Quantitative nuclear phenotype signatures predict nodal disease in oral squamous cell carcinoma.

Liu, Kelly Yi Ping; Zhu, Sarah Yuqi; Harrison, Alan; Chen, Zhao Yang; Guillaud, Martial; Poh, Catherine F

Liu, Kelly Yi Ping; Zhu, Sarah Yuqi; Harrison, Alan; Chen, Zhao Yang; Guillaud, Martial; Poh, Catherine F.

Affiliation

Liu KYP; Department of Oral Biological and Medical Sciences, Faculty of Dentistry, University of British Columbia, Vancouver, British Columbia, Canada.
Zhu SY; Department of Integrative Oncology, BC Cancer Research Centre, Vancouver, British Columbia, Canada.
Harrison A; Department of Oral Biological and Medical Sciences, Faculty of Dentistry, University of British Columbia, Vancouver, British Columbia, Canada.
Chen ZY; Department of Integrative Oncology, BC Cancer Research Centre, Vancouver, British Columbia, Canada.
Guillaud M; Department of Integrative Oncology, BC Cancer Research Centre, Vancouver, British Columbia, Canada.
Poh CF; Department of Integrative Oncology, BC Cancer Research Centre, Vancouver, British Columbia, Canada.

PLoS One ; 16(11): e0259529, 2021.

Article in En | MEDLINE | ID: mdl-34735529

Subject(s)

Carcinoma, Squamous Cell/metabolism; Cell Nucleus/metabolism; Squamous Cell Carcinoma of Head and Neck/metabolism; Aged; Female; Humans; Machine Learning; Male; Middle Aged; Squamous Cell Carcinoma of Head and Neck/genetics

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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Carcinoma, Squamous Cell / Cell Nucleus / Squamous Cell Carcinoma of Head and Neck Type of study: Prognostic_studies / Risk_factors_studies Limits: Aged / Female / Humans / Male / Middle aged Language: En Journal: PLoS One Journal subject: CIENCIA / MEDICINA Year: 2021 Document type: Article Affiliation country: Canada Country of publication: United States

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