Causal Machine Learning Analysis of Empirical Relative Biological Effectiveness for Mandible Osteoradionecrosis in...

Osteoradionecrosis (ORN) of the mandible is one of the most severe adverse events for head and neck (H&N) cancer radiation therapy. Previous retrospective investigations on real-world data relied heavily on conventional statistical models that primarily elucidate correlation rather than establishing causal relationships. Through the novel causal machine learning method, we aim to obtain empirical relative biological effectiveness (RBE) for mandible ORN in patients with H&N cancer treated with pencil-beam-scanning proton therapy (PBSPT).

A total of 1266 patients with H&N cancer were included: 335 patients treated by PBSPT and 931 patients treated by volumetric modulated arc therapy (VMAT). We used 1:1 propensity-score case matching to minimize imbalance in clinical factors between patients treated with PBSPT and VMAT. Standardized mean differences were used to assess residual clinical-factor imbalance within the case-matched cohorts. Causal forest (CF) was adopted to investigate the causal effects between dosimetric factors and the incidence of ORN. For each modality and each prespecified dose-volume histogram (DVH) index, candidate dose-volume thresholds were evaluated systematically, and the volume threshold yielding the largest CF-estimated average treatment effect was selected as the dose-volume constraint (DVC) volume threshold. Empirical RBE values were derived from equal-volume intersections on modality-specific volume-tolerance curves after converting PBSPT Gy[RBE] values to physical dose.

A total of 335 VMAT patients were case-matched to 335 patients treated by PBSPT; however, standardized mean bias analysis revealed persistent covariate imbalances within each group, indicating residual confounding influence. Using CF modeling, we identified candidate DVC volume thresholds for mandibular ORN and found that PBSPT had lower selected DVC volume thresholds than VMAT. The threshold-stability analyses supported the robustness of the DVC thresholds emphasized in the empirical RBE analysis. The resulting empirical RBE exceeded 1.1 in the moderate dose range (1.61 at 40 Gy[RBE], 1.30 at 50 Gy[RBE], and 1.13 at 60 Gy[RBE]).

This study presents a novel application of causal machine learning to evaluate mandibular ORN in radiation therapy, identifying candidate DVC volume thresholds linked to the strongest threshold-defined causal effects and deriving empirical RBEs from equal-volume equivalent constraint dose analysis based on volume-tolerance curves. The results indicate that proton RBE may exceed 1.1 in the moderate dose range (40-60 Gy[RBE]), underscoring the importance of considering endpoint-specific variable RBE in PBSPT treatment planning. These CF-identified DVC volume thresholds should be interpreted as hypothesis-generating risk regions rather than definitive clinical cutoffs, pending independent prospective validation.