Deep learning radiomics nomogram for preoperatively identifying moderate-to-severe chronic cholangitis in children with pancreaticobiliary maljunction: a multicenter study
Abstract Background Long-term severe cholangitis can lead to dense adhesions and increased fragility of the bile duct, complicating surgical procedures and elevating operative risk in children with pancreaticobiliary maljunction (PBM). Consequently, preoperative diagnosis of moderate-to-severe chron...
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2025-02-01
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| Online Access: | https://doi.org/10.1186/s12880-025-01579-3 |
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| author | Hui-min Mao Kai-ge Chen Bin Zhu Wan-liang Guo San-li Shi |
| author_facet | Hui-min Mao Kai-ge Chen Bin Zhu Wan-liang Guo San-li Shi |
| author_sort | Hui-min Mao |
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| description | Abstract Background Long-term severe cholangitis can lead to dense adhesions and increased fragility of the bile duct, complicating surgical procedures and elevating operative risk in children with pancreaticobiliary maljunction (PBM). Consequently, preoperative diagnosis of moderate-to-severe chronic cholangitis is essential for guiding treatment strategies and surgical planning. This study aimed to develop and validate a deep learning radiomics nomogram (DLRN) based on contrast-enhanced CT images and clinical characteristics to preoperatively identify moderate-to-severe chronic cholangitis in children with PBM. Methods A total of 323 pediatric patients with PBM who underwent surgery were retrospectively enrolled from three centers, and divided into a training cohort (n = 153), an internal validation cohort (IVC, n = 67), and two external test cohorts (ETC1, n = 58; ETC2, n = 45). Chronic cholangitis severity was determined by postoperative pathology. Handcrafted radiomics features and deep learning (DL) radiomics features, extracted using transfer learning with the ResNet50 architecture, were obtained from portal venous-phase CT images. Multivariable logistic regression was used to establish the DLRN, integrating significant clinical factors with handcrafted and DL radiomics signatures. The diagnostic performances were evaluated in terms of discrimination, calibration, and clinical usefulness. Results Biliary stones and peribiliary fluid collection were selected as important clinical factors. 5 handcrafted and 5 DL features were retained to build the two radiomics signatures, respectively. The integrated DLRN achieved satisfactory performance, achieving area under the curve (AUC) values of 0.913 (95% CI, 0.834–0.993), 0.916 (95% CI, 0.845–0.987), and 0.895 (95% CI, 0.801–0.989) in the IVC, and two ETCs, respectively. In comparison, the clinical model, handcrafted signature, and DL signature had AUC ranges of 0.654–0.705, 0.823–0.857, and 0.840–0.872 across the same cohorts. The DLRN outperformed single-modality clinical, handcrafted radiomics, and DL radiomics models, with all integrated discrimination improvement values > 0 and P < 0.05. The Hosmer–Lemeshow test and calibration curves showed good consistency of the DLRN (P > 0.05), and the decision curve analysis and clinical impact curve further confirmed its clinical utility. Conclusions The integrated DLRN can be a useful and non-invasive tool for preoperatively identifying moderate-to-severe chronic cholangitis in children with PBM, potentially enhancing clinical decision-making and personalized management strategies. |
| format | Article |
| id | doaj-art-510d22bb7f944eb0847020cf9c279f78 |
| institution | Kabale University |
| issn | 1471-2342 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | BMC |
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| series | BMC Medical Imaging |
