TY - JOUR
T1 - Report on computational assessment of Tumor Infiltrating Lymphocytes from the International Immuno-Oncology Biomarker Working Group
AU - The International Immuno-Oncology Biomarker Working Group
AU - Amgad, Mohamed
AU - Stovgaard, Elisabeth Specht
AU - Balslev, Eva
AU - Thagaard, Jeppe
AU - Chen, Weijie
AU - Dudgeon, Sarah
AU - Sharma, Ashish
AU - Kerner, Jennifer K.
AU - Denkert, Carsten
AU - Yuan, Yinyin
AU - AbdulJabbar, Khalid
AU - Wienert, Stephan
AU - Savas, Peter
AU - Voorwerk, Leonie
AU - Beck, Andrew H.
AU - Madabhushi, Anant
AU - Hartman, Johan
AU - Sebastian, Manu M.
AU - Horlings, Hugo M.
AU - Hudecek, Jan
AU - Ciompi, Francesco
AU - Moore, David A.
AU - Singh, Rajendra
AU - Roblin, Elvire
AU - Balancin, Marcelo Luiz
AU - Mathieu, Marie-Christine
AU - Lennerz, Jochen K.
AU - Kirtani, Pawan
AU - Chen, I-Chun
AU - Braybrooke, Jeremy P.
AU - Pruneri, Giancarlo
AU - Demaria, Sandra
AU - Adams, Sylvia
AU - Schnitt, Stuart J.
AU - Lakhani, Sunil R.
AU - Rojo, Federico
AU - Comerma, Laura
AU - Badve, Sunil S.
AU - Khojasteh, Mehrnoush
AU - Symmans, W. Fraser
AU - Sotiriou, Christos
AU - Gonzalez-Ericsson, Paula
AU - Pogue-Geile, Katherine L.
AU - Kim, Rim S.
AU - Rimm, David L.
AU - Viale, Giuseppe
AU - Hewitt, Stephen M.
AU - Bartlett, John M. S.
AU - Penault-Llorca, Frederique
AU - Goel, Shom
AU - Lien, Huang-Chun
AU - Loibl, Sibylle
AU - Kos, Zuzana
AU - Loi, Sherene
AU - Hanna, Matthew G.
AU - Michiels, Stefan
AU - Kok, Marleen
AU - Nielsen, Torsten O.
AU - Lazar, Alexander J.
AU - Bago-Horvath, Zsuzsanna
AU - Kooreman, Loes F. S.
AU - van der Laak, Jeroen A. W. M.
AU - Saltz, Joel
AU - Gallas, Brandon D.
AU - Kurkure, Uday
AU - Barnes, Michael
AU - Salgado, Roberto
AU - Cooper, Lee A. D.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
U2 - 10.1038/s41523-020-0154-2
DO - 10.1038/s41523-020-0154-2
M3 - Journal article
C2 - 32411818
VL - 6
JO - n p j Breast Cancer
JF - n p j Breast Cancer
SN - 2374-4677
M1 - 16
ER -
