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. 2022 Nov 21;50(3):727–741. doi: 10.1007/s00259-022-06036-9

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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/.

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Fig. 5 — Nomogram of PIDL-CS for the detection of clinically significant prostate cancer. Abbreviation: PI-RADS, Prostate Imaging and Reporting and Data System; DL-CS-Res, the deep learning model based on ResNet3D network for the classification between clinically significant and non-clinically significant prostate cancer; PIDL-CS, integrated model combining DL-CS-Res and PI-RADS assessment