Report on computational assessment of Tumor Infiltrating Lymphocytes from the International Immuno-Oncology Biomarker Working Group.
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Date
2020-05-12ICR Author
Author
Amgad, M
Stovgaard, ES
Balslev, E
Thagaard, J
Chen, W
Dudgeon, S
Sharma, A
Kerner, JK
Denkert, C
Yuan, Y
AbdulJabbar, K
Wienert, S
Savas, P
Voorwerk, L
Beck, AH
Madabhushi, A
Hartman, J
Sebastian, MM
Horlings, HM
Hudeček, J
Ciompi, F
Moore, DA
Singh, R
Roblin, E
Balancin, ML
Mathieu, M-C
Lennerz, JK
Kirtani, P
Chen, I-C
Braybrooke, JP
Pruneri, G
Demaria, S
Adams, S
Schnitt, SJ
Lakhani, SR
Rojo, F
Comerma, L
Badve, SS
Khojasteh, M
Symmans, WF
Sotiriou, C
Gonzalez-Ericsson, P
Pogue-Geile, KL
Kim, RS
Rimm, DL
Viale, G
Hewitt, SM
Bartlett, JMS
Penault-Llorca, F
Goel, S
Lien, H-C
Loibl, S
Kos, Z
Loi, S
Hanna, MG
Michiels, S
Kok, M
Nielsen, TO
Lazar, AJ
Bago-Horvath, Z
Kooreman, LFS
van der Laak, JAWM
Saltz, J
Gallas, BD
Kurkure, U
Barnes, M
Salgado, R
Cooper, LAD
International Immuno-Oncology Biomarker Working Group,
Type
Journal Article
Metadata
Show full item recordAbstract
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.
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Subject
International Immuno-Oncology Biomarker Working Group
Research team
Computational Pathology & Integrated Genomics
Language
eng
Date accepted
2020-02-18
License start date
2020-01
Citation
NPJ breast cancer, 2020, 6 pp. 16 - ?
Publisher
NATURE RESEARCH