Machine learning and SHAP value interpretation for predicting the response to neoadjuvant chemotherapy and long-term clinical outcomes in Chinese female breast cancer.

ABSTRACT:

Background

Most models of neoadjuvant chemotherapy (NACT) for breast cancer (BC) suffer from insufficient data and lack interpretability. Additionally, there is a notable absence of reports from China in this field. This study is also the first to integrate the Advanced Lung Cancer Inflammation Index (ALI) into such a model to evaluate its effectiveness.

Methods

Data from 3,036 female BC patients receiving NACT at Heilongjiang Provincial Tumor Hospital (2008-2019, median follow-up 7.28 years) were analyzed. After screening, 2,909 patients were randomized into training and validation cohorts (7:3). Using eXtreme Gradient Boosting (XGBoost), Gradient Boosting Classifier (GBC), Support Vector Machine (SVM) models, and SHapley Additive exPlanations (SHAP), the best predicting pa

SUBMITTER: Yuan Q 

PROVIDER: S-EPMC12322993 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature