Phenotypic analysis of radio-pathomic maps in de-novo glioblastoma identifies differences in bevacizumab treatment response

Abstract PURPOSE Bevacizumab (Bev) treatment for recurrent glioblastoma ceases tumor angiogenesis, which changes tumor appearance on Gd enhanced T1-weighted imaging. However, little is known about how non-angiogenic portions of tumor are affected by Bev. Therefore, this study tested the hypothesis that patients with specific tumor appearance phenotypes outside contrast enhancement, as identified using autopsy tissue-based radio-pathomic maps, would show differences in survival benefit from Bev treatment. METHODS Previously published methods were used to create machine learning models that generate whole-brain maps of cellularity (Cell), extracellular fluid (ECF) and cytoplasm (Cyt) using glioma autopsy tissue aligned to the patient’s last MRI as ground truth (Bobholz et al. 2022). These maps were then generated for an independent dataset of glioblastoma patients (n=80) using baseline imaging acquired prior to treatment. Patients were then graded for qualitative characteristics of the non-enhancing margin, with phenotypes including Well-Circumscribed (WC) patients with no abnormal tissue surrounding the core, Hypercellular Front (HF) patients with areas of hypercellularity extending from the core, Necrotic Front (NF) patients with areas of necrosis extending from the core, and Hybrid Front (HYF) patients with both hypercellular and necrotic presence beyond the enhancing region. Kaplan Meier analysis was then used to compare survival outcomes between patients who did and did not receive Bev treatment within each phenotype to assess differences in treatment efficacy. RESULTS Only patients with NF or HYF showed significant survival increase with bevacizumab treatment (HR=2.35, p=0.02; and HR=2.45, p=0.03, respectively), with no significant or trending difference in survival observed for WC and HF patients. CONCLUSION Radio-pathomic phenotypes identify patients who show the greatest survival benefit from Bev treatment, which could be used to direct clinical decision-making.