{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Groheux D"],"funding":["Institut National du Cancer","French national cancer institute"],"pagination":["1249"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11987901"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["17(7)"],"pubmed_abstract":["<b>Purpose:</b> Triple-negative breast cancer (TNBC) is a biologically and clinically heterogeneous disease, associated with poorer outcomes when compared with other subtypes of breast cancer. Neoadjuvant chemotherapy (NAC) is often given before surgery, and achieving a pathological complete response (pCR) has been associated with patient outcomes. There is thus strong clinical interest in the ability to accurately predict pCR status using baseline data. <b>Materials and Methods:</b> A cohort of 57 TNBC patients who underwent FDG-PET/CT before NAC was analyzed to develop a machine learning (ML) algorithm predictive of pCR. A total of 241 predictors were collected for each patient: 11 clinical features, 11 histopathological features, 13 genomic features, and 206 PET features, including 195 "],"journal":["Cancers"],"pubmed_title":["FDG-PET/CT and Multimodal Machine Learning Model Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Triple-Negative Breast Cancer."],"pmcid":["PMC11987901"],"funding_grant_id":["INCa-DGOS-5697","5697"],"pubmed_authors":["Martineau A","Lehmann-Che J","Gallinato O","Menu P","Colin T","Teixeira L","Groheux D","Bertheau P","Ferrer L","Borgel A","Vargas J"],"additional_accession":[]},"is_claimable":false,"name":"FDG-PET/CT and Multimodal Machine Learning Model Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Triple-Negative Breast Cancer.","description":"<b>Purpose:</b> Triple-negative breast cancer (TNBC) is a biologically and clinically heterogeneous disease, associated with poorer outcomes when compared with other subtypes of breast cancer. Neoadjuvant chemotherapy (NAC) is often given before surgery, and achieving a pathological complete response (pCR) has been associated with patient outcomes. There is thus strong clinical interest in the ability to accurately predict pCR status using baseline data. <b>Materials and Methods:</b> A cohort of 57 TNBC patients who underwent FDG-PET/CT before NAC was analyzed to develop a machine learning (ML) algorithm predictive of pCR. A total of 241 predictors were collected for each patient: 11 clinical features, 11 histopathological features, 13 genomic features, and 206 PET features, including 195 ","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Apr","modification":"2026-07-02T03:16:25.689Z","creation":"2025-07-10T03:08:18.603Z"},"accession":"S-EPMC11987901","cross_references":{"pubmed":["40227836"],"doi":["10.3390/cancers17071249"]}}