Prediction of pathologic complete response to neoadjuvant chemotherapy using machine learning models in patients with breast cancer

Ji‐Yeon Kim; Eunjoo Jeon; Soonhwan Kwon; Hyungsik Jung; Sunghoon Joo; Youngmin Park; Se Kyung Lee; Joon Jeong; Seok Jin Nam; Eun Yoon Cho; Yeon Hee Park; Jin Seok Ahn; Young‐Hyuck Im

doi:10.1007/s10549-021-06310-8

JOURNAL ARTICLE

Prediction of pathologic complete response to neoadjuvant chemotherapy using machine learning models in patients with breast cancer

Ji‐Yeon Kim Eunjoo Jeon Soonhwan Kwon Hyungsik Jung Sunghoon Joo Youngmin Park Se Kyung Lee Joon Jeong Seok Jin Nam Eun Yoon Cho Yeon Hee Park Jin Seok Ahn Young‐Hyuck Im

Year: 2021 Journal: Breast Cancer Research and Treatment Vol: 189 (3)Pages: 747-757 Publisher: Springer Science+Business Media

DOI: 10.1007/s10549-021-06310-8

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Keywords:

Breast cancer Medicine Internal medicine Multivariate analysis Pathological Oncology Chemotherapy Neoadjuvant therapy Cancer Subgroup analysis Meta-analysis

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Topics

Breast Cancer Treatment Studies

Life Sciences → Biochemistry, Genetics and Molecular Biology → Cancer Research

AI in cancer detection

Physical Sciences → Computer Science → Artificial Intelligence

Cancer Genomics and Diagnostics

Life Sciences → Biochemistry, Genetics and Molecular Biology → Cancer Research

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