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. 2023 Sep 12;14:145. doi: 10.1186/s13244-023-01496-5

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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. 3 — Comparison of predictive efficacy for PHLF between the model and the ALBI score. The AUC of the model was significantly superior to that of the ALBI score in the training cohort (a), internal validation cohort (b), and external validation cohort (c) (DeLong test: p < 0.001, p < 0.001, and p < 0.001, respectively). The calibration curves of the model in the training cohort (d), internal validation cohort (e), and external validation cohort (f) showed good consistency. PHLF, posthepatectomy liver failure; ALBI, albumin–bilirubin; AUC, area under the curve