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. 2023 Dec 14;14:8290. doi: 10.1038/s41467-023-43749-3

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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. 3 — A Example of a HE slide of a cHCC-CCA and its associated attention and prediction heatmaps. This case features relatively distinct HCC and ICCA components, both of which are identified on the attention maps (attention is however higher in ICCA areas). The class predictions match the HCC and ICCA morphological contingents. This analysis was repeated independently with similar results five times. B Distribution of the raw outputs/predictions from the model: the scores follow a bimodal distribution, with a majority of cases peaking at a high HCC or ICCA. C Reclassification of cHCC-CCA as HCC or CCA has an impact on overall survival of patients treated by surgical resection. D Importantly, the prognosis value of the reclassification is validated in patients who underwent liver transplantation. E The model predictions match with the underlying alterations identified in cHCC-CCA (p = 0.0009, Fisher’s exact test). Statistical tests were two-sided and not adjusted for multiple testing. Source data are provided as a Source Data file.