{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Yuan Q"],"funding":["Beijing Science and Technology Medical Development Foundation","Beijing Heart To Heart Foundation","Haiyan Science Foundation"],"pagination":["2541316"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12322993"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["57(1)"],"pubmed_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"],"journal":["Annals of medicine"],"pubmed_title":["Machine learning and SHAP value interpretation for predicting the response to neoadjuvant chemotherapy and long-term clinical outcomes in Chinese female breast cancer."],"pmcid":["PMC12322993"],"funding_grant_id":["KC2021-JF-0055-05","JJMS2022-08","HXXT2021ktyj002"],"pubmed_authors":["Qian Y","Yuan Q","Ye R","Cui X","Niu M","Zhou Y","Yu H","Liu F"],"additional_accession":[]},"is_claimable":false,"name":"Machine learning and SHAP value interpretation for predicting the response to neoadjuvant chemotherapy and long-term clinical outcomes in Chinese female breast cancer.","description":"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","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Dec","modification":"2026-03-31T11:47:57.342Z","creation":"2025-09-01T03:06:23.163Z"},"accession":"S-EPMC12322993","cross_references":{"pubmed":["40754951"],"doi":["10.1080/07853890.2025.2541316"]}}