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

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.