, et al. Predictive and prognostic value

of early response assessment using 18FDG‐PET in advanced non‐small cell lung cancer patients treated with erlotinib. Cancer Chemoth Pharm. 2014;73:299‐307.

2. Sharma S V, Bell DW, Settleman J, Haber DA. Epidermal growth factor receptor mutations in lung cancer. Nat Rev Cancer. 2007;7:169‐181.

3. Pérez‐Soler R, Chachoua A, Hammond LA, et al. Determinants of tumor response and survival with erlotinib in patients with non‐small‐cell lung cancer. J Clin Oncol. 2004;22(16):3238‐3247.

4. Shepherd FA, Pereira JR, Ciuleanu T, et al. Erlotinib in previously treated non–small‐cell lung cancer. New Engl J Med. 2005;353:123‐132.

5. Zander T, Scheffler M, Nogova L, et al. Early prediction of nonprogression in advanced non‐small‐cell lung cancer treated with erlotinib by using [18F]fluorodeoxyglucose and [18F]fluorothymidine positron emission tomography. J Clin Oncol. 2011;29:1701‐1708.

6. Takahashi R, Hirata H, Tachibana I, et al. Early [18F]fluorodeoxyglucose positron emission tomography at two days of gefitinib treatment predicts clinical outcome in patients with adenocarcinoma of the lung. Clin Cancer Res. 2012;18:220‐228.

7. Aukema TS, Kappers I, Olmos R a V, et al. Is 18F‐FDG PET/CT useful for the early prediction of histopathologic response to neoadjuvant erlotinib in patients with non‐small cell lung cancer? J Nucl Med. 2010;51:1344‐1348.

8. Lynch TJ, Bell DW, Sordella R, et al. Activating mutations in the epidermal growth factor receptor underlying responsiveness of non–small‐cell lung cancer to gefitinib. New Engl J Med. 2004;350:2129‐2139.

9. Tsao MS, Sakurada A, Cutz JC, et al. Erlotinib in lung cancer ‐ molecular and clinical predictors of outcome. New Engl J Med. 2005;353:133‐144.

10. Winther‐Larsen A, Fledelius J, Demuth C, Bylov CM, Meldgaard P, Sorensen BS. Early change in FDG‐PET signal and plasma cell‐free DNA level predicts erlotinib response in EGFR wild‐type NSCLC patients. Transl Oncol. 2016;9:505‐511.

11. Su H, Bodenstein C, Dumont RA, et al. Monitoring tumor glucose utilization by positron emission tomography for the prediction of treatment response to epidermal growth factor receptor kinase inhibitors. Clin Cancer Res. 2006;12:5659‐5667.

12. Tuma RS. Sometimes size doesn’t matter: reevaluating RECIST and tumor response rate endpoints. J Natl Cancer I. 2006;98:1272‐1274.

13. Fukuoka M, Yano S, Giaccone G, et al. Multi‐institutional randomized



20

phase II trial of gefitinib for previously treated patients with advanced non‐small‐cell lung cancer. J Clin Oncol. 2003;21:2237‐2246.

14. Thatcher N, Chang A, Parikh P, et al. Gefitinib plus best supportive care in previously treated patients with refractory advanced non‐small‐cell lung cancer: results from a randomised, placebo‐controlled, multicentre study (Iressa survival evaluation in lung cancer). Lancet. 2005;366:1527‐1537.

15. Sunaga N, Oriuchi N, Kaira K, et al. Usefulness of FDG‐PET for early prediction of the response to gefitinib in non‐small cell lung cancer. Lung Cancer. 2008;59:203‐210.

16. Hachemi M, Couturier O, Vervueren L, et al. [18F]FDG positron emission tomography within two weeks of starting erlotinib therapy can predict response in non‐small cell lung cancer patients. PloS one. 2014;9:1‐10.

17. van Gool MH, Aukema TS, Schaake EE, et al. Timing of metabolic response monitoring during erlotinib treatment in non‐small cell lung cancer. J Nucl Med. 2014;55:1081‐1086.

18. Wahl RL, Jacene H, Kasamon Y, Lodge MA. From RECIST to PERCIST: Evolving considerations for PET response criteria in solid tumors. J Nucl Med. 2009;50(Suppl 1):122‐150.

