biostudies-literatureUnknown14(1)Payan N<h4>Background</h4>The aim of this study is to investigate the added value of combining tumour blood flow (BF) and metabolism parameters, including texture features, with clinical parameters to predict, at baseline, the pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in patients with newly diagnosed breast cancer (BC).<h4>Methods</h4>One hundred and twenty-eight BC patients underwent a ¹⁸F-FDG PET/CT before any treatment. Tumour BF and metabolism parameters were extracted from first-pass dynamic and delayed PET images, respectively. Standard and texture features were extracted from BF and metabolic images. Prediction of pCR was performed using logistic regression, random forest and support vector classification algorithms. Models were built using clinical EJNMMI research60https://www.ebi.ac.uk/biostudies/studies/S-EPMC11224181biostudies-literatureRespective contribution of baseline clinical data, tumour metabolism and tumour blood-flow in predicting pCR after neoadjuvant chemotherapy in HER2 and Triple Negative breast cancer.PMC11224181Presles BDesmoulins IBeltjens FLadoire SBrunotte FCochet APayan NVrigneaud JMCoutant CfalseRespective contribution of baseline clinical data, tumour metabolism and tumour blood-flow in predicting pCR after neoadjuvant chemotherapy in HER2 and Triple Negative breast cancer.<h4>Background</h4>The aim of this study is to investigate the added value of combining tumour blood flow (BF) and metabolism parameters, including texture features, with clinical parameters to predict, at baseline, the pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in patients with newly diagnosed breast cancer (BC).<h4>Methods</h4>One hundred and twenty-eight BC patients underwent a ¹⁸F-FDG PET/CT before any treatment. Tumour BF and metabolism parameters were extracted from first-pass dynamic and delayed PET images, respectively. Standard and texture features were extracted from BF and metabolic images. Prediction of pCR was performed using logistic regression, random forest and support vector classification algorithms. Models were built using clinical 2024-01-01T00:00:00Z2024 Jul2025-04-22T12:08:57.413Z2025-04-06T00:13:26.094ZS-EPMC112241813896512410.1186/s13550-024-01115-4