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Automated Radiomic Features Complement the Prognostic Value of VASARI in the TCGA-GBM Dataset

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E Rios Velazquez

E Rios Velazquez1*, V Narayan2 , P Grossmann3 , W Dunn4 , D Gutman5 , H Aerts6 , (1) Dana-Farber Cancer Institute | Harvard Medical School, Boston, MA, (2) Dana-Farber Cancer Institute, Brigham and Womens Hospital, Harvard Medic, Boston, MA, (3) Dana-Farber Cancer Institute/Harvard Medical School, Boston, MA, (4) Emory University School of Medicine, Atlanta, GA, (5) Emory University School of Medicine, Atlanta, GA, (6) Dana-Farber/Brigham Womens Cancer Center, Boston, MA


TU-CD-BRB-4 (Tuesday, July 14, 2015) 10:15 AM - 12:15 PM Room: Ballroom B

Purpose: To compare the complementary prognostic value of automated Radiomic features to that of radiologist-annotated VASARI features in TCGA-GBM MRI dataset.

Methods: For 96 GBM patients, pre-operative MRI images were obtained from The Cancer Imaging Archive. The abnormal tumor bulks were manually defined on post-contrast T1w images. The contrast-enhancing and necrotic regions were segmented using FAST. From these sub-volumes and the total abnormal tumor bulk, a set of Radiomic features quantifying phenotypic differences based on the tumor intensity, shape and texture, were extracted from the post-contrast T1w images.
Minimum-redundancy-maximum-relevance (MRMR) was used to identify the most informative Radiomic, VASARI and combined Radiomic-VASARI features in 70% of the dataset (training-set). Multivariate Cox-proportional hazards models were evaluated in 30% of the dataset (validation-set) using the C-index for OS. A bootstrap procedure was used to assess significance while comparing the C-Indices of the different models.

Results: Overall, the Radiomic features showed a moderate correlation with the radiologist-annotated VASARI features (r = -0.37 – 0.49); however that correlation was stronger for the Tumor Diameter and Proportion of Necrosis VASARI features (r = -0.71 – 0.69). After MRMR feature selection, the best-performing Radiomic, VASARI, and Radiomic-VASARI Cox-PH models showed a validation C-index of 0.56 (p = NS), 0.58 (p = NS) and 0.65 (p = 0.01), respectively. The combined Radiomic-VASARI model C-index was significantly higher than that obtained from either the Radiomic or VASARI model alone (p = <0.001).

Conclusion: Quantitative volumetric and textural Radiomic features complement the qualitative and semi-quantitative annotated VASARI feature set. The prognostic value of informative qualitative VASARI features such as Eloquent Brain and Multifocality is increased with the addition of quantitative volumetric and textural features from the contrast-enhancing and necrotic tumor regions. These results should be further evaluated in larger validation cohorts.

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