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. 2022 Apr 16;23:96. doi: 10.1186/s12931-022-02016-7

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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. 4 — The receiver operating characteristic (ROC) curves showing the performance of the nomogram, radiomic model, clinical-semantic model, intra-nodular radiomic model and peri-nodular radiomic model in A training and B validation set. A Delong test showed that the nomogram exhibited better performance comparing to the clinical-semantic model in both training (p = 0.0002) and validation set (p = 0.003). Data in the parentheses referred to the 95% confidence interval of area under the curve (AUC)