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Report on computational assessment of Tumor Infiltrating Lymphocytes from the International Immuno-Oncology Biomarker Working Group

  • International Immuno-Oncology Biomarker Working Group
  • Emory University
  • University of Copenhagen
  • Technical University of Denmark
  • Visiopharm A/S
  • United States Food and Drug Administration
  • PathAI
  • University of Marburg
  • Charité – Universitätsmedizin Berlin
  • The Institute of Cancer Research
  • University of Melbourne
  • Netherlands Cancer Institute
  • Case Western Reserve University
  • Louis Stokes VA Medical Center
  • Karolinska Institutet
  • University of Texas MD Anderson Cancer Center
  • Radboud University Nijmegen
  • University College London
  • Icahn School of Medicine at Mount Sinai
  • Université Paris-Saclay
  • Universidade de São Paulo
  • University of Paris Sud
  • Massachusetts General Hospital
  • Manipal Hospitals Dwarka
  • National Taiwan University
  • University of Oxford
  • University Hospitals Bristol and Weston NHS Foundation Trust
  • University of Milan
  • Cornell University
  • New York University
  • Brigham and Women’s Hospital
  • University of Queensland
  • CIBERONC-Instituto de Investigación Sanitaria Fundación Jiménez Díaz (IIS-FJD)
  • GEICAM-Spanish Breast Cancer Research Group
  • Indiana University Bloomington
  • Digital Pathology
  • Université libre de Bruxelles
  • Vanderbilt University
  • NSABP Foundation, Inc.
  • Yale University
  • IRCCS Istituto Europeo di Oncologia - Milano
  • National Institutes of Health
  • Ontario Institute for Cancer Research
  • NHS Lothian
  • Centre Jean Perrin
  • Université Clermont Auvergne

Research output: Contribution to journalReview articlepeer-review

137 Scopus citations

Abstract

Assessment of tumor-infiltrating lymphocytes (TILs) is increasingly recognized as an integral part of the prognostic workflow in triple-negative (TNBC) and HER2-positive breast cancer, as well as many other solid tumors. This recognition has come about thanks to standardized visual reporting guidelines, which helped to reduce inter-reader variability. Now, there are ripe opportunities to employ computational methods that extract spatio-morphologic predictive features, enabling computer-aided diagnostics. We detail the benefits of computational TILs assessment, the readiness of TILs scoring for computational assessment, and outline considerations for overcoming key barriers to clinical translation in this arena. Specifically, we discuss: 1. ensuring computational workflows closely capture visual guidelines and standards; 2. challenges and thoughts standards for assessment of algorithms including training, preanalytical, analytical, and clinical validation; 3. perspectives on how to realize the potential of machine learning models and to overcome the perceptual and practical limits of visual scoring.

Original languageEnglish
Article number16
Journalnpj Breast Cancer
Volume6
Issue number1
DOIs
StatePublished - Dec 2022

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