supplementary materials for · curated pathways from the nci pathway interaction database using the...

www.sciencetranslationalmedicine.org/cgi/content/full/7/303/303ra139/DC1

Supplementary Materials for

Magnetic resonance image features identify glioblastoma phenotypic

subtypes with distinct molecular pathway activities

Haruka Itakura, Achal S. Achrol, Lex A. Mitchell, Joshua J. Loya, Tiffany Liu,

Erick M. Westbroek, Abdullah H. Feroze, Scott Rodriguez, Sebastian Echegaray,

Tej D. Azad, Kristen W. Yeom, Sandy Napel, Daniel L. Rubin, Steven D. Chang,

Griffith R. Harsh IV, Olivier Gevaert*

*Corresponding author. E-mail: [email protected]

Published 2 September 2015, Sci. Transl. Med. 7, 303ra139 (2015)

DOI: 10.1126/scitranslmed.aaa7582

The PDF file includes:

Methods

Fig. S1. Relative contributions of the top 24 imaging features in characterizing

each of the clusters.

Table S1. Clinical characteristics of the Stanford cohort before and after selection

of the development cohort.

Table S2. Clinical characteristics of the TCGA cohort before and after selection

of the validation cohort.

Table S3. All 2D and multislice 2D quantitative MR image features used for

analysis.

Table S4. Two-dimensional and multislice 2D quantitative MR image features

significantly associated with each cluster.

Table S5. Cluster assignment by subjects in the development cohort with and

without midline-crossing lesions.

Table S6. Regulatory signaling pathways significantly associated with each

cluster.

References (45–47)

SUPPLEMENTARY METHODS

Two-dimensional feature representations

To represent 2D features, we computed quantitative image features from the largest slice of

the tumor volume. To represent multislice 2D features, we aggregated 2D features from all

slices within the three-dimensional tumor volume. The quantitative imaging feature

extraction pipeline thus generated separate 2D and multislice 2D features for each patient.

For each subject in each cohort, this generated a feature vector of 193 2D, and 195

multislice 2D quantitative image features, resulting in a total of 388 features (table S3).

Validation of clusters

To validate the reproducibility of the clusters derived from consensus clustering in the

development cohort, we used the IGP analysis to demonstrate the existence of these clusters

in the validation cohort. IGP, which measures the proportion of samples in the group whose

nearest neighbor is also classified to the same group, validated the reproducibility and

robustness of the generated clusters by quantifying how well centroids from the

development cohort predicted test set co-memberships (24).

Statistical significance in IGP denotes low probabilities that the clusters originated

from a null distribution, in which centroids are placed randomly in the data and,

consequently, result in low quality clusters. The p-value of a cluster is the fraction of the null

distribution IGPs that approximate the value of 1 (perfect IGP) more than the actual IGP of

the cluster. The null hypothesis that a cluster is not a high-quality cluster is rejected if the

actual IGP of the group is high enough (i.e. approximates 1). A significant p-value indicates

that a high-quality cluster corresponding to the original cluster was found in an independent

dataset (validation cohort). Hence, the development cohort cluster is deemed reproducible

and validated.

Next we trained a PAM model on the development cohort, fitting a nearest shrunken

centroid classifier on the centroids generated from the development cohort clusters. By

fitting these centroids in the validation cohort, we assigned individual patients to imaging

subtypes defined by the development cohort.

Mapping canonical pathways to imaging subtypes

We used the matched molecular data in the TCGA validation cohort to assign canonical

signaling pathways to the discovered imaging subtypes. TCGA provided CNV and gene

expression data for 560 and 495 subjects, respectively. These data were integrated with

curated pathways from the NCI Pathway Interaction Database using the PARADIGM

algorithm to infer signaling pathway activities (up-regulation or down-regulation) to

generate a molecular signaling pathway consisting of 131 features. Next we used the SAM

algorithm to identify specific regulatory pathways that were significantly associated with

each of the identified imaging subtypes for each of the 144 subjects. We placed the FDR

threshold at <5%.

Evaluating imaging subtypes for traditional risk factors

We examined the subtypes for statistically significant differences in historically associated

risk factors. Such factors included both clinical (age, KPS) (4, 45, 46) and molecular factors,

including previously described gene expression cluster molecular subgroups (8), as well as

CIMP status (10), IDH1 mutation (6, 7), MGMT hypermethylation (47), and EGFR

amplification (9). Tumor volume data were available only for the development cohort. We

compared across-cluster values for all available data, using the Kruskal-Wallis one-way

analysis of variance by ranks test for continuous variables and Fisher’s exact test for

categorical variables.

SUPPLEMENTARY FIGURES

Figure S1. Relative contributions of the top 24 imaging features in characterizing each

of the clusters. The graphic displays the multivariate combination of the top 24

differentially expressed 2D and multislice 2D (2D-MS) imaging features across the three

clusters (table S4). Displacement of colored bars to the right of the central vertical line

demonstrates over-expression, whereas displacement to the left indicates under-expression

of the given imaging feature. The length of the bar signifies magnitude.

-1.5 -0.5 0.5 1.5 -1.5 -0.5 0.5 1.5 -1.5 -0.5 0.5 1.5

2D-MS-LAII2R3.iqr2D-MS-LAII2R3.madmedian2D-MS-LAII2R3.madmean

2D-MS-LAII2R3.var2D-MS-LAII2R3.min2D-MS-LAII2R5.iqr2D-MS-RDS.robustmin2D-MS-RDS.trimmedmean25perc

2D-MS-RDS.mean2D-MS-RDS.min2D-MS-EdgeSharpnessWindow.robustmin2D-MS-EdgeSharpnessWindow.mode2D-MS-EdgeSharpnessWindow.min2D-MS-compactness2dmean2D-LAII2R5.range2D-LAII2R5.madmean

2D-LAII2R8.range2D-RDS.robustmin2D-EdgeSharpnessScale.range2D-EdgeSharpnessScale.iqr2D-EdgeSharpnessScale.madmedian

2D-EdgeSharpnessScale.madmean2D-EdgeSharpnessWindow.min

2D-Compactness2dmean

Cluster1 Cluster2 Cluster3

SUPPLEMENTARY TABLES

Table S1. Clinical characteristics of the Stanford cohort before and after selection of

the development cohort.

Stanford Cohort Stanford Development Cohort

Characteristics (n = 364) (n = 121)

Age, mean (SD) 62.2 (14.1) 64.6 (14.1)

Sex, n male (%) 209 (57) 69 (57)

KPS, n (%)

>70% 192 (53) 65 (54)

50-70% 141 (39) 44 (37)

<50% 28 (8) 11 (9)

Mean ± SD 71.7 ± 16.4 71.7 ± 17

Table S2. Clinical characteristics of the TCGA cohort before and after selection of the

validation cohort. Of the validation cohort, 114 patients had clinical data.

TCGA Cohort TCGA Validation Cohort

Characteristic (n = 575) (n = 144)

Age, mean ± SD 57.9 ± 14.4 58.5 ± 15

Sex, n male (%) 352 (61) 73 (64)

KPS, n (%)

>70% 317 (55) 78 (68)

50-70% 89 (15) 19 (17)

<50% 18 (3) 1 (1)

Missing 151 16

Mean ± SD 77.2 ± 14.6 78.6 ± 12.3

Table S3. All 2D and multislice 2D quantitative MR image features used for analysis. Quantitative image features were extracted using a pipeline previously applied in non–small

cell lung cancer (19) and glioblastoma (15) to characterize intensity, shape, edge sharpness,

and texture of lesions from images.

