biostudies-literatureYuan QBeijing Science and Technology Medical Development FoundationBeijing Heart To Heart FoundationHaiyan Science Foundation2541316https://www.ebi.ac.uk/biostudies/studies/S-EPMC12322993biostudies-literatureUnknown57(1)<h4>Background</h4>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.<h4>Methods</h4>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 paAnnals of medicineMachine learning and SHAP value interpretation for predicting the response to neoadjuvant chemotherapy and long-term clinical outcomes in Chinese female breast cancer.PMC12322993KC2021-JF-0055-05JJMS2022-08HXXT2021ktyj002Qian YYuan QYe RCui XNiu MZhou YYu HLiu FfalseMachine learning and SHAP value interpretation for predicting the response to neoadjuvant chemotherapy and long-term clinical outcomes in Chinese female breast cancer.<h4>Background</h4>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.<h4>Methods</h4>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 pa2025-01-01T00:00:00Z2025 Dec2026-03-31T11:47:57.342Z2025-09-01T03:06:23.163ZS-EPMC123229934075495110.1080/07853890.2025.2541316