19. Winther‐Larsen A, Fledelius J, Sorensen BS, Meldgaard P. Metabolic tumor burden as marker of outcome in advanced EGFR wild‐type NSCLC patients treated with erlotinib. Lung Cancer. 2016;94:81‐87.

20. Mac Manus MP, Hicks RJ, Matthews JP, et al. Positron emission tomography is superior to computed tomography scanning for response‐assessment after radical radiotherapy or chemoradiotherapy in patients with non‐small‐cell lung cancer. J Clin Oncol. 2003;21:1285‐1292.

21. Fleiss JL, Levin B, Paik MC. Statistical methods for rates and proportions. 3rd edition. John Wiley & Sons; 2003.

22. Boellaard R, Delgado‐Bolton R, Oyen WJG, et al. FDG PET/CT: EANM procedure guidelines for tumour imaging: version 2.0. Eur J Nucl Med Mol imaging. 2014;42:328‐354.

23. Kobe C, Scheffler M, Holstein A, et al. Predictive value of early and late residual 18F‐fluorodeoxyglucose and 18F‐fluorothymidine uptake using different SUV measurements in patients with non‐small‐cell lung cancer treated with erlotinib. Eur J Nucl Med Mol imaging. 2012;39:1117‐1127.

24. Moon SH, Cho S‐H, Park LC, et al. Metabolic response evaluated by 18F‐FDG PET/CT as a potential screening tool in identifying a subgroup of patients with advanced non‐small cell lung cancer for immediate maintenance therapy after first‐line chemotherapy. Eur J Nucl Med Mol imaging.2013;40:1005‐1013.

25. Kahraman D, Holstein A, Scheffler M, et al. Tumor lesion glycolysis and



21

tumor lesion proliferation for response prediction and prognostic differentiation in patients with advanced non‐small cell lung cancer treated with erlotinib. Clin Nucl Med. 2012;37:1058‐1064.

26. Nakamoto Y, Zasadny KR, Minn H, Wahl RL. Reproducibility of common semi‐quantitative parameters for evaluating lung cancer glucose metabolism with positron emission tomography using 2‐deoxy‐2‐[18F]fluoro‐D‐glucose. Mol Imaging and Biol. 2002;4:171‐178.

27. Weber WA, Gatsonis CA, Mozley PD, et al. Repeatability of 18F‐FDG PET/CT in advanced non‐small cell lung cancer: Prospective assessment in 2 multicenter trials. J Nucl Med. 2015;56:1137‐1143.

28. Kramer GM, Frings V, Hoetjes N, et al. Repeatability of quantitative whole body 18F‐FDG PET/CT uptake measures as function of uptake interval and lesion selection in non‐small cell lung cancer patients. J Nucl Med. 2016;57:1343‐1349.

29. Fledelius J, Khalil A, Hjorthaug K, Frøkiær J. Inter‐observer agreement improves with PERCIST 1.0 as opposed to qualitative evaluation in non‐small cell lung cancer patients evaluated with F‐18‐FDG PET/CT early in the course of chemo‐radiotherapy. EJNMMI Research. 2016;6:71.



1

Figures and figure legends

FIGURE 1. Patient selection

FIGURE 2. Correlation between A) SULpeak, B) TLG (PERCIST) and C) SULmax at 7-10 days and the SLD measured on CT scans performed after 9-11 weeks.



2

FIGURE 3. ROC curves for SULpeak, SULmax and the various TLG variations. The curves illustrate the parameters’ ability to predict PD on the later CT scan performed after 9‐11 weeks of treatment. The curves represent data from the 43 patients who were analyzable by all methods.

FIGURE 4. Waterfall plots for A) SULpeak, B) TLG (PERCIST) and C) SULmax showing the distribution of CT categories (light grey=PD, red=SD and black=PR). The horizontal reference lines represent the optimal cut‐off for PMR and PMD. For SULmax they represent the 15% change suggested by the EORTC criteria for early evaluation.