| spelling | doaj-art-510d22bb7f944eb0847020cf9c279f782025-02-09T13:00:01ZengBMCBMC Medical Imaging1471-23422025-02-0125111410.1186/s12880-025-01579-3Deep learning radiomics nomogram for preoperatively identifying moderate-to-severe chronic cholangitis in children with pancreaticobiliary maljunction: a multicenter studyHui-min Mao0Kai-ge Chen1Bin Zhu2Wan-liang Guo3San-li Shi4Department of Radiology, Children’s Hospital of Soochow UniversityDepartment of Ultrasound, Shandong Provincial Hospital Affiliated to Shandong First Medical UniversityDepartment of Interventional Therapy, Xuzhou Children’s HospitalDepartment of Radiology, Children’s Hospital of Soochow UniversityDepartment of Radiology, The 8th Hospital of Xi’anAbstract Background Long-term severe cholangitis can lead to dense adhesions and increased fragility of the bile duct, complicating surgical procedures and elevating operative risk in children with pancreaticobiliary maljunction (PBM). Consequently, preoperative diagnosis of moderate-to-severe chronic cholangitis is essential for guiding treatment strategies and surgical planning. This study aimed to develop and validate a deep learning radiomics nomogram (DLRN) based on contrast-enhanced CT images and clinical characteristics to preoperatively identify moderate-to-severe chronic cholangitis in children with PBM. Methods A total of 323 pediatric patients with PBM who underwent surgery were retrospectively enrolled from three centers, and divided into a training cohort (n = 153), an internal validation cohort (IVC, n = 67), and two external test cohorts (ETC1, n = 58; ETC2, n = 45). Chronic cholangitis severity was determined by postoperative pathology. Handcrafted radiomics features and deep learning (DL) radiomics features, extracted using transfer learning with the ResNet50 architecture, were obtained from portal venous-phase CT images. Multivariable logistic regression was used to establish the DLRN, integrating significant clinical factors with handcrafted and DL radiomics signatures. The diagnostic performances were evaluated in terms of discrimination, calibration, and clinical usefulness. Results Biliary stones and peribiliary fluid collection were selected as important clinical factors. 5 handcrafted and 5 DL features were retained to build the two radiomics signatures, respectively. The integrated DLRN achieved satisfactory performance, achieving area under the curve (AUC) values of 0.913 (95% CI, 0.834–0.993), 0.916 (95% CI, 0.845–0.987), and 0.895 (95% CI, 0.801–0.989) in the IVC, and two ETCs, respectively. In comparison, the clinical model, handcrafted signature, and DL signature had AUC ranges of 0.654–0.705, 0.823–0.857, and 0.840–0.872 across the same cohorts. The DLRN outperformed single-modality clinical, handcrafted radiomics, and DL radiomics models, with all integrated discrimination improvement values > 0 and P < 0.05. The Hosmer–Lemeshow test and calibration curves showed good consistency of the DLRN (P > 0.05), and the decision curve analysis and clinical impact curve further confirmed its clinical utility. Conclusions The integrated DLRN can be a useful and non-invasive tool for preoperatively identifying moderate-to-severe chronic cholangitis in children with PBM, potentially enhancing clinical decision-making and personalized management strategies.https://doi.org/10.1186/s12880-025-01579-3ChildrenChronic cholangitisDeep learningNomogramPancreaticobiliary maljunctionRadiomics |
| spellingShingle | Hui-min Mao Kai-ge Chen Bin Zhu Wan-liang Guo San-li Shi Deep learning radiomics nomogram for preoperatively identifying moderate-to-severe chronic cholangitis in children with pancreaticobiliary maljunction: a multicenter study BMC Medical Imaging Children Chronic cholangitis Deep learning Nomogram Pancreaticobiliary maljunction Radiomics |
| title | Deep learning radiomics nomogram for preoperatively identifying moderate-to-severe chronic cholangitis in children with pancreaticobiliary maljunction: a multicenter study |
| title_full | Deep learning radiomics nomogram for preoperatively identifying moderate-to-severe chronic cholangitis in children with pancreaticobiliary maljunction: a multicenter study |
| title_fullStr | Deep learning radiomics nomogram for preoperatively identifying moderate-to-severe chronic cholangitis in children with pancreaticobiliary maljunction: a multicenter study |
| title_full_unstemmed | Deep learning radiomics nomogram for preoperatively identifying moderate-to-severe chronic cholangitis in children with pancreaticobiliary maljunction: a multicenter study |
| title_short | Deep learning radiomics nomogram for preoperatively identifying moderate-to-severe chronic cholangitis in children with pancreaticobiliary maljunction: a multicenter study |
| title_sort | deep learning radiomics nomogram for preoperatively identifying moderate to severe chronic cholangitis in children with pancreaticobiliary maljunction a multicenter study |
| topic | Children Chronic cholangitis Deep learning Nomogram Pancreaticobiliary maljunction Radiomics |
| url | https://doi.org/10.1186/s12880-025-01579-3 |
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