Number Image feature

1 2D-spherecity

2 2D-volume

3 2D-surfaceArea

4 2D-roughness2dmean

5 2D-compactness2dmean

6 2D-LowIntensityProportion

7 2D-HistogramRaw-min

8 2D-HistogramRaw-mean

9 2D-HistogramRaw-median

10 2D-HistogramRaw-max

11 2D-HistogramRaw-var

12 2D-HistogramRaw-skewness

13 2D-HistogramRaw-kurtosis

14 2D-HistogramRaw-madmean

15 2D-HistogramRaw-madmedian

16 2D-HistogramRaw-iqr

17 2D-HistogramRaw-mode

18 2D-HistogramRaw-range

19 2D-HistogramRaw-trimmedmean25perc

20 2D-HistogramRaw-harmmean

21 2D-HistogramRaw-robustmin

22 2D-HistogramRaw-robustmax

23 2D-HistogramRaw-robustskewness

24 2D-HistogramRaw-robustkurtosis

25 2D-HistogramRaw-entropy

26 2D-EdgeSharpnessWindow-min

27 2D-EdgeSharpnessWindow-mean

28 2D-EdgeSharpnessWindow-median

29 2D-EdgeSharpnessWindow-max

30 2D-EdgeSharpnessWindow-var

31 2D-EdgeSharpnessWindow-skewness

32 2D-EdgeSharpnessWindow-kurtosis

33 2D-EdgeSharpnessWindow-madmean

34 2D-EdgeSharpnessWindow-madmedian

35 2D-EdgeSharpnessWindow-iqr

36 2D-EdgeSharpnessWindow-mode

37 2D-EdgeSharpnessWindow-range

38 2D-EdgeSharpnessWindow-trimmedmean25perc

39 2D-EdgeSharpnessWindow-harmmean

40 2D-EdgeSharpnessWindow-robustmin

41 2D-EdgeSharpnessWindow-robustmax

42 2D-EdgeSharpnessWindow-robustskewness

43 2D-EdgeSharpnessWindow-robustkurtosis

44 2D-EdgeSharpnessWindow-entropy

45 2D-EdgeSharpnessScale-min

46 2D-EdgeSharpnessScale-mean

47 2D-EdgeSharpnessScale-median

48 2D-EdgeSharpnessScale-max

49 2D-EdgeSharpnessScale-var

50 2D-EdgeSharpnessScale-skewness

51 2D-EdgeSharpnessScale-kurtosis

52 2D-EdgeSharpnessScale-madmean

53 2D-EdgeSharpnessScale-madmedian

54 2D-EdgeSharpnessScale-iqr

55 2D-EdgeSharpnessScale-mode

56 2D-EdgeSharpnessScale-range

57 2D-EdgeSharpnessScale-trimmedmean25perc

58 2D-EdgeSharpnessScale-harmmean

59 2D-EdgeSharpnessScale-robustmin

60 2D-EdgeSharpnessScale-robustmax

61 2D-EdgeSharpnessScale-robustskewness

62 2D-EdgeSharpnessScale-robustkurtosis

63 2D-EdgeSharpnessScale-entropy

64 2D-EdgeSharpnessBlurryness-min

65 2D-EdgeSharpnessBlurryness-mean

66 2D-EdgeSharpnessBlurryness-median

67 2D-EdgeSharpnessBlurryness-max

68 2D-EdgeSharpnessBlurryness-var

69 2D-EdgeSharpnessBlurryness-skewness

70 2D-EdgeSharpnessBlurryness-kurtosis

71 2D-EdgeSharpnessBlurryness-madmean

72 2D-EdgeSharpnessBlurryness-madmedian

73 2D-EdgeSharpnessBlurryness-iqr

74 2D-EdgeSharpnessBlurryness-mode

75 2D-EdgeSharpnessBlurryness-range

76 2D-EdgeSharpnessBlurryness-trimmedmean25perc

77 2D-EdgeSharpnessBlurryness-harmmean

78 2D-EdgeSharpnessBlurryness-robustmin

79 2D-EdgeSharpnessBlurryness-robustmax

80 2D-EdgeSharpnessBlurryness-robustskewness

81 2D-EdgeSharpnessBlurryness-robustkurtosis

82 2D-EdgeSharpnessBlurryness-entropy

83 2D-RDS-min

84 2D-RDS-mean

85 2D-RDS-median

86 2D-RDS-var

87 2D-RDS-skewness

88 2D-RDS-kurtosis

89 2D-RDS-madmean

90 2D-RDS-madmedian

91 2D-RDS-iqr

92 2D-RDS-range

93 2D-RDS-trimmedmean25perc

94 2D-RDS-harmmean

95 2D-RDS-robustmin

96 2D-RDS-robustskewness

97 2D-RDS-robustkurtosis

98 2D-RDS-entropy

99 2D-LAII2R10-min

100 2D-LAII2R10-mean

101 2D-LAII2R10-median

102 2D-LAII2R10-max

103 2D-LAII2R10-var

104 2D-LAII2R10-skewness

105 2D-LAII2R10-kurtosis

106 2D-LAII2R10-madmean

107 2D-LAII2R10-madmedian

108 2D-LAII2R10-iqr

109 2D-LAII2R10-mode

110 2D-LAII2R10-range

111 2D-LAII2R10-trimmedmean25perc

112 2D-LAII2R10-harmmean

113 2D-LAII2R10-robustmin

114 2D-LAII2R10-robustmax

115 2D-LAII2R10-robustskewness

116 2D-LAII2R10-robustkurtosis

117 2D-LAII2R10-entropy

118 2D-LAII2R8-min

119 2D-LAII2R8-mean


121 2D-LAII2R8-max

122 2D-LAII2R8-var





127 2D-LAII2R8-iqr

128 2D-LAII2R8-mode









137 2D-LAII2R5-min

138 2D-LAII2R5-mean


140 2D-LAII2R5-max

141 2D-LAII2R5-var





146 2D-LAII2R5-iqr

147 2D-LAII2R5-mode









156 2D-LAII2R3-min

157 2D-LAII2R3-mean


159 2D-LAII2R3-max

160 2D-LAII2R3-var





165 2D-LAII2R3-iqr

166 2D-LAII2R3-mode









175 2D-LAII2R2-min

176 2D-LAII2R2-mean


178 2D-LAII2R2-max

179 2D-LAII2R2-var





184 2D-LAII2R2-iqr

185 2D-LAII2R2-mode









194 2D-MultiSlice-spherecity

195 2D-MultiSlice-volume

196 2D-MultiSlice-surfaceArea

197 2D-MultiSlice-roughness2dmean

198 2D-MultiSlice-roughness2dvar

199 2D-MultiSlice-compactness2dmean

200 2D-MultiSlice-compactness2dvar

201 2D-MultiSlice-LowIntensityProportion

202 2D-MultiSlice-HistogramRaw-min

203 2D-MultiSlice-HistogramRaw-mean

204 2D-MultiSlice-HistogramRaw-median

205 2D-MultiSlice-HistogramRaw-max

206 2D-MultiSlice-HistogramRaw-var

207 2D-MultiSlice-HistogramRaw-skewness

208 2D-MultiSlice-HistogramRaw-kurtosis

209 2D-MultiSlice-HistogramRaw-madmean

210 2D-MultiSlice-HistogramRaw-madmedian

211 2D-MultiSlice-HistogramRaw-iqr

212 2D-MultiSlice-HistogramRaw-mode

