{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Shen B"],"funding":["Strategic Research Foundation Grant-aided Project","Japan Society for the Promotion of Science"],"pagination":["182-194"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10073928"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["9(3)"],"pubmed_abstract":["In recent years, the treatment of breast cancer has advanced dramatically and neoadjuvant chemotherapy (NAC) has become a common treatment method, especially for locally advanced breast cancer. However, other than the subtype of breast cancer, no clear factor indicating sensitivity to NAC has been identified. In this study, we attempted to use artificial intelligence (AI) to predict the effect of preoperative chemotherapy from hematoxylin and eosin images of pathological tissue obtained from needle biopsies prior to chemotherapy. Application of AI to pathological images typically uses a single machine-learning model such as support vector machines (SVMs) or deep convolutional neural networks (CNNs). However, cancer tissues are extremely diverse and learning with a realistic number of cases"],"journal":["The journal of pathology. Clinical research"],"pubmed_title":["Development of multiple AI pipelines that predict neoadjuvant chemotherapy response of breast cancer using H&E-stained tissues."],"pmcid":["PMC10073928"],"funding_grant_id":["17H04067","S1511011","21H02706","18K07027"],"pubmed_authors":["Sato E","Cheng E","Ishikawa T","Ueda A","Shen B","Fujita K","Nagamatsu Y","Kobayashi M","Cosatto E","Hazama S","Graf HP","Hoda SA","Nagano H","Nagao T","Kuroda M","Hashimoto M","Matsubayashi J","Saito A","Mirza AH"],"additional_accession":[]},"is_claimable":false,"name":"Development of multiple AI pipelines that predict neoadjuvant chemotherapy response of breast cancer using H&E-stained tissues.","description":"In recent years, the treatment of breast cancer has advanced dramatically and neoadjuvant chemotherapy (NAC) has become a common treatment method, especially for locally advanced breast cancer. However, other than the subtype of breast cancer, no clear factor indicating sensitivity to NAC has been identified. In this study, we attempted to use artificial intelligence (AI) to predict the effect of preoperative chemotherapy from hematoxylin and eosin images of pathological tissue obtained from needle biopsies prior to chemotherapy. Application of AI to pathological images typically uses a single machine-learning model such as support vector machines (SVMs) or deep convolutional neural networks (CNNs). However, cancer tissues are extremely diverse and learning with a realistic number of cases","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 May","modification":"2025-04-04T10:42:05.933Z","creation":"2025-04-04T10:42:05.933Z"},"accession":"S-EPMC10073928","cross_references":{"pubmed":["36896856"],"doi":["10.1002/cjp2.314"]}}