A radiomics-based model for predicting lymph nodes metastasis of pancreatic ductal adenocarcinoma: a multicenter study
Abstract Purpose To develop a radiomics model to predict lymph nodes metastasis (LNM) in patients with pancreatic ductal adenocarcinoma (PDAC) and assess its value for clinical management. Methods Patients with pathologically confirmed PDAC from four centers were retrospectively enrolled and split i...
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SpringerOpen
2025-06-01
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| Series: | Insights into Imaging |
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| Online Access: | https://doi.org/10.1186/s13244-025-02025-2 |
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| author | Liwen Zhu Ben Zhao Tianyi Xia Di Chang Cong Xia Mengqiu Liu Ridong Li Buyue Cao Yue Qiu Yaoyao Yu Shuwei Zhou Huayu Chen Wu Cai Zhimin Ding Chunqiang Lu Tianyu Tang Yang Song Yuancheng Wang Jing Ye Ying Liu Shenghong Ju |
| author_facet | Liwen Zhu Ben Zhao Tianyi Xia Di Chang Cong Xia Mengqiu Liu Ridong Li Buyue Cao Yue Qiu Yaoyao Yu Shuwei Zhou Huayu Chen Wu Cai Zhimin Ding Chunqiang Lu Tianyu Tang Yang Song Yuancheng Wang Jing Ye Ying Liu Shenghong Ju |
| author_sort | Liwen Zhu |
| collection | DOAJ |
| description | Abstract Purpose To develop a radiomics model to predict lymph nodes metastasis (LNM) in patients with pancreatic ductal adenocarcinoma (PDAC) and assess its value for clinical management. Methods Patients with pathologically confirmed PDAC from four centers were retrospectively enrolled and split into four cohorts: training (n = 192), validation (n = 82), testing (n = 100), and clinical utilization (n = 163). A radiomics model was constructed based on contrast-enhanced CT (CECT) to predict LNM, and its performance was evaluated using the areas under the curve (AUC). Kaplan–Meier analysis was used to assess the prognostic and therapeutic decision-assisting value of the radiomics model. Results A total of 437 patients (mean age: 63.1 years ± 9.2 standard deviation; 253 men) were included. The radiomics model outperformed other models with AUCs of 0.84, 0.82, and 0.78 in the training, validation, and testing cohorts (all p < 0.05), respectively. LNM predicted by the radiomics model was significantly associated with overall survival (p < 0.001). Kaplan–Meier analysis revealed that patients with a higher risk of LNM also had worse outcomes (all p < 0.05). Additionally, among the high-risk subgroup identified by the radiomics model in the clinical utilization cohort, patients who underwent dissection of ≥ 15 lymph nodes exhibited better overall survival compared to those with fewer lymph nodes dissected (p = 0.002). Conclusion The radiomics model we constructed demonstrated impressive performance in predicting LNM and prognosis, suggesting its potential for optimizing the clinical management of PDAC. Critical relevance statement This radiomics model can predict the risk of lymph nodes metastasis and prognosis of patients in pancreatic ductal adenocarcinoma and has potential value in selecting patients who can benefit from different extents of lymph nodes dissection. Key Points Thorough lymph node dissection is important for achieving the best prognosis in pancreatic ductal adenocarcinoma (PDAC). The radiomics model can accurately predict lymph node status and stratify patients’ prognosis. This radiomics model enhances the clinical management of PDAC. Graphical Abstract |
| format | Article |
| id | doaj-art-5b76751a8be54a44aa20e490b85e5853 |
| institution | OA Journals |
| issn | 1869-4101 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | SpringerOpen |
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| series | Insights into Imaging |
| spelling | doaj-art-5b76751a8be54a44aa20e490b85e58532025-08-20T02:37:58ZengSpringerOpenInsights into Imaging1869-41012025-06-0116111110.1186/s13244-025-02025-2A radiomics-based model for predicting lymph nodes metastasis of pancreatic ductal adenocarcinoma: a multicenter studyLiwen Zhu0Ben Zhao1Tianyi Xia2Di Chang3Cong Xia4Mengqiu Liu5Ridong Li6Buyue Cao7Yue Qiu8Yaoyao Yu9Shuwei Zhou10Huayu Chen11Wu Cai12Zhimin Ding13Chunqiang Lu14Tianyu Tang15Yang Song16Yuancheng Wang17Jing Ye18Ying Liu19Shenghong Ju20Nurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, Department of Radiology, Zhongda Hospital, Medical School of Southeast UniversityNurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, Department of Radiology, Zhongda Hospital, Medical School of Southeast UniversityNurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, Department of Radiology, Zhongda Hospital, Medical School of Southeast UniversityNurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, Department of Radiology, Zhongda Hospital, Medical School of Southeast UniversityNurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, Department of Radiology, Zhongda Hospital, Medical School of Southeast UniversityDepartment of Radiology, The First Affiliated Hospital of University of Science and Technology of ChinaDepartment of Radiology, Northern Jiangsu People’s HospitalNurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, Department of Radiology, Zhongda Hospital, Medical School of Southeast UniversityNurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, Department of Radiology, Zhongda Hospital, Medical School of Southeast UniversityNurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, Department of Radiology, Zhongda Hospital, Medical School of Southeast UniversityNurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, Department of Radiology, Zhongda Hospital, Medical School of Southeast UniversityNurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, Department of Radiology, Zhongda Hospital, Medical School of Southeast UniversityDepartment of Radiology, The Second Affiliated Hospital of Soochow UniversityDepartment of Radiology, Yijishan Hospital of Wannan Medical CollegeNurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, Department of Radiology, Zhongda Hospital, Medical School of Southeast UniversityNurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, Department of Radiology, Zhongda Hospital, Medical School of Southeast UniversityMR Scientific Marketing, Siemens HealthineersNurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, Department of Radiology, Zhongda Hospital, Medical School of Southeast UniversityDepartment of Radiology, Northern Jiangsu People’s HospitalDepartment of Radiology, The First Affiliated Hospital of University of Science and Technology of ChinaNurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, Department of Radiology, Zhongda Hospital, Medical School of Southeast UniversityAbstract Purpose To develop a radiomics model to predict lymph nodes metastasis (LNM) in patients with pancreatic ductal adenocarcinoma (PDAC) and assess its value for clinical management. Methods Patients with pathologically confirmed PDAC from four centers were retrospectively enrolled and split into four cohorts: training (n = 192), validation (n = 82), testing (n = 100), and clinical utilization (n = 163). A radiomics model was constructed based on contrast-enhanced CT (CECT) to predict LNM, and its performance was evaluated using the areas under the curve (AUC). Kaplan–Meier analysis was used to assess the prognostic and therapeutic decision-assisting value of the radiomics model. Results A total of 437 patients (mean age: 63.1 years ± 9.2 standard deviation; 253 men) were included. The radiomics model outperformed other models with AUCs of 0.84, 0.82, and 0.78 in the training, validation, and testing cohorts (all p < 0.05), respectively. LNM predicted by the radiomics model was significantly associated with overall survival (p < 0.001). Kaplan–Meier analysis revealed that patients with a higher risk of LNM also had worse outcomes (all p < 0.05). Additionally, among the high-risk subgroup identified by the radiomics model in the clinical utilization cohort, patients who underwent dissection of ≥ 15 lymph nodes exhibited better overall survival compared to those with fewer lymph nodes dissected (p = 0.002). Conclusion The radiomics model we constructed demonstrated impressive performance in predicting LNM and prognosis, suggesting its potential for optimizing the clinical management of PDAC. Critical relevance statement This radiomics model can predict the risk of lymph nodes metastasis and prognosis of patients in pancreatic ductal adenocarcinoma and has potential value in selecting patients who can benefit from different extents of lymph nodes dissection. Key Points Thorough lymph node dissection is important for achieving the best prognosis in pancreatic ductal adenocarcinoma (PDAC). The radiomics model can accurately predict lymph node status and stratify patients’ prognosis. This radiomics model enhances the clinical management of PDAC. Graphical Abstracthttps://doi.org/10.1186/s13244-025-02025-2Pancreatic ductal adenocarcinomaLymph nodes metastasisRadiomicsComputed tomographyPrognosis |
| spellingShingle | Liwen Zhu Ben Zhao Tianyi Xia Di Chang Cong Xia Mengqiu Liu Ridong Li Buyue Cao Yue Qiu Yaoyao Yu Shuwei Zhou Huayu Chen Wu Cai Zhimin Ding Chunqiang Lu Tianyu Tang Yang Song Yuancheng Wang Jing Ye Ying Liu Shenghong Ju A radiomics-based model for predicting lymph nodes metastasis of pancreatic ductal adenocarcinoma: a multicenter study Insights into Imaging Pancreatic ductal adenocarcinoma Lymph nodes metastasis Radiomics Computed tomography Prognosis |
| title | A radiomics-based model for predicting lymph nodes metastasis of pancreatic ductal adenocarcinoma: a multicenter study |
| title_full | A radiomics-based model for predicting lymph nodes metastasis of pancreatic ductal adenocarcinoma: a multicenter study |
| title_fullStr | A radiomics-based model for predicting lymph nodes metastasis of pancreatic ductal adenocarcinoma: a multicenter study |
| title_full_unstemmed | A radiomics-based model for predicting lymph nodes metastasis of pancreatic ductal adenocarcinoma: a multicenter study |
| title_short | A radiomics-based model for predicting lymph nodes metastasis of pancreatic ductal adenocarcinoma: a multicenter study |
| title_sort | radiomics based model for predicting lymph nodes metastasis of pancreatic ductal adenocarcinoma a multicenter study |
| topic | Pancreatic ductal adenocarcinoma Lymph nodes metastasis Radiomics Computed tomography Prognosis |
| url | https://doi.org/10.1186/s13244-025-02025-2 |
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