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. 2022 Jun 7;20:261. doi: 10.1186/s12967-022-03458-9

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© The Author(s) 2022

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Fig. 2 — Overall workflow of this study. a Two adjacent sections stained with CD3 and CD8 are digitalized by using the whole-slide scanner. b By using a semi-automated image analysis algorithm, a binary mask of segmented tumour region is created for each WSI. c The CD3⁺ and CD8⁺ T-cells in the tumour region are segmented and identified by using a fully-automated algorithm. d The CD3-score and CD8-score (normalized CD3⁺ and CD8⁺ cell density, ranging from 0 to 100) are classified into low and high based on the cutoffs determined by maximally selected rank statistics, respectively. e, f, g A three-category I-score and a two-category I-score are established by integrating the classifications of CD3-score and CD8-score based on the discovery dataset. h The validation cohort is used to assess the prognostic value of the I-score