Stable and Discriminatory Radiomic Features from the Tumor and Its Habitat Associated with Progression-Free Survival in Glioblastoma: A Multi-Institutional Study

• Type of Material: Article
• Authors: Ruchika Verma, Case Western Reserve University; VB Hill, Northwestern University; V Statsevych, Cleveland Clinic; K Bera, Case Western Reserve University; R Correa, Case Western Reserve University; P Leo, Case Western Reserve University; M Ahluwalia, Miami Cancer Institute; Anant Madabhushi, Emory University; P Tiwari, University of Wisconsin Madison
• Language: English
• Date: 2022-08-01
• Publisher: AMER SOC NEURORADIOLOGY
• Publication Version: Final Published Version
• Copyright Statement: © 2022 by American Journal of Neuroradiology
• Final Published Version (URL): http://dx.doi.org/10.3174/ajnr.A7591
• Title of Journal or Parent Work: AMERICAN JOURNAL OF NEURORADIOLOGY
• Volume: 43
• Issue: 8
• Start Page: 1115
• End Page: 1123
• Grant/Funding Information: Training Grant; the Neptune Career Development Award; the Ohio Third Frontier Technology Validation Start-up Fund; the Wallace H. Coulter Foundation Program in the Department of Biomedical Engineering and the Clinical and Translational Science Award Program at Case Western Reserve University (NCI 1U01CA248226-01); the Department of Defense Peer Reviewed Cancer Research Program (W81XWH-18-1-0404); the Dana Foundation David Mahoney Neuroimaging Program; and the V Foundation Translational Research Award.; Research reported in this publication was supported by the National Cancer Institute of the National Institutes of Health under award Nos. 1U24CA199374-01, R01CA202752-01A1, R01CA208236-01A1, R01 CA216579-01A1, R01 CA220581-01A1, R01CA264017, 1U01 CA239055-01, 1U01CA248226-01; the National Institute for Biomedical Imaging and Bioengineering (1R43EB028736-01); the National Center for Research Resources under award No. 1 C06 RR12463-01; the VA Merit Review Award (IBX004121A) from the US Department of Veterans Affairs Biomedical Laboratory Research and Development Service; the Department of Defense Breast Cancer Research Program Breakthrough Level 1 Award No. W81XWH-19-1-0668; the Department of Defense Prostate Cancer Idea Development Award (W81XWH-15-1-0558); the Department of Defense Lung Cancer Investigator-Initiated Translational Research Award (W81XWH-18-1-0440); the Department of Defense Peer Reviewed Cancer Research Program (W81XWH-16-1-0329); the Kidney Precision Medicine Project Glue Grant (5T32DK747033); the Case Western Reserve University Nephrology
• Keywords: IMPACT; INFORMATION; Radiology, Nuclear Medicine & Medical Imaging; Life Sciences & Biomedicine; Clinical Neurology; Neurosciences & Neurology; Science & Technology; REPRODUCIBILITY; Neuroimaging
• Research Categories: Health Sciences, Radiology; Biology, Neuroscience

Abstract

BACKGROUND AND PURPOSE: Glioblastoma is an aggressive brain tumor, with no validated prognostic biomarkers for survival before surgical resection. Although recent approaches have demonstrated the prognostic ability of tumor habitat (constituting necrotic core, enhancing lesion, T2/FLAIR hyperintensity subcompartments) derived radiomic features for glioblastoma survival on treatment-naive MR imaging scans, radiomic features are known to be sensitive to MR imaging acquisitions across sites and scanners. In this study, we sought to identify the radiomic features that are both stable across sites and discriminatory of poor and improved progression-free survival in glioblastoma tumors. MATERIALS AND METHODS: We used 150 treatment-naive glioblastoma MR imaging scans (Gadolinium-T1w, T2w, FLAIR) obtained from 5 sites. For every tumor subcompartment (enhancing tumor, peritumoral FLAIR-hyperintensities, necrosis), a total of 316 three-dimensional radiomic features were extracted. The training cohort constituted studies from 4 sites (n = 93) to select the most stable and discriminatory radiomic features for every tumor subcompartment. These features were used on a hold-out cohort (n = 57) to evaluate their ability to discriminate patients with poor survival from those with improved survival. RESULTS: Incorporating the most stable and discriminatory features within a linear discriminant analysis classifier yielded areas under the curve of 0.71, 0.73, and 0.76 on the test set for distinguishing poor and improved survival compared with discriminatory features alone (areas under the curve of 0.65, 0.54, 0.62) from the necrotic core, enhancing tumor, and peritumoral T2/FLAIR hyperintensity, respectively. CONCLUSIONS: Incorporating stable and discriminatory radiomic features extracted from tumors and associated habitats across multisite MR imaging sequences may yield robust prognostic classifiers of patient survival in glioblastoma tumors.