Unraveling the power of radiomics: prediction and exploration of lymph node metastasis in stage T1/2 esophageal squamous cell carcinoma

Accurate assessment of lymph node metastasis (LNM) in T1/2-stage esophageal squamous cell carcinoma (ESCC) is critical for treatment planning but remains challenging due to diagnostic inaccuracies and unclear metastatic mechanisms. This study aimed to predict LNM in T1/2-stage ESCC using machine learning-based radiomics and elucidate its biological underpinnings. We retrospectively analyzed 374 surgically treated ESCC patients from two centers, employing six machine-learning algorithms to derive an optimal radiomics score. Key pathways and genes linked to LNM were investigated via bioinformatics and experimental validation. The decision tree (DT)-based radiomics model demonstrated superior predictive performance, with AUCs of 0.933 (training), 0.887 (validation), and 0.845 (test). Bioinformatics analysis implicated tumor-lymphatic invasion pathways, with EFNA1 emerging as a potential key regulator. These findings highlight the clinical utility of radiomics for LNM prediction in early-stage ESCC and provide insights into its molecular mechanisms.