Predicting survival in patients with glioblastoma using MRI radiomic features extracted from radiation planning volumes

• Published in: Journal of neuro-oncology Vol. 156; no. 3; pp. 579 - 588
• Main Authors: Geraghty, Benjamin J., Dasgupta, Archya, Sandhu, Michael, Malik, Nauman, Maralani, Pejman Jabehdar, Detsky, Jay, Tseng, Chia-Lin, Soliman, Hany, Myrehaug, Sten, Husain, Zain, Perry, James, Lau, Angus, Sahgal, Arjun, Czarnota, Gregory J.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.02.2022; Springer Nature B.V
• Subjects: Biopsy; Brain cancer; Brain Neoplasms - diagnostic imaging; Brain Neoplasms - radiotherapy; Clinical Study; Glioblastoma; Glioblastoma - diagnostic imaging; Glioblastoma - radiotherapy; Humans; Image processing; Magnetic Resonance Imaging; Medicine; Medicine & Public Health; Neurology; Oncology; Patients; Predictive Value of Tests; Radiation; Radiomics; Radiotherapy, Adjuvant; Risk groups; Survival; Survival Analysis; Tumors; Magnetic resonance imaging (MRI); Glioblastoma multiforme (GBM); Radiotherapy; Radiomics
• DOI: 10.1007/s11060-021-03939-9
• ISSN: 0167-594X; 1573-7373

Background Quantitative image analysis using pre-operative magnetic resonance imaging (MRI) has been able to predict survival in patients with glioblastoma (GBM). The study explored the role of postoperative radiation (RT) planning MRI-based radiomics to predict the outcomes, with features extracted from the gross tumor volume (GTV) and clinical target volume (CTV). Methods Patients with IDH-wildtype GBM treated with adjuvant RT having MRI as a part of RT planning process were included in the study. 546 features were extracted from each GTV and CTV. A LASSO Cox model was applied, and internal validation was performed using leave-one-out cross-validation with overall survival as endpoint. Cross-validated time-dependent area under curve (AUC) was constructed to test the efficacy of the radiomics model, and clinical features were used to generate a combined model. Analysis was done for the entire group and in individual surgical groups-gross total excision (GTR), subtotal resection (STR), and biopsy. Results 235 patients were included in the study with 57, 118, and 60 in the GTR, STR, and biopsy subgroup, respectively. Using the radiomics model, binary risk groups were feasible in the entire cohort (p < 0.01) and biopsy group (p = 0.04), but not in the other two surgical groups individually. The integrated AUC (iAUC) was 0.613 for radiomics-based classification in the biopsy subgroup, which improved to 0.632 with the inclusion of clinical features. Conclusion Imaging features extracted from the GTV and CTV regions can lead to risk-stratification of GBM undergoing biopsy, while the utility in other individual subgroups needs to be further explored.