{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Li C"],"funding":["Guangzhou Science and Technology Major Program","Guangdong Medical Science and Technology Program","Sun Yat-Sen University Clinical Research 5010 Program","National Natural Science Foundation of China","Natural Science Foundation of Guangdong Province","Tencent Charity Foundation","National Science and Technology Major Project","Guangdong Science and Technology Department","Medical Artificial Intelligence Project of Sun Yat-Sen Memorial Hospital","Sun Yat-Sen Clinical Research Cultivating Program"],"pagination":["7685-7693"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9550709"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["29(12)"],"pubmed_abstract":["<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 use"],"journal":["Annals of surgical oncology"],"pubmed_title":["A Noninvasive Tool Based on Magnetic Resonance Imaging Radiomics for the Preoperative Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer."],"pmcid":["PMC9550709"],"funding_grant_id":["2017A030313828","202206010078","81972471","2020ZX09201021","SYSU-05160-20200506-0001","82073408","YXRGZN201902","2017B030314026","SYS-C-201801","SYSU-81000-20200311-0001","A2020558","81572596","201704020131","2018007"],"pubmed_authors":["Ren W","Liu J","Li C","Mao L","Liu Y","Wu Z","Lu N","He Z","Tan Y","Chen Y","Yao H","Xie C","Yu Y"],"additional_accession":[]},"is_claimable":false,"name":"A Noninvasive Tool Based on Magnetic Resonance Imaging Radiomics for the Preoperative Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer.","description":"<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 use","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Nov","modification":"2025-04-05T11:38:30.847Z","creation":"2025-04-05T11:38:30.847Z"},"accession":"S-EPMC9550709","cross_references":{"pubmed":["35773561"],"doi":["10.1245/s10434-022-12034-w"]}}