213 2D-MultiSlice-HistogramRaw-range

214 2D-MultiSlice-HistogramRaw-trimmedmean25perc

215 2D-MultiSlice-HistogramRaw-harmmean

216 2D-MultiSlice-HistogramRaw-robustmin

217 2D-MultiSlice-HistogramRaw-robustmax

218 2D-MultiSlice-HistogramRaw-robustskewness

219 2D-MultiSlice-HistogramRaw-robustkurtosis

220 2D-MultiSlice-HistogramRaw-entropy

221 2D-MultiSlice-EdgeSharpnessWindow-min

222 2D-MultiSlice-EdgeSharpnessWindow-mean

223 2D-MultiSlice-EdgeSharpnessWindow-median

224 2D-MultiSlice-EdgeSharpnessWindow-max

225 2D-MultiSlice-EdgeSharpnessWindow-var

226 2D-MultiSlice-EdgeSharpnessWindow-skewness

227 2D-MultiSlice-EdgeSharpnessWindow-kurtosis

228 2D-MultiSlice-EdgeSharpnessWindow-madmean

229 2D-MultiSlice-EdgeSharpnessWindow-madmedian

230 2D-MultiSlice-EdgeSharpnessWindow-iqr

231 2D-MultiSlice-EdgeSharpnessWindow-mode

232 2D-MultiSlice-EdgeSharpnessWindow-range

233 2D-MultiSlice-EdgeSharpnessWindow-trimmedmean25perc

234 2D-MultiSlice-EdgeSharpnessWindow-harmmean

235 2D-MultiSlice-EdgeSharpnessWindow-robustmin

236 2D-MultiSlice-EdgeSharpnessWindow-robustmax

237 2D-MultiSlice-EdgeSharpnessWindow-robustskewness

238 2D-MultiSlice-EdgeSharpnessWindow-robustkurtosis

239 2D-MultiSlice-EdgeSharpnessWindow-entropy

240 2D-MultiSlice-EdgeSharpnessScale-min

241 2D-MultiSlice-EdgeSharpnessScale-mean

242 2D-MultiSlice-EdgeSharpnessScale-median

243 2D-MultiSlice-EdgeSharpnessScale-max

244 2D-MultiSlice-EdgeSharpnessScale-var

245 2D-MultiSlice-EdgeSharpnessScale-skewness

246 2D-MultiSlice-EdgeSharpnessScale-kurtosis

247 2D-MultiSlice-EdgeSharpnessScale-madmean

248 2D-MultiSlice-EdgeSharpnessScale-madmedian

249 2D-MultiSlice-EdgeSharpnessScale-iqr

250 2D-MultiSlice-EdgeSharpnessScale-mode

251 2D-MultiSlice-EdgeSharpnessScale-range

252 2D-MultiSlice-EdgeSharpnessScale-trimmedmean25perc

253 2D-MultiSlice-EdgeSharpnessScale-harmmean

254 2D-MultiSlice-EdgeSharpnessScale-robustmin

255 2D-MultiSlice-EdgeSharpnessScale-robustmax

256 2D-MultiSlice-EdgeSharpnessScale-robustskewness

257 2D-MultiSlice-EdgeSharpnessScale-robustkurtosis

258 2D-MultiSlice-EdgeSharpnessScale-entropy

259 2D-MultiSlice-EdgeSharpnessBlurryness-min

260 2D-MultiSlice-EdgeSharpnessBlurryness-mean

261 2D-MultiSlice-EdgeSharpnessBlurryness-median

262 2D-MultiSlice-EdgeSharpnessBlurryness-max

263 2D-MultiSlice-EdgeSharpnessBlurryness-var

264 2D-MultiSlice-EdgeSharpnessBlurryness-skewness

265 2D-MultiSlice-EdgeSharpnessBlurryness-kurtosis

266 2D-MultiSlice-EdgeSharpnessBlurryness-madmean

267 2D-MultiSlice-EdgeSharpnessBlurryness-madmedian

268 2D-MultiSlice-EdgeSharpnessBlurryness-iqr

269 2D-MultiSlice-EdgeSharpnessBlurryness-mode

270 2D-MultiSlice-EdgeSharpnessBlurryness-range

271 2D-MultiSlice-EdgeSharpnessBlurryness-

trimmedmean25perc

272 2D-MultiSlice-EdgeSharpnessBlurryness-harmmean

273 2D-MultiSlice-EdgeSharpnessBlurryness-robustmin

274 2D-MultiSlice-EdgeSharpnessBlurryness-robustmax

275 2D-MultiSlice-EdgeSharpnessBlurryness-robustskewness

276 2D-MultiSlice-EdgeSharpnessBlurryness-robustkurtosis

277 2D-MultiSlice-EdgeSharpnessBlurryness-entropy

278 2D-MultiSlice-RDS-min

279 2D-MultiSlice-RDS-mean

280 2D-MultiSlice-RDS-median

281 2D-MultiSlice-RDS-var

282 2D-MultiSlice-RDS-skewness

283 2D-MultiSlice-RDS-kurtosis

284 2D-MultiSlice-RDS-madmean

285 2D-MultiSlice-RDS-madmedian

286 2D-MultiSlice-RDS-iqr

287 2D-MultiSlice-RDS-range

288 2D-MultiSlice-RDS-trimmedmean25perc

289 2D-MultiSlice-RDS-harmmean

290 2D-MultiSlice-RDS-robustmin

291 2D-MultiSlice-RDS-robustskewness

292 2D-MultiSlice-RDS-robustkurtosis

293 2D-MultiSlice-RDS-entropy

294 2D-MultiSlice-LAII2R10-min

295 2D-MultiSlice-LAII2R10-mean

296 2D-MultiSlice-LAII2R10-median

297 2D-MultiSlice-LAII2R10-max

298 2D-MultiSlice-LAII2R10-var

299 2D-MultiSlice-LAII2R10-skewness

300 2D-MultiSlice-LAII2R10-kurtosis

301 2D-MultiSlice-LAII2R10-madmean

302 2D-MultiSlice-LAII2R10-madmedian

303 2D-MultiSlice-LAII2R10-iqr

304 2D-MultiSlice-LAII2R10-mode

305 2D-MultiSlice-LAII2R10-range

306 2D-MultiSlice-LAII2R10-trimmedmean25perc

307 2D-MultiSlice-LAII2R10-harmmean

308 2D-MultiSlice-LAII2R10-robustmin

309 2D-MultiSlice-LAII2R10-robustmax

310 2D-MultiSlice-LAII2R10-robustskewness

311 2D-MultiSlice-LAII2R10-robustkurtosis

312 2D-MultiSlice-LAII2R10-entropy



































Table S4. Two-dimensional and multislice 2D quantitative MR image features

significantly associated with each cluster. For each feature the fold change is reported and

defined as the number of times the feature is “expressed” in samples in the cluster versus in

samples not in the cluster. For example, a 3.1-fold increase was observed for the top feature

in Cluster 1 compared with patients not in Cluster 1. The features are ranked by descending

order of fold changes.

