Radiogenomic-Based Survival Risk Stratification of Tumor Habitat on Gd-T1w MRI Is Associated with Biological Processes in Glioblastoma
| Autor: | Anant Madabhushi, Ruchika Verma, Nathaniel Braman, Salendra Singh, Manmeet Ahluwalia, Vinay Varadan, Pallavi Tiwari, Anas Saeed Bamashmos, Niha Beig, Marwa Ismail, Jacob Antunes, Virginia Hill, Volodymyr Statsevych, Kaustav Bera, Ramon Correa, Prateek Prasanna |
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| Rok vydání: | 2020 |
| Předmět: |
Adult
Male 0301 basic medicine Oncology Cancer Research medicine.medical_specialty Necrotic core Risk Assessment Article Young Adult 03 medical and health sciences 0302 clinical medicine Text mining Internal medicine Image Interpretation Computer-Assisted Biomarkers Tumor medicine Humans Aged Aged 80 and over Framingham Risk Score Brain Neoplasms Gene ontology Proportional hazards model business.industry Middle Aged Prognosis medicine.disease Magnetic Resonance Imaging Gene Expression Regulation Neoplastic Survival Rate 030104 developmental biology 030220 oncology & carcinogenesis Mutation Cohort Risk stratification Female Glioblastoma business Signal Transduction |
| Zdroj: | Clin Cancer Res |
| ISSN: | 1557-3265 1078-0432 |
| DOI: | 10.1158/1078-0432.ccr-19-2556 |
| Popis: | Purpose: To (i) create a survival risk score using radiomic features from the tumor habitat on routine MRI to predict progression-free survival (PFS) in glioblastoma and (ii) obtain a biological basis for these prognostic radiomic features, by studying their radiogenomic associations with molecular signaling pathways. Experimental Design: Two hundred three patients with pretreatment Gd-T1w, T2w, T2w-FLAIR MRI were obtained from 3 cohorts: The Cancer Imaging Archive (TCIA; n = 130), Ivy GAP (n = 32), and Cleveland Clinic (n = 41). Gene-expression profiles of corresponding patients were obtained for TCIA cohort. For every study, following expert segmentation of tumor subcompartments (necrotic core, enhancing tumor, peritumoral edema), 936 3D radiomic features were extracted from each subcompartment across all MRI protocols. Using Cox regression model, radiomic risk score (RRS) was developed for every protocol to predict PFS on the training cohort (n = 130) and evaluated on the holdout cohort (n = 73). Further, Gene Ontology and single-sample gene set enrichment analysis were used to identify specific molecular signaling pathway networks associated with RRS features. Results: Twenty-five radiomic features from the tumor habitat yielded the RRS. A combination of RRS with clinical (age and gender) and molecular features (MGMT and IDH status) resulted in a concordance index of 0.81 (P < 0.0001) on training and 0.84 (P = 0.03) on the test set. Radiogenomic analysis revealed associations of RRS features with signaling pathways for cell differentiation, cell adhesion, and angiogenesis, which contribute to chemoresistance in GBM. Conclusions: Our findings suggest that prognostic radiomic features from routine Gd-T1w MRI may also be significantly associated with key biological processes that affect response to chemotherapy in GBM. |
| Databáze: | OpenAIRE |
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