Multimodal CT Radiomics–Dosiomics Fusion Predicts Local Recurrence and Survival after Low-Dose-Rate Brachytherapy for Salivary Gland Carcinoma

Background Salivary-gland carcinomas (SGCs) are histologically diverse with variable prognoses. For postoperative residual disease treated by ¹²⁵ I low-dose-rate (LDR) brachytherapy, conventional prognostic factors are insufficient for individualized risk stratification. Radiomics and dosiomics quantify tumor phenotype and three-dimensional dose heterogeneity and may offer complementary prognostic value. Methods We retrospectively analysed 263 SGC patients treated with ¹²⁵ I LDR brachytherapy (2011–2019). Radiomic features (planning CT) and dosiomic features (post-implant 3D dose maps) were extracted with PyRadiomics and filtered for redundancy. Five Cox models (clinical, DVH, radiomics, dosiomics, hybrid) were trained with 30% held-out internal validation. Temporal external validation was performed in an independent 2020 cohort (n = 74; all survivors ≥ 5-year follow-up). Performance was assessed by Harrell’s C-index, time-dependent AUCs at 1/3/5 years, integrated Brier score (IBS), decision-curve analysis (DCA), and KM risk stratification. Results For local recurrence (LR), the radiomics model was the best single-modality model internally (C-index 0.86), while the hybrid model performed best overall (C-index 0.87). For overall survival (OS), a parsimonious four-variable hybrid achieved the highest internal discrimination (C-index 0.63). In the external 2020 cohort, the hybrid model maintained out-of-sample performance: LR—C = 0.714; AUCs 0.645/0.722/0.732; IBS 0.090; OS—C = 0.859; AUCs 0.945/0.887/0.909; IBS 0.030, with positive net benefit on DCA and clear KM separation. Conclusions Radiomics captures intratumoral heterogeneity relevant to local control, while dosiomics contributes independent dose-heterogeneity information for survival. Integrating both with clinical variables yields the most accurate LR prediction and improves OS discrimination.