Cluster 1 Image feature Fold

change

2D-MultiSlice-LAII2R3-iqr 3.10

2D-MultiSlice-LAII2R3-madmedian 3.09

2D-LAII2R5-madmean 3.08




2D-LAII2R5-iqr 2.96

2D-LAII2R3-iqr 2.96

2D-MultiSlice-LAII2R8-robustmax 2.96


2D-LAII2R3-madmedian 2.95

2D-LAII2R2-var 2.93

2D-MultiSlice-LAII2R5-madmean 2.93







2D-RDS-range 2.86


2D-RDS-var 2.83





2D-MultiSlice-LAII2R5-var 2.75

2D-LAII2R3-range 2.74

2D-MultiSlice-RDS-madmean 2.74

2D-LAII2R3-var 2.74

2D-RDS-madmean 2.73

2D-LAII2R5-var 2.69

2D-MultiSlice-RDS-iqr 2.68



2D-LAII2R8-var 2.66

2D-MultiSlice-RDS-var 2.66

2D-LAII2R3-robustmax 2.66


2D-MultiSlice-RDS-madmedian 2.65



2D-LAII2R2-iqr 2.63







2D-RDS-iqr 2.56


2D-LAII2R10-var 2.54




2D-LAII2R8-iqr 2.49

2D-RDS-madmedian 2.46



2D-roughness2dmean 2.35

2D-LAII2R3-max 2.32

2D-MultiSlice-roughness2dmean 2.27


2D-LAII2R10-iqr 2.27

2D-LAII2R5-max 2.15

2D-MultiSlice-LAII2R10-entropy 2.15


2D-LAII2R8-max 2.11


2D-LAII2R10-max 2.09





2D-MultiSlice-roughness2dvar 1.91

2D-MultiSlice-RDS-entropy 1.90

2D-MultiSlice-compactness2dvar 1.90

2D-MultiSlice-LAII2R5-range 1.84

2D-MultiSlice-LAII2R3-robustskewness 1.84


2D-MultiSlice-HistogramRaw-robustkurtosis 1.70


2D-MultiSlice-RDS-skewness 1.65




2D-MultiSlice-RDS-range 1.60

2D-MultiSlice-RDS-robustskewness 1.58

2D-MultiSlice-LAII2R5-max 1.55

2D-LAII2R2-entropy 1.52

2D-LAII2R2-max 1.49

2D-MultiSlice-HistogramRaw-kurtosis 1.48

2D-EdgeSharpnessWindow-robustskewness 1.48


2D-MultiSlice-EdgeSharpnessWindow-robustskewness 1.47

2D-LAII2R3-robustskewness 1.47

2D-MultiSlice-EdgeSharpnessBlurryness-

robustskewness

1.45

2D-compactness2dmean 0.33

2D-MultiSlice-RDS-mean 0.34

2D-MultiSlice-RDS-trimmedmean25perc 0.35

2D-RDS-robustmin 0.35

2D-RDS-min 0.35

2D-RDS-harmmean 0.36

2D-MultiSlice-RDS-harmmean 0.36

2D-MultiSlice-RDS-median 0.36

2D-RDS-mean 0.37

2D-MultiSlice-LAII2R2-median 0.38

2D-MultiSlice-compactness2dmean 0.38

2D-MultiSlice-LAII2R2-trimmedmean25perc 0.38

2D-LAII2R2-median 0.38

2D-LAII2R2-robustmin 0.38

2D-RDS-trimmedmean25perc 0.38

2D-LAII2R2-min 0.39

2D-LAII2R2-harmmean 0.39



2D-LAII2R2-trimmedmean25perc 0.40

2D-MultiSlice-LAII2R2-mean 0.40


2D-LAII2R5-min 0.40

2D-RDS-median 0.40


2D-LAII2R3-min 0.40





2D-LAII2R8-min 0.41

2D-MultiSlice-LAII2R5-robustmin 0.43

2D-LAII2R2-mean 0.43


2D-MultiSlice-LAII2R3-harmmean 0.43

2D-LAII2R10-min 0.43

2D-spherecity 0.43







2D-MultiSlice-spherecity 0.47


2D-MultiSlice-RDS-robustmin 0.48

2D-MultiSlice-LAII2R2-robustkurtosis 0.50







2D-MultiSlice-RDS-robustkurtosis 0.54

2D-MultiSlice-LAII2R3-min 0.54

2D-MultiSlice-LAII2R2-kurtosis 0.54


2D-MultiSlice-LAII2R2-mode 0.56

2D-MultiSlice-RDS-kurtosis 0.57

2D-LAII2R3-mode 0.58

2D-LAII2R2-mode 0.59







2D-HistogramRaw-robustskewness 0.61

2D-MultiSlice-HistogramRaw-robustskewness 0.61


2D-MultiSlice-LAII2R3-mode 0.62

2D-MultiSlice-RDS-min 0.63

2D-MultiSlice-HistogramRaw-skewness 0.63

2D-HistogramRaw-skewness 0.63



change

2D-MultiSlice-compactness2dmean 2.32


2D-MultiSlice-RDS-robustmin 2.17


2D-MultiSlice-RDS-harmmean 2.16

2D-compactness2dmean 2.16




2D-MultiSlice-RDS-mean 2.05

