biostudies-literatureGroheux DInstitut National du CancerFrench national cancer institute1249https://www.ebi.ac.uk/biostudies/studies/S-EPMC11987901biostudies-literatureUnknown17(7)<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 CancersFDG-PET/CT and Multimodal Machine Learning Model Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Triple-Negative Breast Cancer.PMC11987901INCa-DGOS-56975697Martineau ALehmann-Che JGallinato OMenu PColin TTeixeira LGroheux DBertheau PFerrer LBorgel AVargas JfalseFDG-PET/CT and Multimodal Machine Learning Model Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Triple-Negative Breast Cancer.<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 2025-01-01T00:00:00Z2025 Apr2026-07-02T03:16:25.689Z2025-07-10T03:08:18.603ZS-EPMC119879014022783610.3390/cancers17071249