Journal article
Scoring of tumor-infiltrating lymphocytes: From visual estimation to machine learning
F Klauschen, KR Müller, A Binder, M Bockmayr, M Hägele, P Seegerer, S Wienert, G Pruneri, S de Maria, S Badve, S Michiels, TO Nielsen, S Adams, P Savas, F Symmans, S Willis, T Gruosso, M Park, B Haibe-Kains, B Gallas Show all
Seminars in Cancer Biology | ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD | Published : 2018
Abstract
The extent of tumor-infiltrating lymphocytes (TILs), along with immunomodulatory ligands, tumor-mutational burden and other biomarkers, has been demonstrated to be a marker of response to immune-checkpoint therapy in several cancers. Pathologists have therefore started to devise standardized visual approaches to quantify TILs for therapy prediction. However, despite successful standardization efforts visual TIL estimation is slow, with limited precision and lacks the ability to evaluate more complex properties such as TIL distribution patterns. Therefore, computational image analysis approaches are needed to provide standardized and efficient TIL quantification. Here, we discuss different au..
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Awarded by Breast Cancer Research Foundation
Funding Acknowledgements
RS and SL are supported by a grant from the Breast Cancer Research Foundation (BCRF). CD and FK are supported by the German Cancer Consortium (Berlin partner site, DKTK). This work was supported by the Brain Korea 21 Plus Program through the National Research Foundation of Korea; the Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) [No. 2017-0-01779]; the Deutsche Forschungsgemeinschaft (DFG) [grant MU 987/17-1]; and the German Ministry for Education and Research as Berlin Big Data Center (BBDC) [01IS14013A] and Berlin Machine Learning Center (BZML). This publication only reflects the authors views.