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

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@article{9382004522da4d46a501a50aea052756,
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.",
author = "Mohamed Amgad and Stovgaard, {Elisabeth Specht} and Eva Balslev and Jeppe Thagaard and Weijie Chen and Sarah Dudgeon and Ashish Sharma and Kerner, {Jennifer K} and Carsten Denkert and Yinyin Yuan and Khalid AbdulJabbar and Stephan Wienert and Peter Savas and Leonie Voorwerk and Beck, {Andrew H} and Anant Madabhushi and Johan Hartman and Sebastian, {Manu M} and Horlings, {Hugo M} and Jan Hude{\v c}ek and Francesco Ciompi and Moore, {David A} and Rajendra Singh and Elvire Roblin and Balancin, {Marcelo Luiz} and Marie-Christine Mathieu and Lennerz, {Jochen K} and Pawan Kirtani and I-Chun Chen and Braybrooke, {Jeremy P} and Giancarlo Pruneri and Sandra Demaria and Sylvia Adams and Schnitt, {Stuart J} and Lakhani, {Sunil R} and Federico Rojo and Laura Comerma and Badve, {Sunil S} and Mehrnoush Khojasteh and Symmans, {W Fraser} and Christos Sotiriou and Paula Gonzalez-Ericsson and Pogue-Geile, {Katherine L} and Kim, {Rim S} and Rimm, {David L} and Giuseppe Viale and Hewitt, {Stephen M} and Bartlett, {John M S} and Fr{\'e}d{\'e}rique Penault-Llorca and Shom Goel and {International Immuno-Oncology Biomarker Working Group}",
note = "{\textcopyright} The Author(s) 2020.",
year = "2020",
doi = "10.1038/s41523-020-0154-2",
language = "English",
volume = "6",
pages = "16",
journal = "Current Medical Literature. Breast Cancer",
issn = "0956-6511",
publisher = "Remedica Medical Education and Publishing Ltd",
number = "1",

}

RIS

TY - JOUR

T1 - Report on computational assessment of Tumor Infiltrating Lymphocytes from 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 - Hudeček, 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, Frédérique

AU - Goel, Shom

AU - International Immuno-Oncology Biomarker Working Group

N1 - © The Author(s) 2020.

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 - Review

C2 - 32411818

VL - 6

SP - 16

JO - Current Medical Literature. Breast Cancer

JF - Current Medical Literature. Breast Cancer

SN - 0956-6511

IS - 1

ER -

ID: 62371544