Analysis of tumor nuclear features using artificial intelligence to predict response to neoadjuvant chemotherapy in high-risk breast cancer patients.

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Bibliographic Details
Title: Analysis of tumor nuclear features using artificial intelligence to predict response to neoadjuvant chemotherapy in high-risk breast cancer patients.
Authors: Dodington DW; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada., Lagree A; Biological Sciences Platform, Sunnybrook Research Institute, Toronto, ON, Canada., Tabbarah S; Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, ON, Canada., Mohebpour M; Biological Sciences Platform, Sunnybrook Research Institute, Toronto, ON, Canada., Sadeghi-Naini A; Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.; Department of Electrical Engineering and Computer Science, York University, Toronto, ON, Canada., Tran WT; Biological Sciences Platform, Sunnybrook Research Institute, Toronto, ON, Canada.; Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.; Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada.; Temerty Centre for Artificial Intelligence Research and Education in Medicine, University of Toronto, Toronto, ON, Canada., Lu FI; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada. fangi.lu@sunnybrook.ca.; Department of Laboratory Medicine and Molecular Diagnostics, Sunnybrook Health Sciences Centre, 2075 Bayview Ave., Rm E423a, Toronto, ON, M4N 3M5, Canada. fangi.lu@sunnybrook.ca.
Source: Breast cancer research and treatment [Breast Cancer Res Treat] 2021 Apr; Vol. 186 (2), pp. 379-389. Date of Electronic Publication: 2021 Jan 23.
Publication Type: Journal Article
Journal Info: Publisher: Kluwer Academic Country of Publication: Netherlands NLM ID: 8111104 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1573-7217 (Electronic) Linking ISSN: 01676806 NLM ISO Abbreviation: Breast Cancer Res Treat Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1573-7217
DOI:10.1007/s10549-020-06093-4