2D-RDS-min 2.04

2D-RDS-robustmin 2.03

2D-LAII2R5-min 1.98

2D-LAII2R8-min 1.98

2D-LAII2R3-min 1.98




2D-MultiSlice-RDS-trimmedmean25perc 1.94

2D-LAII2R2-min 1.93



2D-RDS-harmmean 1.92

2D-RDS-mean 1.91

2D-RDS-trimmedmean25perc 1.87

2D-MultiSlice-RDS-median 1.86

2D-RDS-median 1.84



2D-LAII2R10-min 1.82








2D-MultiSlice-EdgeSharpnessBlurryness-entropy 1.74





2D-MultiSlice-EdgeSharpnessWindow-min 1.65

2D-MultiSlice-EdgeSharpnessWindow-mode 1.65

2D-MultiSlice-EdgeSharpnessWindow-robustmin 1.65


2D-MultiSlice-EdgeSharpnessScale-robustmin 1.63


2D-MultiSlice-EdgeSharpnessScale-min 1.61

2D-MultiSlice-EdgeSharpnessScale-mode 1.61



2D-MultiSlice-EdgeSharpnessBlurryness-robustmin 1.58

2D-MultiSlice-spherecity 1.57


2D-EdgeSharpnessWindow-min 1.55

2D-EdgeSharpnessWindow-mode 1.55

2D-EdgeSharpnessWindow-robustmin 1.55









2D-LAII2R8-max 0.46





2D-MultiSlice-RDS-var 0.46


2D-LAII2R10-max 0.46


2D-MultiSlice-RDS-madmean 0.47

2D-MultiSlice-roughness2dmean 0.47

2D-LAII2R5-max 0.47







2D-RDS-range 0.49





2D-MultiSlice-compactness2dvar 0.49







2D-LAII2R2-var 0.51


2D-MultiSlice-RDS-iqr 0.52

2D-LAII2R5-var 0.52


2D-LAII2R3-max 0.52




2D-MultiSlice-RDS-madmedian 0.53

2D-LAII2R8-var 0.53

2D-RDS-madmean 0.53

2D-RDS-var 0.53

2D-surfaceArea 0.53

2D-LAII2R3-var 0.53







2D-LAII2R10-kurtosis 0.55

2D-LAII2R3-iqr 0.55




2D-LAII2R10-var 0.56


2D-MultiSlice-roughness2dvar 0.57



2D-roughness2dmean 0.57


2D-LAII2R5-iqr 0.57

2D-RDS-iqr 0.57

2D-MultiSlice-surfaceArea 0.58

2D-RDS-madmedian 0.58


2D-LAII2R2-iqr 0.58



2D-EdgeSharpnessScale-madmedian 0.59

2D-LAII2R10-robustkurtosis 0.59



2D-volume 0.60

2D-MultiSlice-EdgeSharpnessScale-range 0.60

2D-EdgeSharpnessScale-range 0.60


2D-EdgeSharpnessScale-iqr 0.61

2D-EdgeSharpnessScale-madmean 0.61

2D-MultiSlice-EdgeSharpnessScale-madmean 0.61


2D-MultiSlice-volume 0.62

2D-MultiSlice-EdgeSharpnessScale-iqr 0.62

2D-MultiSlice-EdgeSharpnessScale-madmedian 0.62

2D-MultiSlice-EdgeSharpnessScale-var 0.62

2D-EdgeSharpnessScale-var 0.62

2D-LAII2R8-iqr 0.63




2D-MultiSlice-EdgeSharpnessBlurryness-madmean 0.65

2D-MultiSlice-HistogramRaw-range 0.66

2D-LAII2R10-iqr 0.66



2D-HistogramRaw-range 0.68

2D-MultiSlice-EdgeSharpnessBlurryness-range 0.69

2D-MultiSlice-EdgeSharpnessBlurryness-robustkurtosis 0.70


2D-LAII2R2-max 0.70


2D-MultiSlice-EdgeSharpnessBlurryness-iqr 0.71


2D-MultiSlice-EdgeSharpnessWindow-range 0.72

2D-MultiSlice-HistogramRaw-max 0.72

2D-MultiSlice-EdgeSharpnessScale-robustmax 0.72

2D-MultiSlice-EdgeSharpnessBlurryness-max 0.72

2D-EdgeSharpnessWindow-range 0.72

2D-MultiSlice-EdgeSharpnessBlurryness-kurtosis 0.73

2D-HistogramRaw-max 0.73

2D-MultiSlice-EdgeSharpnessBlurryness-madmedian 0.73


2D-MultiSlice-EdgeSharpnessScale-max 0.73

2D-EdgeSharpnessScale-max 0.74


change

2D-EdgeSharpnessScale-madmedian 2.58

2D-EdgeSharpnessScale-iqr 2.44

2D-EdgeSharpnessScale-range 2.42

2D-EdgeSharpnessScale-madmean 2.40

2D-EdgeSharpnessScale-var 2.27

2D-MultiSlice-EdgeSharpnessScale-madmean 2.22

2D-MultiSlice-HistogramRaw-robustskewness 2.17

2D-MultiSlice-EdgeSharpnessScale-madmedian 2.17