3

Tables

TABLE 1. Time (days) between scans and first day of treatment

Baseline (median (range))

> 4 weeks (n (%))

> 2 weeks (n (%))

Follow‐up (median (range))

Early CT (n (%))

CT 15 (4‐56) 5 (10%) 26 (52%) 77 (20‐85) 14 (28%) FDG‐PET/CT 1 (0‐21) 8 (2‐23)

> 4 (and 2) weeks = number of patients with an interval between baseline CT and first day of treatment above 4 (and 2) weeks. Early CT = number of patients with a follow‐up CT earlier than 9 weeks.

TABLE 2. Compliance with the PERCIST 1.0 standardization criteria

Parameter Baseline Follow‐up Numerical difference

PERCIST 1.0 Adherence to PERCIST 1.0

Injected activity (Mbq) Mean (std) 340 (90) 335 (87) 18 (14) Baseline +/‐ 20% 100% (50/50) Range 197‐609 199‐618 0‐54 Glucose level (mmol/liter)

Mean (std) 6.3 (0.9) 6.4 (0.9) 0.4 (0.5) < 11mM 100% (50/50) Range 4.6‐8.8 4.7‐9.0 0.0‐1.6 Uptake time (minutes) Mean (std) 58.7 (4.0) 59.3 (4.9) 5.1 (4.19) 60 +/‐ 10 min 98% (98/100*) Range 51‐69 48‐72 0‐15 Baseline +/‐ 15 min 100% (50/50)

* The uptake time at both baseline and follow up. Two patients with 48 and 72 minutes uptake time at follow‐up was included because the difference between scans in both cases was within +/‐ 15 minutes. Std is the standard deviation of the means.

TABLE 3. AUCs and sensitivity and specificity from the ROC analyses

95% CI Sens. and spec. equally important

Spec. as high as possible

Method AUC Lower Upper Sens. Spec. Cut-off Sens. Spec. Cut-off

TLG (PERCIST)

0.923 0.842 1.000 0.89 0.88 -6.6 % 0.50 1.00 9.2 %

SULpeak 0.887 0.791 0.983 0.77 0.82 -6.9 % 0.58 1.00 2.1 % SULmax 0.835 0.715 0.954 0.73 0.82 -4.5 % 0.35 1.00 13.2 % TLG 50 0.824 0.698 0.949 0.77 0.82 3.0 % 0.39 1.00 30.0 % TLG 40 0.821 0.696 0.946 0.77 0.71 -1.4 % 0.35 1.00 24.6 % TLG 30 0.790 0.645 0.934 0.73 0.76 0.0 % 0.58 0.94 11.9 %

AUCs optimized by identifying the point on the curve closest to the upper left corner (considering sensitivity and specificity equally important (middle columns)), and considering the specificity of the major importance thereby avoiding any false progressions (right columns). “Cut-off” is the corresponding optimal cut-off value for percentage change



4

TABLE 4. TLG (PERCIST (25%)), SULpeak (20%) and visual response compared to RECIST 1.1 CT TLG (PERCIST (25%)) SULpeak (20%) Visual PMR SMD PMD Total PMR SMD PMD Total PMR SMD PMD Total

PR 4 0 0 4 3 2 0 5 4 1 0 5 SD 6 10 0 16 5 12 0 17 5 11 1 17

PD 1 15 11 27 1 19 8 28 1 17 10 28 Total 1 25 11 47 9 33 8 50 10 29 11 50

TABLE 5. Kappa values for all methods Method Kappa (95% CI)

Visual 0.47 (0.24‐0.70) SULpeak 30% 0.39 (0.19‐0.59) SULpeak 25% 0.41 (0.20‐0.62) SULpeak 20% 0.41 (0.20‐0.62) SULpeak 20% 0.42 (0.22‐0.61) TLG (PERCIST) 45/75% 0.25 (n.c.) TLG (PERCIST) 50% 0.29 (0.20‐0.39) TLG (PERCIST) 40% 0.32 (0.16‐0.47) TLG (PERCIST) 30% 0.45 (0.24‐0.67) TLG (PERCIST) 25% 0.53 (0.31‐0.75) TLG (PERCIST) 20% 0.49 (0.29‐0.69) TLG (50) 45/70% 0.23 (n.c.)

