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Integration of Radiomic and Multi-omic Analyses Predicts Survival of Newly Diagnosed IDH1 Wild-Type Glioblastoma

Division of Radiation Oncology, Department of Oncology, McGill University, Montreal, QC H4A 3J1, Canada
The Laboratory for Imagery, Vision and Artificial Intelligence, École de Technologie Supérieure (ETS), Montréal, QC H3C 1K3, Canada
Department of Pathology, McGill University, Montreal, QC H4A 3J1, Canada
Research Institute of the McGill University Health Centre, Glen Site, Montreal, QC H4A 3J1, Canada
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Cancers 2019, 11(8), 1148;
Received: 15 July 2019 / Revised: 5 August 2019 / Accepted: 8 August 2019 / Published: 10 August 2019
PDF [5145 KB, uploaded 10 August 2019]


Predictors of patient outcome derived from gene methylation, mutation, or expression are severely limited in IDH1 wild-type glioblastoma (GBM). Radiomics offers an alternative insight into tumor characteristics which can provide complementary information for predictive models. The study aimed to evaluate whether predictive models which integrate radiomic, gene, and clinical (multi-omic) features together offer an increased capacity to predict patient outcome. A dataset comprising 200 IDH1 wild-type GBM patients, derived from The Cancer Imaging Archive (TCIA) (n = 71) and the McGill University Health Centre (n = 129), was used in this study. Radiomic features (n = 45) were extracted from tumor volumes then correlated to biological variables and clinical outcomes. By performing 10-fold cross-validation (n = 200) and utilizing independent training/testing datasets (n = 100/100), an integrative model was derived from multi-omic features and evaluated for predictive strength. Integrative models using a limited panel of radiomic (sum of squares variance, large zone/low gray emphasis, autocorrelation), clinical (therapy type, age), genetic (CIC, PIK3R1, FUBP1) and protein expression (p53, vimentin) yielded a maximal AUC of 78.24% (p = 2.9 × 10−5). We posit that multi-omic models using the limited set of ‘omic’ features outlined above can improve capacity to predict the outcome for IDH1 wild-type GBM patients. View Full-Text
Keywords: IDH1; radiomics; glioblastoma; survival time IDH1; radiomics; glioblastoma; survival time

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Chaddad, A.; Daniel, P.; Sabri, S.; Desrosiers, C.; Abdulkarim, B. Integration of Radiomic and Multi-omic Analyses Predicts Survival of Newly Diagnosed IDH1 Wild-Type Glioblastoma. Cancers 2019, 11, 1148.

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