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. 2023 Jan 27;7:14. doi: 10.1038/s41698-023-00352-5

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© The Author(s) 2023

Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 1 — a H&E tissue segmentation based on DeepLabV3 model. The segmentation generates stroma region, tumor region, and lymphocytes aggregated (lymph) region. b IHC markers segmentation. CD8, CD163, and PD-L1 were segmented. c H&E and IHC non-rigid registration. First row: representative H&E patches; second row: corresponding IHC patches after registration. d IMage-based Pathological REgistration and Segmentation Statistics (IMPRESS) feature construction. Totally 36 IMPRESS features were constructed. e Neoadjuvant chemotherapy (NAC) prediction with logistic regression.