TLG (50) 50% 0.27 (0.17‐0.36) TLG (50) 40% 0.25 (0.16‐0.33) TLG (50) 30% 0.35 (0.16‐0.53) TLG (50) 25% 0.33 (0.12‐0.53) TLG (50) 20% 0.38 (0.16‐0.61) TLG (40) 45/70% 0.27 (n.c.) TLG (40) 50% 0.34 (0.16‐0.52) TLG (40) 40% 0.33 (0.16‐0.50) TLG (40) 30% 0.42 (0.18‐0.66) TLG (40) 25% 0.44 (0.20‐0.69) TLG (40) 20% 0.43 (0.20‐0.66) TLG (30) 45/70% 0.24 (n.c.) TLG (30) 50% 0.33 (0.12‐0.54) TLG (30) 40% 0.38 (0.14‐0.62)

TLG (30) 30% 0.32 (0.12‐0.53) TLG (30) 25% 0.38 (0.15‐0.61)

TLG (30) 20% 0.41 (0.18‐0.64)

Quadratic weighted kappa values (95% confidence intervals) for all the methods analyzed including the various cut‐offs for response and progression. n.c.= not calculable by the method used, described by Fleiss et al (21).



Supplemental Figure 1

Correlation between %TLGs, %MTVs and %SULmeans at 7-10 days and change in CT

diameter (%SLD after 9-11 weeks.



Supplemental Figure 2 Waterfall plots for SULmax and TLGs: In order to find PD patients without excluding any non-PD patients, we want to find a method that does not include any reds or blacks (or as far to right as possible) in the progression (left) side of the plots



SUPLEMENTARY TABLE 1: An overview of the various evaluation methods. Method Parameter Delineation SMD/PMR SMD/PMD 1 Visual change Sign decrease Sign increase 2a SULpeak 30% decrease 30% increase 2b SULpeak 25% decrease 25% increase 2c SULpeak 20% decrease 20% increase 2d SULpeak 15% decrease 15% increase 3a TLG PERCIST 2 SD above mean SUL liver* 45% decrease 75% increase 3b TLG PERCIST 2 SD above mean SUL liver* 50% decrease 50% increase 3c TLG PERCIST 2 SD above mean SUL liver* 40% decrease 40% increase 3d TLG PERCIST 2 SD above mean SUL liver* 30% decrease 30% increase 3e TLG PERCIST 2 SD above mean SUL liver* 25% decrease 25% increase 3f TLG PERCIST 2 SD above mean SUL liver* 20% decrease 20% increase 4a TLG 50 50% of SULmax of lesion 45% decrease 75% increase 4b TLG 50 50% of SULmax of lesion 50% decrease 50% increase 4c TLG 50 50% of SULmax of lesion 40% decrease 40% increase 4d TLG 50 50% of SULmax of lesion 30% decrease 30% increase 4e TLG 50 50% of SULmax of lesion 25% decrease 25% increase 4f TLG 50 50% of SULmax of lesion 20% decrease 20% increase 5a TLG 40 40% of SULmax of lesion 45% decrease 75% increase 5b TLG 40 40% of SULmax of lesion 50% decrease 50% increase 5c TLG 40 40% of SULmax of lesion 40% decrease 40% increase 5d TLG 40 40% of SULmax of lesion 30% decrease 30% increase 5e TLG 40 40% of SULmax of lesion 25% decrease 25% increase 5f TLG 40 40% of SULmax of lesion 20% decrease 20% increase 6a TLG 30 30% of SULmax of lesion 45% decrease 75% increase 6b TLG 30 30% of SULmax of lesion 50% decrease 50% increase 6c TLG 30 30% of SULmax of lesion 40% decrease 40% increase 6d TLG 30 30% of SULmax of lesion 30% decrease 30% increase 6e TLG 30 30% of SULmax of lesion 25% decrease 25% increase 6f TLG 30 30% of SULmax of lesion 20% decrease 20% increase

* normal mean liver = mean SUL in a 3 cm in diameter ROI in the right lobe of the liver (as according to PERCIST 1.0). The two methods described in PERCIST 1.0 are highlighted in bold. Supplemented with SULmax 25 and SUL max (15).



