An immunophenotype-guided interpretable subregional radiomics model for predicting local recurrence-free survival in esophageal squamous cell cancer patients treated with definitive chemoradiotherapy: a multicenter study

This study aimed to demonstrate tumor immune microenvironment (TIME) subtypes as prognostic factors for local recurrence-free survival (LRFS) in esophageal squamous cell carcinoma (ESCC) patients treated with definitive chemoradiotherapy (dCRT) and to develop an interpretable, TIME-guided subregional radiomics model for predicting LRFS. 986 ESCC patients from three centers were enrolled. For a subset with available biopsies, multiplex immunofluorescence was used to assess CD3, CD8, and PD-L1 expression, defining prognostic TIME subtypes. Pre-treatment contrast-enhanced CT (CECT) images were used for tumor segmentation, intratumoral subregion partitioning and radiomic feature extraction. Features associated with TIME subtypes were selected to build a subregional radiomic signature (Subrad signature) for LRFS prediction using an advanced machine learning framework. This signature was integrated with clinical factors to construct a Clinic-Subregional radiomic model (CliSubrad-M). A “PD-L1-low/TILs-high” TIME subtype was identified as an independent favorable prognostic factor for LRFS (HR = 0.34, P < 0.001). The Subrad signature, comprising seven TIME-related features from various subregions, demonstrated robust predictive performance (C-indexes: 0.631–0.637). The integrated CliSubrad-M (built with Random Survival Forest) achieved the best performance, with C-indexes of 0.702 (training), 0.679 (internal testing), 0.653 (external testing1), and 0.625 (external testing2), significantly outperforming the clinical model (all P < 0.05). The model effectively stratified patients into distinct risk groups with significant differences in LRFS. SHAP analysis confirmed the signature’s strong contribution and enhanced model interpretability. The developed interpretable subregional radiomics model, integrating TIME biology with clinical factors, provides a non-invasive tool for predicting LRFS in ESCC received dCRT.