2D-MultiSlice-EdgeSharpnessScale-range 2.16

2D-MultiSlice-EdgeSharpnessScale-var 2.15

2D-MultiSlice-EdgeSharpnessScale-iqr 2.14

2D-spherecity 2.14

2D-HistogramRaw-robustskewness 2.09

2D-MultiSlice-HistogramRaw-skewness 2.06

2D-HistogramRaw-skewness 2.05

2D-volume 1.98

2D-MultiSlice-volume 1.95

2D-MultiSlice-EdgeSharpnessBlurryness-madmean 1.93


2D-MultiSlice-HistogramRaw-madmean 1.90


2D-HistogramRaw-range 1.89

2D-MultiSlice-LowIntensityProportion 1.89

2D-MultiSlice-EdgeSharpnessBlurryness-robustkurtosis 1.88

2D-MultiSlice-HistogramRaw-iqr 1.87

2D-MultiSlice-HistogramRaw-range 1.86

2D-LowIntensityProportion 1.85

2D-HistogramRaw-madmean 1.83

2D-MultiSlice-RDS-robustkurtosis 1.83


2D-MultiSlice-EdgeSharpnessBlurryness-range 1.81



2D-MultiSlice-surfaceArea 1.80

2D-MultiSlice-HistogramRaw-var 1.80



2D-HistogramRaw-var 1.77





2D-HistogramRaw-max 1.75

2D-MultiSlice-HistogramRaw-max 1.74


2D-MultiSlice-EdgeSharpnessWindow-range 1.71



2D-HistogramRaw-iqr 1.69

2D-MultiSlice-RDS-kurtosis 1.69

2D-MultiSlice-EdgeSharpnessBlurryness-max 1.67

2D-MultiSlice-HistogramRaw-madmedian 1.66


2D-MultiSlice-HistogramRaw-robustmax 1.65

2D-MultiSlice-EdgeSharpnessWindow-robustkurtosis 1.64



2D-MultiSlice-EdgeSharpnessBlurryness-madmedian 1.64

2D-MultiSlice-EdgeSharpnessBlurryness-iqr 1.64

2D-surfaceArea 1.64



2D-MultiSlice-EdgeSharpnessBlurryness-var 1.62

2D-HistogramRaw-robustmax 1.61


2D-MultiSlice-EdgeSharpnessScale-robustmax 1.61


2D-MultiSlice-EdgeSharpnessScale-harmmean 1.60

2D-EdgeSharpnessScale-robustmax 1.58

2D-MultiSlice-EdgeSharpnessBlurryness-kurtosis 1.57




2D-EdgeSharpnessBlurryness-robustkurtosis 1.55

2D-EdgeSharpnessBlurryness-kurtosis 1.54


2D-EdgeSharpnessWindow-robustkurtosis 1.53

2D-EdgeSharpnessScale-max 1.53

2D-MultiSlice-EdgeSharpnessBlurryness-robustmax 1.51

2D-MultiSlice-EdgeSharpnessScale-max 1.51











2D-MultiSlice-EdgeSharpnessWindow-min 0.41

2D-MultiSlice-EdgeSharpnessWindow-mode 0.41

2D-MultiSlice-EdgeSharpnessWindow-robustmin 0.41

2D-EdgeSharpnessWindow-min 0.42

2D-EdgeSharpnessWindow-mode 0.42

2D-EdgeSharpnessWindow-robustmin 0.42

2D-MultiSlice-EdgeSharpnessScale-robustmin 0.43

2D-MultiSlice-EdgeSharpnessScale-min 0.44

2D-MultiSlice-EdgeSharpnessScale-mode 0.44

2D-EdgeSharpnessScale-robustmin 0.48

2D-MultiSlice-EdgeSharpnessBlurryness-robustmin 0.48

2D-MultiSlice-EdgeSharpnessWindow-robustskewness 0.48

2D-EdgeSharpnessScale-min 0.48

2D-EdgeSharpnessScale-mode 0.48



2D-EdgeSharpnessBlurryness-entropy 0.51



2D-MultiSlice-EdgeSharpnessBlurryness-entropy 0.53


2D-MultiSlice-RDS-entropy 0.53




2D-MultiSlice-EdgeSharpnessBlurryness-min 0.56

2D-MultiSlice-EdgeSharpnessBlurryness-mode 0.56

2D-MultiSlice-RDS-skewness 0.56

2D-MultiSlice-RDS-robustskewness 0.56

2D-MultiSlice-EdgeSharpnessBlurryness-

robustskewness

0.57

2D-EdgeSharpnessWindow-entropy 0.58

2D-MultiSlice-EdgeSharpnessWindow-skewness 0.60




2D-LAII2R5-iqr 0.64

Table S5. Cluster assignment by subjects in the development cohort with and without

midline-crossing lesions. With midline-crossing, n = 140; without midline crossing, n =

121.