Supplementary Table 2. Data on blood Glucose levels, injected FDG-activity and uptake time per patient and the variation between scans. Patient Blood Glucose (mM) Injected FDG activity (MBq) Uptake time (minutes)

A B Diff.(%) A B Diff. (%) A B Diff. 2 8.1 7.4 8.6 284 312 9.9 58 65 7 4 6.1 7.6 24.6 277 297 7.2 56 65 9 6 7.2 5.9 18.1 237 230 -3.0 56 55 1 7 7.4 7.3 1.4 484 465 -3.9 59 48 11 8 5.5 6.8 23.6 372 377 1.3 55 57 2 9 4.9 5.7 16.3 344 331 -3.8 51 56 5 10 6.8 7.7 13.2 450 421 -6.4 57 61 4 11 7.9 9.0 13.9 415 390 -6.0 51 66 15 12 6.4 6.6 3.1 288 321 11.5 61 60 1 13 7.8 6.9 11.5 475 440 -7.4 69 55 14 14 6.4 6.4 0.0 426 385 -9.6 59 60 1 15 6.2 6.3 1.6 371 323 -12.9 59 61 2 16 7.4 5.8 21.6 323 319 -1.2 54 54 0 17 5.9 6.4 8.5 330 331 0.3 57 60 3 18 6.6 5.9 10.6 356 345 -3.1 60 64 4 19 5.7 5.5 3.5 237 263 11.0 62 63 1 22 6.2 5.6 9.7 547 505 -7.7 58 50 8 24 5.3 5.3 0.0 340 348 2.4 63 59 4 25 4.6 5.8 26.1 340 345 1.5 58 63 5 26 7.1 7.6 7.0 310 287 -7.4 59 62 3 27 6.3 6.3 0.0 406 399 -1.7 56 57 1 28 7.6 7.7 1.3 381 412 8.1 61 58 3 29 4.9 6.4 30.6 434 421 -3.0 65 60 5 30 5.7 6.0 5.3 255 244 -4.3 58 58 0 31 7.4 8.4 13.5 242 259 7.0 60 57 3 32 6.7 6.2 7.5 363 374 3.0 60 67 7 33 8.8 7.8 11.4 430 392 -8.8 55 62 7 34 5.2 6.8 30.8 306 330 7.8 55 55 0 35 6.3 6.7 6.3 485 460 -5.2 51 59 8 36 6.3 6.2 1.6 295 316 7.1 59 58 1 39 6.2 6.3 1.6 323 306 -5.3 57 56 1 40 5.6 5.8 3.6 295 295 0.0 57 60 3 41 5.5 5.9 7.3 253 227 -10.3 57 72 15 45 7.7 7.9 2.6 448 448 0.0 63 54 9 46 5.8 5.7 1.7 214 202 -5.6 68 55 13 47 6.3 5.3 15.9 295 301 2.0 58 66 8 48 5.9 6.7 13.6 302 248 -17.9 62 66 4 49 6.7 6.9 3.0 248 255 2.8 59 57 2 51 5.3 4.9 7.5 286 293 2.4 55 56 1 53 6.4 6.3 1.6 609 618 1.5 65 59 6 54 6.4 7.1 10.9 350 348 -0.6 59 51 8 55 6.2 5.8 6.5 330 287 -13.0 57 55 2 56 6.2 6.4 3.2 240 259 7.9 59 56 3 57 5.9 5.5 6.8 242 245 1.2 59 70 11 59 5.6 5.4 3.6 258 251 -2.7 66 53 13 60 6.3 7.7 22.2 197 199 1.0 66 63 3 62 5.4 4.7 13.0 344 375 9.0 56 63 7 63 6.1 5.7 6.6 252 254 0.8 54 58 4 64 5.9 6.8 15.3 398 404 1.5 61 58 3 65 5.3 5.6 5.7 305 313 2.6 57 61 4

A=baseline scan, B=Follow-up scan, Diff. = difference, for Glucose concentration and FDG-actvity it is percentage difference and for uptake-time, the difference is in minutes.



