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

ABSTRACT:

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

SUBMITTER: Wang X 

PROVIDER: S-EPMC12479532 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature