Abstract GS1-05: Prognostic and predictive associations of manual, digital and AI-derived tumor infiltrating lymphocytes-scoring: A retrospective analysis from the Phase III APHINITY trial
Year: 2026
Type: conference-abstract
Source: Clinical Cancer Research
Authors: R. Salgado, L. E. Lara Gonzalez, F. Giudici, F. Rojo, L. Comerma, S. Wienert, J. Palacios, Z. Kos, S. L. De Haas, A. Rodriguez Lescure, G. Viale, Y. Zheng, D. Gao, A. Kiermaier, F. Andre, S. Loibl, M. J. PIccart, R. Gelber, D. Cameron, I. E. Krop, P. Savas, T. O. Nielsen, C. Denkert, S. Michiels, S. Loi, The International Immuno-Oncology Biomarker Working Group
Institutions: Peter MacCallum Cancer Centre, Centro di Riferimento Oncologico, Hospital Universitario Fundación Jiménez Díaz, Centro de Investigación Biomédica en Red de Cáncer, Instituto de Investigación Sanitaria Fundación Jiménez Díaz, Hospital Del Mar, Humboldt-Universität zu Berlin, Freie Universität Berlin, Universidad de Alcalá, Instituto Ramón y Cajal de Investigación Sanitaria, Hospital Universitario Ramón y Cajal, University of British Columbia, Roche (Switzerland), Hospital General Universitario de Elche, European Institute of Oncology, Dana-Farber Cancer Institute, Université Paris-Saclay, Institut Gustave Roussy, Université Libre de Bruxelles, Institut Jules Bordet, Harvard University, Dana-Farber/Harvard Cancer Center, University of Edinburgh, Yale Cancer Center, Yale University, Philipps University of Marburg
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: closed
OpenAlex ID: W7129683304