Report on computational assessment of tumor infiltrating lymphocytes from the International Immuno-Oncology Biomarker Working Group Review

   


Authors: Amgad, M.; Stovgaard, E. S.; Balslev, E.; Thagaard, J.; Chen, W.; Dudgeon, S.; Sharma, A.; Kerner, J. K.; Denkert, C.; Yuan, Y.; AbdulJabbar, K.; Wienert, S.; Savas, P.; Voorwerk, L.; Beck, A. H.; Madabhushi, A.; Hartman, J.; Sebastian, M. M.; Horlings, H. M.; Hudecek, J.; Ciompi, F.; Moore, D. A.; Singh, R.; Roblin, E.; Balancin, M. L.; Mathieu, M. C.; Lennerz, J. K.; Kirtani, P.; Chen, I. C.; Braybrooke, J. P.; Pruneri, G.; Demaria, S.; Adams, S.; Schnitt, S. J.; Lakhani, S. R.; Rojo, F.; Comerma, L.; Badve, S. S.; Khojasteh, M.; Symmans, W. F.; Sotiriou, C.; Gonzalez-Ericsson, P.; Pogue-Geile, K. L.; Kim, R. S.; Rimm, D. L.; Viale, G.; Hewitt, S. M.; Bartlett, J. M. S.; Penault-Llorca, F.; Goel, S.; Lien, H. C.; Loibl, S.; Kos, Z.; Loi, S.; Hanna, M. G.; Michiels, S.; Kok, M.; Nielsen, T. O.; Lazar, A. J.; Bago-Horvath, Z.; Kooreman, L. F. S.; van der Laak, J. A. W. M.; Saltz, J.; Gallas, B. D.; Kurkure, U.; Barnes, M.; Salgado, R.; Cooper, L. A. D.; and the International Immuno-Oncology Biomarker Working Group
Contributors: Brogi, E.; Reis-Filho, J.; d'Alfons, T.
Review Title: Report on computational assessment of tumor infiltrating lymphocytes from the International Immuno-Oncology Biomarker Working Group
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.
Keywords: classification; in-situ; solid tumors; breast-cancer; t-cells; digital pathology; quality-control; image-analysis; standardized method; tils
Journal Title: npj Breast Cancer
Volume: 6
ISSN: 2374-4677
Publisher: Nature Publishing Group  
Date Published: 2020-05-12
Start Page: 16
Language: English
ACCESSION: WOS:000544983100002
DOI: 10.1038/s41523-020-0154-2
PROVIDER: wos
PMCID: PMC7217824
PUBMED: 32411818
Notes: Timothy D'Alfonso's last name is misspelled on the original publication -- Review -- Source: Wos
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MSK Authors
  1. Edi Brogi
    515 Brogi
  2. Jorge Sergio Reis-Filho
    639 Reis-Filho
  3. Matthew George Hanna
    101 Hanna
  4. Timothy D'Alfonso
    54 D'Alfonso
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