With midline-

crossing

lesions Cluster

Without midline-

crossing lesions Cluster

Patient_033 1 Patient_033 1

























Patient_128 2

Patient_130 3

Patient_152 3

Patient_165 3

Patient_231 1

Patient_234 3

Patient_241 3

Patient_257 2

Patient_270 2

Patient_271 2

Patient_285 3

Patient_291 1

Patient_293 3

Patient_302 2

Patient_311 1

Patient_312 3

Patient_342 2

Patient_343 2

Patient_379 2

Table S6. Regulatory signaling pathways significantly associated with each cluster.

Each cluster is associated with up-regulated (≥ 1.0 fold change) or down-regulated (< 1.0

fold change) signaling pathways as determined by individual gene expression and copy

number variation data (CNV) integrated using the Pathway Recognition Algorithm Using

Data Integration on Genomic Models (PARADIGM). Only signaling pathways with a false

discovery rate (FDR or q-value) <5% are included. Signaling pathways are ranked in

descending order of fold changes.

Signaling pathway Fold

change

q-value

(%)

Cluster 1 Signaling events mediated by stem cell factor receptor (c-Kit) 1.033 0

Cluster 2 Insulin-mediated glucose transport 1.005 3.4

Signaling events mediated by prolactin (PRL) 0.997 4.5

Platelet-derived growth factor receptor-alpha (PDGFR-α)

signaling pathway

0.997 0

Reelin signaling pathway 0.997 4.5

Vascular endothelial growth factor receptor 1 (VEGFR1)–

specific signals

0.996 4.5

Polo-like kinase 1 (PLK1) signaling events 0.996 4.5

Regulation of nuclear mothers against decapentaplegic homolog

2/3 (SMAD2/3) signaling

0.995 4.5

Signaling events mediated by protein-tyrosine phosphatase 1B

(PTP1B)

0.995 4.5

Osteopontin-mediated events 0.995 4.5

Signaling events activated by hepatocyte growth factor receptor

(c-Met)

0.994 4.5

Alpha-M beta-2 (αMβ2) Integrin signaling 0.994 4.5

Syndecan-1–mediated signaling events 0.993 4.5

Erythropoietin (EPO) signaling pathway 0.993 4.5

Endothelins 0.991 0

Forkhead box protein A2 (FOXA2) and FOXA3 transcription

factor networks

0.991 0

Signaling events mediated by vascular endothelial growth factor

receptor 1 (VEGFR1) and VEGFR2

0.991 0

Interleukin 6 (IL6)–mediated signaling events 0.989 0

Angiopoietin receptor Tie2-mediated signaling 0.988 0

IL23-mediated signaling events 0.981 0

Signaling events mediated by c-Kit 0.976 0

Forkhead box protein M1 (FOXM1) transcription factor

network

0.965 0

Cluster 3 FOXA2 and FOXA3 transcription factor networks 1.013 0

PDGFR-β signaling pathway 1.013 0

Endothelins 1.012 0

Thromboxane A2 receptor signaling 1.012 0

Syndecan-4–mediated signaling events 1.012 3.62

Syndecan-1–mediated signaling events 1.011 0

IL6–mediated signaling events 1.010 3.62

Osteopontin-mediated events 1.010 0

Fc-ε receptor I signaling in mast cells 1.009 0

αMβ2 Integrin signaling 1.009 0

Angiopoietin receptor Tie2-mediated signaling 1.009 3.62

PTP1B 1.008 0

B cell receptor (BCR) signaling pathway 1.007 3.62

Retinoic acid receptor and retinoid X receptor (RXR and RAR)

heterodimerization with other nuclear receptor

1.006 0

Atypical nuclear factor-κ B pathway 1.005 0

PLK1 signaling events 1.005 0

Ceramide signaling pathway 1.005 0

Noncanonical Wnt signaling pathway 1.005 3.62

SMAD2/3 signaling 1.005 3.62

T cell receptor (TCR) signaling in naïve CD8+ T cells 1.005 3.62

IL1–mediated signaling events 1.005 0

Calcium signaling in the CD4+ TCR pathway 1.005 0

Ras signaling in the CD4+ TCR pathway 1.005 3.62

Nongenotropic androgen signaling 1.004 0

Regulation of p38-α and p38-β 1.004 3.62

VEGFR1–specific signals 1.004 0

E-cadherin signaling in the nascent adherens junction 1.004 3.62

PRL 1.004 0

PDGFR-α signaling pathway 1.004 0

IL27–mediated signaling events 1.003 3.62

Canonical Wnt signaling pathway 1.003 3.62

supplementary materials for · curated pathways from the nci pathway interaction database using the...

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