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Predicting pathologic complete response to neoadjuvant chemotherapy in breast cancer using a machine learning approach.

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第一作者： Fangyuan,Zhao

第一单位： Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Laboratory of Molecular Oncology, Peking University Cancer Hospital & Institute, Beijing, China.

作者： Fangyuan,Zhao ^[1] ; Eric,Polley ^[2] ; Julian,McClellan ^[2] ; Frederick,Howard ^[3] ; Olufunmilayo I,Olopade ^[3] ; Dezheng,Huo ^[4]

作者单位： Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Laboratory of Molecular Oncology, Peking University Cancer Hospital & Institute, Beijing, China. ^[1] Department of Public Health Sciences, University of Chicago, Chicago, IL, USA. ^[2] Department of Medicine, Section of Hematology and Oncology, University of Chicago, Chicago, IL, USA. ^[3] Department of Public Health Sciences, University of Chicago, Chicago, IL, USA. dhuo@bsd.uchicago.edu.;Department of Medicine, Section of Hematology and Oncology, University of Chicago, Chicago, IL, USA. dhuo@bsd.uchicago.edu. ^[4]

关键词 Breast cancer Decision curve analysis Hormone receptor positive Machine learning Neoadjuvant chemotherapy Pathologic complete response Prediction model