An interpretable machine learning model using SHapley Additive exPlanations for preoperative cervical lymph node metastasis risk stratification in tongue squamous cell carcinoma: a multicenter study

Tongue squamous cell carcinoma (TSCC) is characterized by early lymph node metastasis (LNM), which significantly impacts prognosis. Traditional diagnostic methods rely on invasive biopsies or postoperative histopathology, highlighting the need for non-invasive preoperative prediction tools. This study aimed to develop an interpretable radiomics model using tumor shape features from magnetic resonance imaging (MRI) to predict cervical LNM in TSCC. We retrospectively analyzed data from 293 TSCC patients across two hospitals. Shape-related radiomic features were extracted from preoperative contrast-enhanced T1-weighted imaging (CET1WI) and T2-weighted imaging (T2WI). A radiomics model was developed using logistic regression (LR) and validated internally and externally. Clinical variables were integrated into a combined model. The SHapley Additive exPlanations (SHAP) framework was employed to interpret feature contributions. The radiomics model achieved AUCs of 0.818 (training cohort), 0.739 (validation cohort), and 0.755 (test cohort). Incorporating clinical variables did not significantly improve performance. SHAP analysis identified T2WI_SurfaceVolumeRatio as the most influential feature. Individualized force plots and a web-based nomogram provided intuitive visualizations of model predictions. Tumor shape features derived from MRI, particularly SurfaceVolumeRatio, independently predict cervical LNM in TSCC. The SHAP-interpretable radiomics model offers a clinically transparent, non-invasive tool for preoperative risk stratification, aiding personalized treatment decisions.