AUTHOR=Wang Xueping , Tan Wencheng , Sheng Hui , Zhou Wenjia , Zheng Hailin , Huang Kewei , Lin Jinfei , Guo Songhe , Mao Minjie 

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

JOURNAL=Frontiers in Immunology

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1660897

DOI=10.3389/fimmu.2025.1660897

ISSN=1664-3224

ABSTRACT=BackgroundAlthough 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.MethodsIn 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.ResultsFollowing 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.ConclusionsWe 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.