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s41523-020-0154-2.pdf (2.97 MB)

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

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journal contribution
posted on 2023-06-19, 09:40 authored by M Amgad, ES Stovgaard, E Balslev, J Thagaard, W Chen, S Dudgeon, A Sharma, JK Kerner, C Denkert, Y Yuan, K AbdulJabbar, S Wienert, P Savas, L Voorwerk, AH Beck, A Madabhushi, J Hartman, MM Sebastian, HM Horlings, J Hudeček, F Ciompi, DA Moore, R Singh, E Roblin, ML Balancin, MC Mathieu, JK Lennerz, P Kirtani, IC Chen, JP Braybrooke, G Pruneri, S Demaria, S Adams, SJ Schnitt, SR Lakhani, F Rojo, L Comerma, SS Badve, M Khojasteh, WF Symmans, C Sotiriou, P Gonzalez-Ericsson, KL Pogue-Geile, RS Kim, DL Rimm, G Viale, SM Hewitt, JMS Bartlett, F Penault-Llorca, S Goel, HC Lien, S Loibl, Z Kos, S Loi, MG Hanna, S Michiels, M Kok, TO Nielsen, AJ Lazar, Z Bago-Horvath, LFS Kooreman, JAWM van der Laak, J Saltz, BD Gallas, U Kurkure, M Barnes, R Salgado, LAD Cooper, International Immuno-Oncology Biomarker Working Group
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.


Crick (Grant ID: CC2041, Grant title: Swanton CC2041)