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. 2022 Oct 15;20:471. doi: 10.1186/s12967-022-03688-x

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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 — Training and validation of the TILs-predicting radiomics (Rad-TILs) model. A, B Representative TNBC pathological samples with high (A) and low (B) stromal tumor-infiltrating lymphocytes (sTILs). C Heatmap showing the distribution of selected radiomics feature value in high and low sTILs samples from the training and validation cohorts. D, E The correlation between sTILs status evaluated by pathologists (high and low sTILs) and TILs scores predicted by the radiomics model (Radiomics TILs score) in the training cohort (D) and validation cohort (E). F, G Receiver operating characteristic (ROC) curve of Rad-TILs model in the training cohort (F) and validation cohort (G)