Supplementary Table 3. The categorization tables, sensitivity, specificity and predictive values for all methods for PMD/non-PMD predicting PD/non-PD on CT

Sens Spec PPV NPV Visual 0.36 0.95 0.91 0.54 SULpeak 30% 0.25 1.00 1.00 0.52 SULpeak 25% 0.29 1.00 1.00 0.52 SULpeak 20% 0.29 1.00 1.00 0.52 SULpeak 20% 0.29 1.00 1.00 0.52 TLG (PERCIST) 45/70% 0.04 1.00 1.00 0.43 TLG (PERCIST) 50% 0.11 1.00 1.00 0.45 TLG (PERCIST) 40% 0.19 1.00 1.00 0.48 TLG (PERCIST) 30% 0.33 1.00 1.00 0.53 TLG (PERCIST) 25% 0.41 1.00 1.00 0.56 TLG (PERCIST) 20% 0.41 1.00 1.00 0.56 TLG (50) 45/70% 0.11 1.00 1.00 0.44 TLG (50) 50% 0.15 1.00 1.00 0.45 TLG (50) 40% 0.22 1.00 1.00 0.48 TLG (50) 30% 0.33 1.00 1.00 0.51 TLG (50) 25% 0.38 0.89 0.83 0.50 TLG (50) 20% 0.48 0.89 0.87 0.55 TLG (40) 45/70% 0.07 1.00 1.00 0.42 TLG (40) 50% 0.19 1.00 1.00 0.45 TLG (40) 40% 0.22 1.00 1.00 0.46 TLG (40) 30% 0.30 1.00 1.00 0.49 TLG (40) 25% 0.33 1.00 1.00 0.50 TLG (40) 20% 0.33 0.94 0.90 0.49 TLG (30) 45/70% 0.04 0.94 0.50 0.39 TLG (30) 50% 0.19 0.94 0.83 0.43 TLG (30) 40% 0.27 0.94 0.88 0.46 TLG (30) 30% 0.31 0.94 0.89 0.47 TLG (30) 25% 0.31 0.94 0.89 0.47 TLG (30) 20% 0.42 0.94 0.92 0.52

Table A: Sensitivity, specificity, PPV and NPV for all methods comparing progression versus non-progression on FDG-PET (PMD vs non-PMD) to the 9-11 week CT scan (PD vs non-PD). 2x2 tables for all methods are presented below.



B)

3x3 tables for all methods comparing classification on FDG-PET/CT to the RECIST classification

for the 10 week CT.

Visual PMR SMD PMD Total

PR 4 1 0 5

SD 5 11 1 17

PD 1 17 10 28

Total 10 29 11 50

SULpeak 30% PMR SMD PMD Total

PR 3 2 0 5

SD 5 12 0 17

PD 1 20 7 28

Total 9 34 7 50


PR 3 2 0 5

SD 5 12 0 17

PD 1 19 8 28

Total 9 33 8 50


PR 3 2 0 5

SD 5 12 0 17

PD 1 19 8 28

Total 9 33 8 50


PR 3 2 0 5

SD 7 10 0 17

PD 1 19 8 28

Total 11 31 8 50



TLG (PERCIST)

