Personalized machine learning-guided radiation dose escalation in newly diagnosed glioblastoma: prospective pilot study

Abstract

Glioblastoma is a highly aggressive primary brain tumor with near-universal recurrence despite maximal safe resection followed by standard chemoradiation. We conducted a prospective pilot study (ClinicalTrials.gov identifier: NCT03477513) with predefined endpoints and structured dose-escalation criteria to evaluate the feasibility and safety of personalized precision radiation therapy (PPRT) guided by machine learning (ML)–based maps of tumor infiltration. Twenty patients with newly diagnosed IDH-wildtype glioblastoma who underwent gross total resection received PPRT with concomitant and adjuvant temozolomide. The primary outcomes were median progression-free survival (PFS) and safety. Secondary outcomes included patterns of recurrence, rate of clinically significant toxicity, and median overall survival (OS). PPRT was feasible and well tolerated, with no grade ≥3 acute adverse events; 47% experienced grade 1 and 53% grade 2 events. Following therapy, radiation necrosis occurred in 47% of PPRT-temozolomide patients versus 12% in standard-of-care patients (p < 0.001). Median PFS was 24.4 months versus 11.6 months in 68 propensity score matched (PSM) historical control group (HR 0.28, 95% CI 0.13–0.61; p = 0.001). Median OS was 35.4 versus 17.7 months (HR 0.34, 95% CI 0.17–0.69; p = 0.003). Exploratory post hoc comparison suggests improved survival, but these findings are preliminary and require validation in randomized trials. As radiotherapy techniques improve so does the ability to more precisely target glioblastoma while sparing neighboring healthy tissue. Here, the authors present a prospective pilot study evaluating the feasibility of personalized precision radiation guided by machine-learning maps of estimated tumor infiltration and predicted recurrence in patients with newly diagnosed glioblastoma.