{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Fang S"],"funding":["Medical Science and Technology Project of Zhejiang Province","Project of the Education Department of Zhejiang Province","Public Welfare Technology Application Research Project of Zhejiang Province","Medical and Health Science and Technology Project of Zhejiang Province","Zhejiang Clinovation Pride","Zhejiang Province Public Welfare Technology Application Research Project"],"pagination":["e71221"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12415587"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["14(17)"],"pubmed_abstract":["<h4>Background</h4>The pathological response to neoadjuvant chemotherapy (NAC) has become a vital prognostic indicator for patients with breast cancer (BC). The newly generated models depended on rather basic imaging and pathology characteristics and did not sufficiently elucidate the importance of the incorporated data. The purpose of this study is to establish and authenticate a machine learning model for predicting the pathological complete response to NAC using baseline clinical and pathological features in BC patients.<h4>Methods</h4>Data were collected from hospitalized BC patients treated with NAC at Zhejiang Provincial People's Hospital between January 2014 and August 2023. The dataset was randomly split, with 70% allocated for model training and 30% for validation. LASSO regressio"],"journal":["Cancer medicine"],"pubmed_title":["Interpretable Machine Learning for Predicting Neoadjuvant Chemotherapy Response in Breast Cancer Using the Baseline Clinical and Pathological Characteristics."],"pmcid":["PMC12415587"],"funding_grant_id":["Y202044656","LGD22H070003","2023KY046","2025KY574","2022KY068","CXTD202501004"],"pubmed_authors":["Zhang W","Kong M","Zhang J","Zhang H","Chen W","Fang S","Han C","Yuan H","Xia W","Zhong M"],"additional_accession":[]},"is_claimable":false,"name":"Interpretable Machine Learning for Predicting Neoadjuvant Chemotherapy Response in Breast Cancer Using the Baseline Clinical and Pathological Characteristics.","description":"<h4>Background</h4>The pathological response to neoadjuvant chemotherapy (NAC) has become a vital prognostic indicator for patients with breast cancer (BC). The newly generated models depended on rather basic imaging and pathology characteristics and did not sufficiently elucidate the importance of the incorporated data. The purpose of this study is to establish and authenticate a machine learning model for predicting the pathological complete response to NAC using baseline clinical and pathological features in BC patients.<h4>Methods</h4>Data were collected from hospitalized BC patients treated with NAC at Zhejiang Provincial People's Hospital between January 2014 and August 2023. The dataset was randomly split, with 70% allocated for model training and 30% for validation. LASSO regressio","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Sep","modification":"2026-06-02T00:37:36.525Z","creation":"2026-05-24T03:07:22.521Z"},"accession":"S-EPMC12415587","cross_references":{"pubmed":["40917012"],"doi":["10.1002/cam4.71221"]}}