DE-ESCALATING INTENSITY AND PRESERVING OUTCOMES: A BAYESIAN-ML NETWORK META-ANALYSIS OF MULTIMODAL TREATMENT STRATEGIES IN HPV-POSITIVE OROPHARYNGEAL CANCER
Muhammad Allam Rafi*
The study presents the first integrative Bayesian-machine learning (ML) network meta-analysis, enhanced by machine learning algorithms, to evaluate and rank de-escalation treatment strategies for HPV-positive oropharyngeal squamous cell carcinoma (OPSCC). A total of 2,298 patients from 10 multicenter studies and randomized controlled trials were included, comprising randomized controlled trials, observational studies, and phase II investigations. Twelve distinct treatment strategies were analyzed, including TORS with de-escalated adjuvant RT, reduced-dose chemoradiotherapy, adaptive radiotherapy, and immunotherapy-based regimens. The SUCRA (Surface Under the Cumulative Ranking) scores indicated TORS + de-escalated RT as the top-ranked strategy (SUCRA = 0.91), followed by reduced-dose CRT (0.88) and adaptive RT (0.84). SHAP (SHapley Additive exPlanations) analysis from a Random Forest classifier confirmed that toxicity reduction (impact = 0.34) and quality of life (QOL) improvement (0.28) were the most critical factors driving high SUCRA rankings, with overall survival (OS) rates consistently above 90% in the top three strategies. Funnel plots suggested low publication bias, while cluster heatmaps demonstrated clear stratification of treatment profiles. The t-SNE visualization validated strong feature convergence among top-performing modalities. This analysis highlights the potential of machine learning-guided evidence synthesis to enhance clinical decision-making in personalized OPSCC therapy by balancing oncologic efficacy with functional outcomes.
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