45/70%

PMR SMD PMD Total

PR 2 2 0 4

SD 4 12 0 16

PD 0 26 1 27

Total 6 40 1 47

TLG(PERCIST)50% PMR SMD PMD Total

PR 2 2 0 4

SD 3 13 0 16

PD 0 24 3 27

Total 5 39 3 47


PR 2 2 0 4

SD 5 11 0 16

PD 1 21 5 27

Total 8 34 5 47


PR 3 1 0 4

SD 6 10 0 16

PD 1 17 9 27

Total 10 28 9 47


PR 4 0 0 4

SD 6 10 0 16

PD 1 15 11 27

Total 11 25 11 47


PR 4 0 0 4

SD 8 8 0 16

PD 2 14 11 27

Total 14 22 11 47



TLG(50)45/70% PMR SMD PMD Total

PR 2 2 0 4

SD 4 11 0 15

PD 2 22 3 27

Total 8 35 3 46

TLG(50)40% PMR SMD PMD Total

PR 2 2 0 4

SD 5 10 0 15

PD 4 17 6 27

Total 11 29 6 46


PR 3 1 0 4

SD 5 8 2 15

PD 4 13 10 27

Total 12 22 12 46


PR 2 2 0 4

SD 5 10 0 15

PD 2 21 4 27

Total 9 33 4 46


PR 3 1 0 4

SD 5 10 0 15

PD 4 14 9 27

Total 12 25 9 46


PR 3 1 0 4

SD 5 8 2 15

PD 4 10 13 27

Total 12 19 15 46




PR 2 2 0 4

SD 3 11 0 14

PD 0 25 2 27

Total 5 38 2 45


PR 2 2 0 4

SD 3 11 0 14

PD 1 20 6 27

Total 6 33 6 45


PR 3 1 0 4

SD 3 11 0 14

PD 1 17 9 27

Total 7 29 9 45


PR 2 2 0 4

SD 3 11 0 14

PD 0 22 5 27

Total 5 35 5 45


PR 3 1 0 4

SD 3 11 0 14

PD 1 18 8 27

Total 7 30 8 45


PR 3 1 0 4

SD 5 8 1 14

PD 1 17 9 27

Total 9 26 10 45




PR 2 1 0 3

SD 3 10 1 14

PD 0 25 1 26

Total 5 36 2 43


PR 2 1 0 3

SD 3 10 1 14

PD 0 19 7 26

Total 5 30 8 43


PR 3 0 0 3

SD 4 9 1 14

PD 2 16 8 26

Total 9 25 9 43


PR 2 1 0 3

SD 3 10 1 14

PD 0 21 5 26

Total 5 32 6 43


PR 2 1 0 3

SD 3 10 1 14

PD 2 16 8 26

Total 7 27 9 43


PR 3 0 0 3

SD 5 8 1 14

PD 3 12 11 26

Total 11 20 12 43



C) 2x2 tables for CT PD/non-PD versus Methods PMR/non-PMR (left column) and PMD/non-PMD (right column) true positives are marked in red. The corresponding Sensitivity, specificity, PPV and NPV below each table for predicting PD on CT on predicting PD on CT. When using non-PMR as a sign of progression sensitivity is high range: 0.85-1.00, but specificity is rather low range: 0.25-0.50, best for method 3.

Using PMD as sign of progression the specificity is high (0.89-1.00), but the sensitivity is low:

0.04-0.48.

Visual Non-PMR PMR Total

Non-PD 13 9 22

PD 27 1 28

Total 44 10 50 Sensitivity = 0.96 Sensitivity = 0.36 Specificity = 0.41 Specificity = 0.95 PPV = 0.66 PPV = 0.91 NPV = 1.00 NPV = 0.54

SULpeak 30% Non-PMR PMR Total

Non-PD 14 8 22

PD 27 1 28



Non-PD 14 8 22

PD 27 1 28


Visual Non-PMD PMD Total

Non-PD 21 1 22

PD 18 10 28

Total 39 11 50

SULpeak 30% Non-PMD PMD Total

Non-PD 22 0 22

PD 21 7 28

Total 43 7 49


Non-PD 22 0 22

PD 20 8 28

Total 42 8 50




Non-PD 14 8 22

PD 27 1 28



Non-PD 12 10 22

PD 27 1 28


TLG(PERCIST)

45/70%

Non-PMR PMR Total

Non-PD 14 6 20

PD 27 0 27



Non-PD 22 0 22

PD 20 8 28

Total 42 8 50


Non-PD 22 0 22

PD 20 8 28

Total 42 8 50

TLG(PERCIST)

