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. 2023 Aug 19;50(13):3996–4009. doi: 10.1007/s00259-023-06399-7

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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 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. 2 — Nomogram and calibration curves. a An integrated MTDLR nomogram was built with TNM stage, DeepMTS-derived prognostic prediction score (DeepMTS-Score), and DeepMTS-derived automatic radiomics score (AutoRadio-Score) to predict 3-year and 5-year PFS probability. For calculating the 3-year and 5-year PFS probability with the nomogram, firstly, we locate the patient’s TNM stage and draw a line straight upward to the “Points” axis to determine the points associated with the corresponding TNM stage. Then, we repeat the process for DeepMTS-Score and AutoRadio-Score, and sum the total points achieved for the three covariates. Lastly, we locate this sum on the “Total Points” axis, and draw a line straight down to determine the probability of 3-year and 5-year PFS. b The 3-year and c 5-year PFS calibration curves of the integrated MTDLR nomogram in the training, internal validation, and external validation cohorts. The actual PFS probability is plotted on the y-axis, while nomogram predicted probability is plotted on the x-axis. The P value of calibration was calculated by Hosmer–Lemeshow goodness-of-fit test, and P value > 0.05 indicates the good match between the actual and predicted PFS probability