biostudies-literatureLi CGuangzhou Science and Technology Major ProgramGuangdong Medical Science and Technology ProgramSun Yat-Sen University Clinical Research 5010 ProgramNational Natural Science Foundation of ChinaNatural Science Foundation of Guangdong ProvinceTencent Charity FoundationNational Science and Technology Major ProjectGuangdong Science and Technology DepartmentMedical Artificial Intelligence Project of Sun Yat-Sen Memorial HospitalSun Yat-Sen Clinical Research Cultivating Program7685-7693https://www.ebi.ac.uk/biostudies/studies/S-EPMC9550709biostudies-literatureUnknown29(12)<h4>Purpose</h4>This study aimed to identify patients with pathological complete response (pCR) and make better clinical decisions by constructing a preoperative predictive model based on tumoral and peritumoral volumes of multiparametric magnetic resonance imaging (MRI) obtained before neoadjuvant chemotherapy (NAC).<h4>Methods</h4>This study investigated MRI before NAC in 448 patients with nonmetastatic invasive ductal breast cancer (Sun Yat-sen Memorial Hospital, Sun Yat-sen University, n = 362, training cohort; and Sun Yat-sen University Cancer Center, n = 86, validation cohort). The tumoral and peritumoral volumes of interest (VOIs) were segmented and MRI features were extracted. The radiomic features were filtered via a random forest algorithm, and a supporting vector machine was useAnnals of surgical oncologyA Noninvasive Tool Based on Magnetic Resonance Imaging Radiomics for the Preoperative Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer.PMC95507092017A030313828202206010078819724712020ZX09201021SYSU-05160-20200506-000182073408YXRGZN2019022017B030314026SYS-C-201801SYSU-81000-20200311-0001A2020558815725962017040201312018007Ren WLiu JLi CMao LLiu YWu ZLu NHe ZTan YChen YYao HXie CYu YfalseA Noninvasive Tool Based on Magnetic Resonance Imaging Radiomics for the Preoperative Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer.<h4>Purpose</h4>This study aimed to identify patients with pathological complete response (pCR) and make better clinical decisions by constructing a preoperative predictive model based on tumoral and peritumoral volumes of multiparametric magnetic resonance imaging (MRI) obtained before neoadjuvant chemotherapy (NAC).<h4>Methods</h4>This study investigated MRI before NAC in 448 patients with nonmetastatic invasive ductal breast cancer (Sun Yat-sen Memorial Hospital, Sun Yat-sen University, n = 362, training cohort; and Sun Yat-sen University Cancer Center, n = 86, validation cohort). The tumoral and peritumoral volumes of interest (VOIs) were segmented and MRI features were extracted. The radiomic features were filtered via a random forest algorithm, and a supporting vector machine was use2022-01-01T00:00:00Z2022 Nov2025-04-05T11:38:30.847Z2025-04-05T11:38:30.847ZS-EPMC95507093577356110.1245/s10434-022-12034-w