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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| Header | DbId: mdl DbLabel: MEDLINE Ultimate An: 35690630 AccessLevel: 2 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Quantitative digital histopathology and machine learning to predict pathological complete response to chemotherapy in breast cancer patients using pre-treatment tumor biopsies. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AU" term="%22Saednia+K%22">Saednia K</searchLink>; 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.<br /><searchLink fieldCode="AU" term="%22Lagree+A%22">Lagree A</searchLink>; Department of Radiation Oncology, Sunnybrook Health Sciences Center, Toronto, ON, Canada.<br /><searchLink fieldCode="AU" term="%22Alera+MA%22">Alera MA</searchLink>; Department of Radiation Oncology, Sunnybrook Health Sciences Center, Toronto, ON, Canada.<br /><searchLink fieldCode="AU" term="%22Fleshner+L%22">Fleshner L</searchLink>; Department of Radiation Oncology, Sunnybrook Health Sciences Center, Toronto, ON, Canada.<br /><searchLink fieldCode="AU" term="%22Shiner+A%22">Shiner A</searchLink>; Department of Radiation Oncology, Sunnybrook Health Sciences Center, Toronto, ON, Canada.<br /><searchLink fieldCode="AU" term="%22Law+E%22">Law E</searchLink>; Department of Radiation Oncology, Sunnybrook Health Sciences Center, Toronto, ON, Canada.<br /><searchLink fieldCode="AU" term="%22Law+B%22">Law B</searchLink>; Department of Radiation Oncology, Sunnybrook Health Sciences Center, Toronto, ON, Canada.<br /><searchLink fieldCode="AU" term="%22Dodington+DW%22">Dodington DW</searchLink>; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada.<br /><searchLink fieldCode="AU" term="%22Lu+FI%22">Lu FI</searchLink>; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada.<br /><searchLink fieldCode="AU" term="%22Tran+WT%22">Tran WT</searchLink>; 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.<br /><searchLink fieldCode="AU" term="%22Sadeghi-Naini+A%22">Sadeghi-Naini A</searchLink>; 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. – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22101563288%22">Scientific reports</searchLink> [Sci Rep] 2022 Jun 11; Vol. 12 (1), pp. 9690. <i>Date of Electronic Publication: </i>2022 Jun 11. – Name: TypePub Label: Publication Type Group: TypPub Data: Journal Article; Research Support, Non-U.S. Gov't – Name: TitleSource Label: Journal Info Group: Src Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Nature+Publishing+Group%22">Nature Publishing Group </searchLink><i>Country of Publication: </i>England <i>NLM ID: </i>101563288 <i>Publication Model: </i>Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>2045-2322 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2220452322%22">20452322 </searchLink><i>NLM ISO Abbreviation: </i>Sci Rep <i>Subsets: </i>MEDLINE |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=mdl&AN=35690630 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1038/s41598-022-13917-4 Languages: – Code: eng Text: English PhysicalDescription: Pagination: StartPage: 9690 Titles: – TitleFull: Quantitative digital histopathology and machine learning to predict pathological complete response to chemotherapy in breast cancer patients using pre-treatment tumor biopsies. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Saednia K – PersonEntity: Name: NameFull: Lagree A – PersonEntity: Name: NameFull: Alera MA – PersonEntity: Name: NameFull: Fleshner L – PersonEntity: Name: NameFull: Shiner A – PersonEntity: Name: NameFull: Law E – PersonEntity: Name: NameFull: Law B – PersonEntity: Name: NameFull: Dodington DW – PersonEntity: Name: NameFull: Lu FI – PersonEntity: Name: NameFull: Tran WT – PersonEntity: Name: NameFull: Sadeghi-Naini A IsPartOfRelationships: – BibEntity: Dates: – D: 11 M: 06 Text: 2022 Jun 11 Type: published Y: 2022 Identifiers: – Type: issn-electronic Value: 2045-2322 Numbering: – Type: volume Value: 12 – Type: issue Value: 1 Titles: – TitleFull: Scientific reports Type: main |
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