Modeling variable interactions using Bayesian networks to identify direct predictors of radiation-induced optic neuropathy after proton therapy

INTRODUCTION: Radiation-induced optic neuropathy (RION) is a rare but functionally-disabling complication of radiotherapy for brain and head/neck tumors. Even with modern proton therapy and adherence to dose constraints, about 6% of patients develop near-blindness from RION. Unlike regression models that estimate addictive effects, a Bayesian network (BN) explicitly encodes conditional dependencies among input variables, and was used for more reliable causality assessment and individualized outcome prediction of RION. METHODS: Demographic, ophthalmological and treatment data were prospectively collected from patients undergoing scanned proton beams between 2018 and 2024. Visual field deficit served as surrogate for grade ≥2 clinically-relevant RION. Using clinical and treatment/dosimetry variables, under expert-informed constraints, BN with Markov-blanket identified direct RION predictors; its explanatory value was evaluated via log-likelihood ratio (LLR), integrated discrimination improvement (IDI), and net reclassification index (NRI). RESULTS: Of 179 eyes (105 patients), 24% developed RION. The BN identified 15 directional associations, 12 were clinically valid, 2 conceivable and 1 weak. The Markov-blanket of the RION node identified baseline visual field deficit, hypertension, and hypercholesterolemia as key predictors. A logistic regression model based on these variables improved patient stratification (LLR=63.34, p < 0.001, IDI=0.35, NRI=0.25); AUC reached 0.75 (95%CI: 0.66-0.84). Age and dosimetric factors did not improve RION risk prediction. CONCLUSION: BN analysis highlighted associations between vascular comorbidities and baseline visual functionand occurrence of RION, while dosimetric factors were not direct predictors. This contrast with photon models is hypothesis-generating and favours proton-specific models for RION prediction.