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RESEARCH Open Access© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, 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 you modif i ed the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. 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 h t t p : / / c r e a t i v e c o m m o n s . o r g / l i c e n s e s / b y - n c - n d / 4 . 0 / . Peng et al. BMC Medical Imaging (2025) 25:4 https://doi.org/10.1186/s12880-024-01542-8BMC Medical Imaging† Yu-ting Peng and Jin-shu Pang contributed equally to this work and share fi rst authorship.*Correspondence:Dong-yue Wen844653793@qq.comYun Heheyun@stu.gxmu.edu.cnFull list of author information is available at the end of the articleAbstractObjectives To develop ultrasound-based radiomics models and a clinical model associated with inf l ammatory markers for predicting intrahepatic cholangiocarcinoma (ICC) lymph node (LN) metastasis. Both are integrated for enhanced preoperative prediction.Methods This study retrospectively enrolled 156 surgically diagnosed ICC patients. A region of interest (ROI) was manually identif i ed on the ultrasound image of the tumor to extract radiomics features. In the training cohort, we performed a Wilcoxon test to screen for dif f erentially expressed features, and then we used 12 machine learning algorithms to develop 107 models within the cross-validation framework and determine the optimal radiomics model through receiver operating characteristic (ROC) curve analysis. Multivariable logistic regression analysis was used to identify independent risk factors to construct a clinical model. The combined model was established by combining ultrasound-based radiomics and clinical parameters. The Delong test and decision curve analysis (DCA) were used to compare the diagnostic ef f i cacy and clinical utility of dif f erent models.Results A total of 1239 radiomics features were extracted from the ROIs of tumors. Among the 107 prediction models, the model (Stepglm + LASSO) utilizing 10 radiomics features ultimately yielded the highest average area under the receiver operating characteristic curve (AUC) of 0.872, with an AUC of 0.916 in the training cohort and 0.827 in the validation cohort. The combined model, which incorporates the optimal radiomics score, clinical N stage, and platelet-to-lymphocyte ratio (PLR), achieved an AUC of 0.882 in the validation cohort, signif i cantly outperforming Preoperative prediction of lymph node metastasis in intrahepatic cholangiocarcinoma: an integrative approach combining ultrasound-based radiomics and inf l ammation-related markersYu-ting Peng 1† , Jin-shu Pang 1† , Peng Lin 2 , Jia-min Chen 1 , Rong Wen 1 , Chang-wen Liu 1 , Zhi-yuan Wen 1 , Yu-quan Wu 1 , Jin-bo Peng 1 , Lu Zhang 3 , Hong Yang 1 , Dong-yue Wen 1* and Yun He 1*