Preoperative prediction of lymph node metastasis in intrahepatic cholangiocarcinoma: an integrative approach combining ultrasound-based radiomics and inflammation-related markers
Abstract Objectives To develop ultrasound-based radiomics models and a clinical model associated with inflammatory markers for predicting intrahepatic cholangiocarcinoma (ICC) lymph node (LN) metastasis. Both are integrated for enhanced preoperative prediction. Methods This study retrospectively enr...
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| Language: | English |
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BMC
2025-01-01
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| Series: | BMC Medical Imaging |
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| Online Access: | https://doi.org/10.1186/s12880-024-01542-8 |
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| author | Yu-ting Peng Jin-shu Pang Peng Lin Jia-min Chen Rong Wen Chang-wen Liu Zhi-yuan Wen Yu-quan Wu Jin-bo Peng Lu Zhang Hong Yang Dong-yue Wen Yun He |
| author_facet | Yu-ting Peng Jin-shu Pang Peng Lin Jia-min Chen Rong Wen Chang-wen Liu Zhi-yuan Wen Yu-quan Wu Jin-bo Peng Lu Zhang Hong Yang Dong-yue Wen Yun He |
| author_sort | Yu-ting Peng |
| collection | DOAJ |
| description | Abstract Objectives To develop ultrasound-based radiomics models and a clinical model associated with inflammatory 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 identified on the ultrasound image of the tumor to extract radiomics features. In the training cohort, we performed a Wilcoxon test to screen for differentially 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 efficacy and clinical utility of different 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, significantly outperforming the clinical model with an AUC of 0.687 (P = 0.009). According to the DCA analysis, the combined model also showed better clinical benefits. Conclusions The combined model incorporating ultrasound-based radiomics features and the PLR marker offers an effective, noninvasive intelligence-assisted tool for preoperative LN metastasis prediction in ICC patients. Clinical trial number Not applicable. |
| format | Article |
| id | doaj-art-565477ca2175411fac53dd909d79dec1 |
| institution | Kabale University |
| issn | 1471-2342 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Imaging |
| spelling | doaj-art-565477ca2175411fac53dd909d79dec12025-01-05T12:50:06ZengBMCBMC Medical Imaging1471-23422025-01-0125111610.1186/s12880-024-01542-8Preoperative prediction of lymph node metastasis in intrahepatic cholangiocarcinoma: an integrative approach combining ultrasound-based radiomics and inflammation-related markersYu-ting Peng0Jin-shu Pang1Peng Lin2Jia-min Chen3Rong Wen4Chang-wen Liu5Zhi-yuan Wen6Yu-quan Wu7Jin-bo Peng8Lu Zhang9Hong Yang10Dong-yue Wen11Yun He12Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical UniversityDepartment of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical UniversityDepartment of Medical Ultrasound, Fujian Medical University Union HospitalDepartment of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical UniversityDepartment of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical UniversityDepartment of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical UniversityDepartment of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical UniversityDepartment of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical UniversityDepartment of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical UniversityDepartment of Medical Pathology, The First Affiliated Hospital of Guangxi Medical UniversityDepartment of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical UniversityDepartment of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical UniversityDepartment of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical UniversityAbstract Objectives To develop ultrasound-based radiomics models and a clinical model associated with inflammatory 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 identified on the ultrasound image of the tumor to extract radiomics features. In the training cohort, we performed a Wilcoxon test to screen for differentially 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 efficacy and clinical utility of different 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, significantly outperforming the clinical model with an AUC of 0.687 (P = 0.009). According to the DCA analysis, the combined model also showed better clinical benefits. Conclusions The combined model incorporating ultrasound-based radiomics features and the PLR marker offers an effective, noninvasive intelligence-assisted tool for preoperative LN metastasis prediction in ICC patients. Clinical trial number Not applicable.https://doi.org/10.1186/s12880-024-01542-8Intrahepatic cholangiocarcinomaLymph node metastasisRadiomicsUltrasoundInflammation-related marker |
| spellingShingle | Yu-ting Peng Jin-shu Pang Peng Lin Jia-min Chen Rong Wen Chang-wen Liu Zhi-yuan Wen Yu-quan Wu Jin-bo Peng Lu Zhang Hong Yang Dong-yue Wen Yun He Preoperative prediction of lymph node metastasis in intrahepatic cholangiocarcinoma: an integrative approach combining ultrasound-based radiomics and inflammation-related markers BMC Medical Imaging Intrahepatic cholangiocarcinoma Lymph node metastasis Radiomics Ultrasound Inflammation-related marker |
| title | Preoperative prediction of lymph node metastasis in intrahepatic cholangiocarcinoma: an integrative approach combining ultrasound-based radiomics and inflammation-related markers |
| title_full | Preoperative prediction of lymph node metastasis in intrahepatic cholangiocarcinoma: an integrative approach combining ultrasound-based radiomics and inflammation-related markers |
| title_fullStr | Preoperative prediction of lymph node metastasis in intrahepatic cholangiocarcinoma: an integrative approach combining ultrasound-based radiomics and inflammation-related markers |
| title_full_unstemmed | Preoperative prediction of lymph node metastasis in intrahepatic cholangiocarcinoma: an integrative approach combining ultrasound-based radiomics and inflammation-related markers |
| title_short | Preoperative prediction of lymph node metastasis in intrahepatic cholangiocarcinoma: an integrative approach combining ultrasound-based radiomics and inflammation-related markers |
| title_sort | preoperative prediction of lymph node metastasis in intrahepatic cholangiocarcinoma an integrative approach combining ultrasound based radiomics and inflammation related markers |
| topic | Intrahepatic cholangiocarcinoma Lymph node metastasis Radiomics Ultrasound Inflammation-related marker |
| url | https://doi.org/10.1186/s12880-024-01542-8 |
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