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. 2022 Mar 2;20:74. doi: 10.1186/s12916-022-02258-8

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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. 7 — Examples of heatmaps generated by our DLR model for PDAC and CP lesions. Generally, the highlighted area of the PDAC lesions is larger than that of the CP lesions, and most of them are distributed inside the tumor. The highlighted areas are dominated by low-enhancement regions with adjacent high-enhancement regions around. The highlighted regions of the CP lesions are mainly distributed at the boundary of the image. This may be due to the lack of PDAC features in the center of the ROI. PDAC, pancreatic ductal adenocarcinoma; CP, chronic pancreatitis; ROI, region of interest; DLR, deep learning radiomics