An interpretable machine learning model using multimodal pretreatment features predicts pathological complete response to neoadjuvant immunochemotherapy in esophageal squamous cell carcinoma

Xueping Wang¹Wencheng Tan¹Hui Sheng¹Wenjia Zhou²Hailin Zheng¹Kewei Huang¹Jinfei Lin¹Songhe Guo³Minjie Mao^4*

• ¹Sun Yat-sen University Cancer Center, Guangzhou, China
• ²Guangdong Medical University, Zhanjiang, China
• ³Affiliated Cancer Hospital and Institute of Guangzhou Medical University, Guangzhou, China
• ⁴Sun Yat-sen University Cancer Center (SYSUCC), Guangzhou, China

Background: Although neoadjuvant immunochemotherapy (nICT) has revolutionized the management of locally advanced esophageal squamous cell carcinoma (ESCC), the inability to accurately predict pathological complete response (pCR) remains a major barrier to treatment personalization. We aimed to develop and validate an interpretable machine learning (ML) model using pretreatment multimodal features to predict pCR prior to nICT initiation. Methods: In this retrospective study, 114 ESCC patients receiving nICT were randomly allocated into training (n=81) and validation (n=33) cohorts (7:3 ratio). Predictors of pCR were identified from pretreatment clinical variables, endoscopic ultrasonography, and hematological biomarkers via least absolute shrinkage and selection operator (LASSO) regression. Eight machine learning algorithms were implemented to construct prediction models. Model performance was assessed by area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Shapley Additive Explanations (SHAP) provided feature importance and model interpretability. Results: Following feature selection, 17 variables were incorporated into model construction. The Random Forest (RF) model demonstrated perfect discrimination in the training cohort (AUC = 1.000, sensitivity = 1.000, specificity = 1.000, PPV = 1.000, NPV = 1.000), while maintaining robust predictive ability in the independent validation cohort (AUC = 0.913, sensitivity = 0.733, specificity = 0.889, PPV = 0.846, NPV = 0.800). Decision curve analysis (DCA) confirmed favorable clinical utility. SHAP analysis identified alcohol consumption, circumferential involvement ≥50%, elevated neutrophil-to-lymphocyte ratio (NLR), C-reactive protein (CRP), and alanine aminotransferase (ALT) as the key contributors to pCR prediction. Conclusions: We established a clinically applicable, interpretable ML model that accurately predicts pCR to nICT in ESCC by integrating multimodal pretreatment data. This tool may optimize patient selection for nICT and advance precision therapy paradigms.