45/70%

Non-PMD PMD Total

Non-PD 20 0 20

PD 26 1 27

Total 46 1 47



TLG(PERCIST)50% Non-PMR PMR Total

Non-PD 15 5 20

PD 27 0 27



Non-PD 13 7 20

PD 26 1 27



Non-PD 11 9 20

PD 26 1 27


TLG(PERCIST)50% Non-PMD PMD Total

Non-PD 20 0 20

PD 24 3 27

Total 44 3 47


Non-PD 20 0 20

PD 22 5 27

Total 42 5 47


Non-PD 20 0 20

PD 18 9 27

Total 38 9 47




Non-PD 10 10 20

PD 26 1 27



Non-PD 8 12 20

PD 25 2 27


TLG(50)45/70% Non-PMR PMR Total

Non-PD 13 6 19

PD 25 2 26



Non-PD 20 0 20

PD 16 11 27

Total 36 11 47


Non-PD 20 0 20

PD 16 11 27

Total 36 11 47

TLG(50)45/70% Non-PMD PMD Total

Non-PD 19 0 19

PD 24 3 27

Total 43 3 46



TLG(50)50% Non-PMR PMR Total

Non-PD 12 7 19

PD 25 2 27



Non-PD 12 7 19

PD 23 4 27



Non-PD 11 8 19

PD 23 4 27


TLG(50)50% Non-PMD PMD Total

Non-PD 19 0 19

PD 23 4 27

Total 42 4 46


Non-PD 19 0 19

PD 21 6 27

Total 40 6 46


Non-PD 19 0 19

PD 18 9 27

Total 37 9 46




Non-PD 11 8 19

PD 23 4 27



Non-PD 11 8 19

PD 23 4 27



Non-PD 13 5 18

PD 27 0 27



Non-PD 17 2 19

PD 17 10 27

Total 34 12 46


Non-PD 17 2 19

PD 14 13 27

Total 31 15 46


Non-PD 18 0 18

PD 25 2 27

Total 43 2 45




Non-PD 13 5 18

PD 27 0 27



Non-PD 12 6 18

PD 26 1 27



Non-PD 12 6 18

PD 26 1 27



Non-PD 18 0 18

PD 22 5 27

Total 40 5 45


Non-PD 18 0 18

PD 21 6 27

Total 39 6 45


Non-PD 18 0 18

PD 19 8 27

Total 37 8 45




Non-PD 12 6 18

PD 26 1 27



Non-PD 10 8 18

PD 26 1 27



Non-PD 12 5 17

PD 26 0 26



Non-PD 18 0 18

PD 18 9 27

Total 36 9 45


Non-PD 17 1 18

PD 18 9 27

Total 35 10 45


Non-PD 16 1 17

PD 25 1 26

Total 41 2 43




Non-PD 12 5 17

PD 26 0 26



Non-PD 12 5 17

PD 26 0 26



Non-PD 12 5 17

PD 24 2 25



Non-PD 16 1 17

PD 21 5 26

Total 37 6 43


Non-PD 16 1 17

PD 19 7 26

Total 35 8 43


Non-PD 16 1 17

PD 18 8 26

Total 34 9 43




Non-PD 10 7 17

PD 24 2 26



Non-PD 9 8 17

PD 23 3 25



Non-PD 16 1 17

PD 18 8 26

Total 34 9 43


Non-PD 16 1 17

PD 15 11 26

Total 31 12 43



Doi: 10.2967/jnumed.117.193003Published online: May 10, 2017.J Nucl Med. Jørgen Frøkiær and Peter MeldgaardJoan Fledelius, Anne Winther-Larsen, Azza Ahmed Khalil, Catharina Mølgaard Bylov, Karin Hjorthaug, Aksel Bertelsen,

A comparison of assessment methods−treated NSCLC patients F-FDG-PET/CT for very early response evaluation predicts CT response in Erlotinib18

http://jnm.snmjournals.org/content/early/2017/05/10/jnumed.117.193003This article and updated information are available at:

http://jnm.snmjournals.org/site/subscriptions/online.xhtml

Information about subscriptions to JNM can be found at:

http://jnm.snmjournals.org/site/misc/permission.xhtmlInformation about reproducing figures, tables, or other portions of this article can be found online at:

and the final, published version.proofreading, and author review. This process may lead to differences between the accepted version of the manuscript

ahead of print area, they will be prepared for print and online publication, which includes copyediting, typesetting,JNMcopyedited, nor have they appeared in a print or online issue of the journal. Once the accepted manuscripts appear in the

. They have not beenJNM ahead of print articles have been peer reviewed and accepted for publication in JNM

(Print ISSN: 0161-5505, Online ISSN: 2159-662X)1850 Samuel Morse Drive, Reston, VA 20190.SNMMI | Society of Nuclear Medicine and Molecular Imaging

is published monthly.The Journal of Nuclear Medicine

© Copyright 2017 SNMMI; all rights reserved.


http://jnm.snmjournals.org/content/early/2017/05/10/jnumed.117.193003

http://jnm.snmjournals.org/site/misc/permission.xhtml

http://jnm.snmjournals.org/site/subscriptions/online.xhtml


18f‐fdg‐pet/ct for very early response evaluation predicts ct...

Documents