{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Han L"],"funding":["China Scholarship Council","Fundo para o Desenvolvimento das Ciências e da Tecnologia"],"pagination":["103356"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12303063"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["86"],"pubmed_abstract":["<h4>Background</h4>Neoadjuvant therapy (NAT) regimens for breast cancer are generally determined according to cancer stage and molecular subtypes without fully considering the inter-patient variability, which may lead to inefficiency or overtreatment. Artificial intelligence (AI) may support personalized regimen recommendations by learning the synergistic relationship between pre-NAT individual-patient data, regimens, and corresponding short- or long-term therapy responses.<h4>Methods</h4>In this retrospective study, we collected data from breast cancer patients treated with NAT between 2000 and 2020 from the Netherlands and the USA. Median follow-up times ranged from 3·7 to 4·9 years across molecular subtypes and cohorts. We developed and externally validated a multi-modal model integrati"],"journal":["EClinicalMedicine"],"pubmed_title":["Exploring personalized neoadjuvant therapy selection strategies in breast cancer: an explainable multi-modal response model."],"pmcid":["PMC12303063"],"funding_grant_id":["202006240065","0105/2022/A"],"pubmed_authors":["Pinker-Domenig K","Kok M","van der Voort A","Han L","Liang X","Lu C","Mann R","Zhang T","Gao Y","Tan T","Wang X","Sonke G","Teuwen J","D'Angelo A"],"additional_accession":[]},"is_claimable":false,"name":"Exploring personalized neoadjuvant therapy selection strategies in breast cancer: an explainable multi-modal response model.","description":"<h4>Background</h4>Neoadjuvant therapy (NAT) regimens for breast cancer are generally determined according to cancer stage and molecular subtypes without fully considering the inter-patient variability, which may lead to inefficiency or overtreatment. Artificial intelligence (AI) may support personalized regimen recommendations by learning the synergistic relationship between pre-NAT individual-patient data, regimens, and corresponding short- or long-term therapy responses.<h4>Methods</h4>In this retrospective study, we collected data from breast cancer patients treated with NAT between 2000 and 2020 from the Netherlands and the USA. Median follow-up times ranged from 3·7 to 4·9 years across molecular subtypes and cohorts. We developed and externally validated a multi-modal model integrati","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Aug","modification":"2026-07-15T09:46:48.234Z","creation":"2025-08-17T03:05:55.137Z"},"accession":"S-EPMC12303063","cross_references":{"pubmed":["40727014"],"doi":["10.1016/j.eclinm.2025.103356"]}}