Preoperative prediction for early recurrence in patients with pancreatic ductal adenocarcinoma: combining radiomics and abdominal fat analysis
Abstract Background The role of radiomics and abdominal fat analysis in the survival prediction of pancreatic ductal adenocarcinoma (PDAC) has attracted attention. This study aims to develop a preoperative model for predicting early recurrence (ER) in patients pathologically confirmed PDAC, combinin...
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BMC
2025-07-01
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| Online Access: | https://doi.org/10.1186/s12880-025-01773-3 |
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| author | Suo Yu Yan Fang Ming Chen Bang Jun Guo Su Hu Li Lin Yi Wen Yang Xin Yu Jiang Hui Yao Chun Hong Hu Yun Yan Su |
| author_facet | Suo Yu Yan Fang Ming Chen Bang Jun Guo Su Hu Li Lin Yi Wen Yang Xin Yu Jiang Hui Yao Chun Hong Hu Yun Yan Su |
| author_sort | Suo Yu Yan |
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| description | Abstract Background The role of radiomics and abdominal fat analysis in the survival prediction of pancreatic ductal adenocarcinoma (PDAC) has attracted attention. This study aims to develop a preoperative model for predicting early recurrence (ER) in patients pathologically confirmed PDAC, combining radiomic and abdominal fat analysis. Methods A total of 177 patients (Hospital A) were retrospectively analyzed and allocated to the training cohort (n = 124) and internal validation cohort (n = 53). Another 71 patients (Hospital B) group formed the geographic external validation cohort. The threshold of ER was set at 6 months after surgery, and the primary endpoint was to determine the best model to predict ER of PDAC patients. A radiomics model for predicting ER was constructed by the least absolute shrinkage and selection operator Cox regression. Univariate and multivariate Cox regression analyses were used to build a combined model based on radiomics, fat quantitation, and clinical features. The combined model’s performance was assessed using the Harrell concordance index (C-index). Based on the nomogram score, patients were stratified into high-risk and low-risk groups, and survival analysis of different risk groups was performed using the Kaplan-Meier (KM) method. All patients were divided into four subgroups according to recurrence patterns: local recurrence subgroup, distant recurrence subgroup, “local + distant” recurrence subgroup, and “multiple” recurrence subgroup. The predictive efficacy of the combined model was calculated in different subgroups. Results Radiomics scores (P < 0.001), CA19-9 (P = 0.009), and visceral to subcutaneous fat volume ratio(P = 0.009) were selected for the combined model. Compared to clinical and radiomics models, the combined model exhibited the best prediction performance. C indexes of the training cohort, internal validation cohort, and external validation cohort were 0.778 (0.711,0.845), 0.746 (0.632,0.860), and 0.712 (0.612,0.812) respectively, showing the improvement over the clinical model (without radiomics and fat quantitation features) in the internal validation and external validation sets (DeLong test: P = 0.027, P = 0.079). KM analysis showed significant differences between risk groups (all P < 0.05). The combined model also achieved robust performance in different subgroups of recurrence patterns. Conclusion The combined model effectively predicted the probability of ER in PDAC patients and may provide an emerging tool to preoperatively guide personalized treatment. Clinical trial number Not applicable |
| format | Article |
| id | doaj-art-c1a32e9dbd354d90b48dc9a850cc084d |
| institution | Kabale University |
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| publishDate | 2025-07-01 |
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| series | BMC Medical Imaging |
| spelling | doaj-art-c1a32e9dbd354d90b48dc9a850cc084d2025-08-20T03:42:02ZengBMCBMC Medical Imaging1471-23422025-07-0125111210.1186/s12880-025-01773-3Preoperative prediction for early recurrence in patients with pancreatic ductal adenocarcinoma: combining radiomics and abdominal fat analysisSuo Yu Yan0Fang Ming Chen1Bang Jun Guo2Su Hu3Li Lin4Yi Wen Yang5Xin Yu Jiang6Hui Yao7Chun Hong Hu8Yun Yan Su9Department of Radiology, The First Affiliated Hospital of Soochow UniversityDepartment of Radiology, Jiangnan University Medical CenterDepartment of Radiology, The First Affiliated Hospital of Soochow UniversityDepartment of Radiology, The First Affiliated Hospital of Soochow UniversityDepartment of Radiology, The First Affiliated