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

Background The aim of this study was to develop a machine learning (ML) based model to accurately predict pathologic complete response (pCR) to neoadjuvant chemotherapy (NAC) using pretreatment clinical and pathological characteristics of electronic medical record (EMR) data in breast cancer (BC). M...

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Veröffentlicht in:	Breast cancer research and treatment 2021-10, Vol.189 (3), p.747-757
Hauptverfasser:	Kim, Ji-Yeon, Jeon, Eunjoo, Kwon, Soonhwan, Jung, Hyungsik, Joo, Sunghoon, Park, Youngmin, Lee, Se Kyung, Lee, Jeong Eon, Nam, Seok Jin, Cho, Eun Yoon, Park, Yeon Hee, Ahn, Jin Seok, Im, Young-Hyuck
Format:	Artikel
Sprache:	eng
Schlagworte:	Adjuvant treatment Analysis Bayesian analysis Breast cancer Cancer Cancer patients Cancer research Care and treatment Chemotherapy Electronic medical records Epidemiology ErbB-2 protein Health aspects Learning algorithms Machine learning Mathematical models Medical colleges Medical records Medicine Medicine & Public Health Multivariate analysis Oncology Patients Pertuzumab Prediction models
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