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Multiscale deep learning framework captures systemic immune features in lymph nodes predictive of triple negative breast cancer outcome in large-scale studies.
Verghese, Gregory; Li, Mengyuan; Liu, Fangfang; Lohan, Amit; Kurian, Nikhil Cherian; Meena, Swati; Gazinska, Patrycja; Shah, Aekta; Oozeer, Aasiyah; Chan, Terry; Opdam, Mark; Linn, Sabine; Gillett, Cheryl; Alberts, Elena; Hardiman, Thomas; Jones, Samantha; Thavaraj, Selvam; Jones, J Louise; Salgado, Roberto; Pinder, Sarah E; Rane, Swapnil; Sethi, Amit; Grigoriadis, Anita.
Afiliación
  • Verghese G; Cancer Bioinformatics, School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK.
  • Li M; School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK.
  • Liu F; Breast Cancer Now Unit, School of Cancer and Pharmaceutical Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK.
  • Lohan A; Cancer Bioinformatics, School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK.
  • Kurian NC; School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK.
  • Meena S; Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and Th
  • Gazinska P; Department of Electrical Engineering, Indian Institute of Technology Bombay, Mumbai, India.
  • Shah A; Department of Electrical Engineering, Indian Institute of Technology Bombay, Mumbai, India.
  • Oozeer A; Department of Electrical Engineering, Indian Institute of Technology Bombay, Mumbai, India.
  • Chan T; Breast Cancer Now Unit, School of Cancer and Pharmaceutical Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK.
  • Opdam M; Biobank Research Group, Lukasiewicz Research Network, PORT Polish Center for Technology Development, Wroclaw, Poland.
  • Linn S; Cancer Bioinformatics, School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK.
  • Gillett C; Department of Pathology, Tata Memorial Centre, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai, India.
  • Alberts E; King's Health Partners Cancer Biobank, King's College London, London, UK.
  • Hardiman T; Division of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
  • Jones S; Division of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
  • Thavaraj S; Division of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
  • Jones JL; Department of Medical Oncology, The Netherlands Cancer Institute, Antoni van Leeuwenhoek, Amsterdam, The Netherlands.
  • Salgado R; Department of Pathology, University Medical Centre, Utrecht, The Netherlands.
  • Pinder SE; King's Health Partners Cancer Biobank, King's College London, London, UK.
  • Rane S; Cancer Bioinformatics, School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK.
  • Sethi A; School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK.
  • Grigoriadis A; Cancer Bioinformatics, School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK.
J Pathol ; 260(4): 376-389, 2023 08.
Article en En | MEDLINE | ID: mdl-37230111
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Neoplasias de la Mama Triple Negativas / Aprendizaje Profundo Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Female / Humans Idioma: En Revista: J Pathol Año: 2023 Tipo del documento: Article País de afiliación: Reino Unido
Texto completo
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Neoplasias de la Mama Triple Negativas / Aprendizaje Profundo Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Female / Humans Idioma: En Revista: J Pathol Año: 2023 Tipo del documento: Article País de afiliación: Reino Unido