Hospital of Soochow UniversityDepartment of Radiology, The First Affiliated Hospital of Soochow UniversityDepartment of Radiology, The First Affiliated Hospital of Soochow UniversityDepartment of Radiology, The First Affiliated Hospital of Soochow UniversityDepartment of Radiology, The First Affiliated Hospital of Soochow UniversityDepartment of Radiology, The First Affiliated Hospital of Soochow UniversityAbstract Background The role of radiomics and abdominal fat analysis in the survival prediction of pancreatic ductal adenocarcinoma (PDAC) has attracted attention. This study aims to develop a preoperative model for predicting early recurrence (ER) in patients pathologically confirmed PDAC, combining radiomic and abdominal fat analysis. Methods A total of 177 patients (Hospital A) were retrospectively analyzed and allocated to the training cohort (n = 124) and internal validation cohort (n = 53). Another 71 patients (Hospital B) group formed the geographic external validation cohort. The threshold of ER was set at 6 months after surgery, and the primary endpoint was to determine the best model to predict ER of PDAC patients. A radiomics model for predicting ER was constructed by the least absolute shrinkage and selection operator Cox regression. Univariate and multivariate Cox regression analyses were used to build a combined model based on radiomics, fat quantitation, and clinical features. The combined model’s performance was assessed using the Harrell concordance index (C-index). Based on the nomogram score, patients were stratified into high-risk and low-risk groups, and survival analysis of different risk groups was performed using the Kaplan-Meier (KM) method. All patients were divided into four subgroups according to recurrence patterns: local recurrence subgroup, distant recurrence subgroup, “local + distant” recurrence subgroup, and “multiple” recurrence subgroup. The predictive efficacy of the combined model was calculated in different subgroups. Results Radiomics scores (P < 0.001), CA19-9 (P = 0.009), and visceral to subcutaneous fat volume ratio(P = 0.009) were selected for the combined model. Compared to clinical and radiomics models, the combined model exhibited the best prediction performance. C indexes of the training cohort, internal validation cohort, and external validation cohort were 0.778 (0.711,0.845), 0.746 (0.632,0.860), and 0.712 (0.612,0.812) respectively, showing the improvement over the clinical model (without radiomics and fat quantitation features) in the internal validation and external validation sets (DeLong test: P = 0.027, P = 0.079). KM analysis showed significant differences between risk groups (all P < 0.05). The combined model also achieved robust performance in different subgroups of recurrence patterns. Conclusion The combined model effectively predicted the probability of ER in PDAC patients and may provide an emerging tool to preoperatively guide personalized treatment. Clinical trial number Not applicablehttps://doi.org/10.1186/s12880-025-01773-3Pancreatic cancerComputed tomographyRecurrenceAbdominal fatRadiomics |
| spellingShingle | Suo Yu Yan Fang Ming Chen Bang Jun Guo Su Hu Li Lin Yi Wen Yang Xin Yu Jiang Hui Yao Chun Hong Hu Yun Yan Su Preoperative prediction for early recurrence in patients with pancreatic ductal adenocarcinoma: combining radiomics and abdominal fat analysis BMC Medical Imaging Pancreatic cancer Computed tomography Recurrence Abdominal fat Radiomics |
| title | Preoperative prediction for early recurrence in patients with pancreatic ductal adenocarcinoma: combining radiomics and abdominal fat analysis |
| title_full | Preoperative prediction for early recurrence in patients with pancreatic ductal adenocarcinoma: combining radiomics and abdominal fat analysis |
| title_fullStr | Preoperative prediction for early recurrence in patients with pancreatic ductal adenocarcinoma: combining radiomics and abdominal fat analysis |
| title_full_unstemmed | Preoperative prediction for early recurrence in patients with pancreatic ductal adenocarcinoma: combining radiomics and abdominal fat analysis |
| title_short | Preoperative prediction for early recurrence in patients with pancreatic ductal adenocarcinoma: combining radiomics and abdominal fat analysis |
| title_sort | preoperative prediction for early recurrence in patients with pancreatic ductal adenocarcinoma combining radiomics and abdominal fat analysis |
| topic | Pancreatic cancer Computed tomography Recurrence Abdominal fat Radiomics |
| url | https://doi.org/10.1186/s12880-025-01773-3 |
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