Quantitative digital histopathology and machine learning to predict pathological complete response to chemotherapy in breast cancer patients using pre-treatment tumor biopsies.
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| Title: | Quantitative digital histopathology and machine learning to predict pathological complete response to chemotherapy in breast cancer patients using pre-treatment tumor biopsies. |
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| Authors: | Saednia K; Department of Electrical Engineering and Computer Science, Lassonde School of Engineering, York University, Toronto, ON, Canada.; Department of Radiation Oncology, Sunnybrook Health Sciences Center, Toronto, ON, Canada., Lagree A; Department of Radiation Oncology, Sunnybrook Health Sciences Center, Toronto, ON, Canada., Alera MA; Department of Radiation Oncology, Sunnybrook Health Sciences Center, Toronto, ON, Canada., Fleshner L; Department of Radiation Oncology, Sunnybrook Health Sciences Center, Toronto, ON, Canada., Shiner A; Department of Radiation Oncology, Sunnybrook Health Sciences Center, Toronto, ON, Canada., Law E; Department of Radiation Oncology, Sunnybrook Health Sciences Center, Toronto, ON, Canada., Law B; Department of Radiation Oncology, Sunnybrook Health Sciences Center, Toronto, ON, Canada., Dodington DW; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada., Lu FI; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada., Tran WT; Department of Radiation Oncology, Sunnybrook Health Sciences Center, Toronto, ON, Canada.; Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada.; Temerity Centre for AI Research and Education in Medicine, University of Toronto, Toronto, ON, Canada., Sadeghi-Naini A; Department of Electrical Engineering and Computer Science, Lassonde School of Engineering, York University, Toronto, ON, Canada. asn@yorku.ca.; Department of Radiation Oncology, Sunnybrook Health Sciences Center, Toronto, ON, Canada. asn@yorku.ca.; Temerity Centre for AI Research and Education in Medicine, University of Toronto, Toronto, ON, Canada. asn@yorku.ca.; Physical Sciences Platform, Sunnybrook Research Institute, Toronto, ON, Canada. asn@yorku.ca. |
| Source: | Scientific reports [Sci Rep] 2022 Jun 11; Vol. 12 (1), pp. 9690. Date of Electronic Publication: 2022 Jun 11. |
| Publication Type: | Journal Article; Research Support, Non-U.S. Gov't |
| Journal Info: | Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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| ISSN: | 2045-2322 |
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| DOI: | 10.1038/s41598-022-13917-4 |
