A radiomics-clinical predictive model for difficult laparoscopic cholecystectomy based on preoperative CT imaging: a retrospective single center study
Abstract Background Accurately identifying difficult laparoscopic cholecystectomy (DLC) preoperatively remains a clinical challenge. Previous studies utilizing clinical variables or morphological imaging markers have demonstrated suboptimal predictive performance. This study aims to develop an optim...
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BMC
2025-07-01
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| Series: | World Journal of Emergency Surgery |
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| Online Access: | https://doi.org/10.1186/s13017-025-00635-1 |
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| author | Rui-Tao Sun Chang-Lei Li Yu-Min Jiang Ao-Yun Hao Kui Liu Kun Li Bin Tan Xiao-Nan Yang Jiu-Fa Cui Wen-Ye Bai Wei-Yu Hu Jing-Yu Cao Chao Qu |
| author_facet | Rui-Tao Sun Chang-Lei Li Yu-Min Jiang Ao-Yun Hao Kui Liu Kun Li Bin Tan Xiao-Nan Yang Jiu-Fa Cui Wen-Ye Bai Wei-Yu Hu Jing-Yu Cao Chao Qu |
| author_sort | Rui-Tao Sun |
| collection | DOAJ |
| description | Abstract Background Accurately identifying difficult laparoscopic cholecystectomy (DLC) preoperatively remains a clinical challenge. Previous studies utilizing clinical variables or morphological imaging markers have demonstrated suboptimal predictive performance. This study aims to develop an optimal radiomics-clinical model by integrating preoperative CT-based radiomics features with clinical characteristics. Methods A retrospective analysis was conducted on 2,055 patients who underwent laparoscopic cholecystectomy (LC) for cholecystitis at our center. Preoperative CT images were processed with super-resolution reconstruction to improve consistency, and high-throughput radiomic features were extracted from the gallbladder wall region. A combination of radiomic and clinical features was selected using the Boruta-LASSO algorithm. Predictive models were constructed using six machine learning algorithms and validated, with model performance evaluated based on the AUC, accuracy, Brier score, and DCA to identify the optimal model. Model interpretability was further enhanced using the SHAP method. Results The Boruta-LASSO algorithm identified 10 key radiomic and clinical features for model construction, including the Rad-Score, gallbladder wall thickness, fibrinogen, C-reactive protein, and low-density lipoprotein cholesterol. Among the six machine learning models developed, the radiomics-clinical model based on the random forest algorithm demonstrated the best predictive performance, with an AUC of 0.938 in the training cohort and 0.874 in the validation cohort. The Brier score, calibration curve, and DCA confirmed the superior predictive capability of this model, significantly outperforming previously published models. The SHAP analysis further visualized the importance of features, enhancing model interpretability. Conclusion This study developed the first radiomics-clinical random forest model for the preoperative prediction of DLC by machine learning algorithms. This predictive model supports safer and individualized surgical planning and treatment strategies. |
| format | Article |
| id | doaj-art-84b6d1a764cd4fa288358def4477c04f |
| institution | Kabale University |
| issn | 1749-7922 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | World Journal of Emergency Surgery |
| spelling | doaj-art-84b6d1a764cd4fa288358def4477c04f2025-08-20T03:46:03ZengBMCWorld Journal of Emergency Surgery1749-79222025-07-0120111710.1186/s13017-025-00635-1A radiomics-clinical predictive model for difficult laparoscopic cholecystectomy based on preoperative CT imaging: a retrospective single center studyRui-Tao Sun0Chang-Lei Li1Yu-Min Jiang2Ao-Yun Hao3Kui Liu4Kun Li5Bin Tan6Xiao-Nan Yang7Jiu-Fa Cui8Wen-Ye Bai9Wei-Yu Hu10Jing-Yu Cao11Chao Qu12Department of Hepatobiliary and Pancreatic Surgery, The Affiliated Hospital of Qingdao UniversityDepartment of Hepatobiliary and Pancreatic Surgery, The Affiliated Hospital of Qingdao UniversityDepartment of Hepatobiliary and Pancreatic Surgery, The Affiliated Hospital of Qingdao UniversityDepartment of Hepatobiliary and Pancreatic Surgery, The Affiliated Hospital of Qingdao UniversityDepartment of Hepatobiliary and Pancreatic Surgery, The Affiliated Hospital of Qingdao UniversityDepartment of Hepatobiliary and Pancreatic Surgery, The Affiliated Hospital of Qingdao UniversityDepartment of Hepatobiliary and Pancreatic Surgery, The Affiliated Hospital of Qingdao UniversityDepartment of Radiology, The Affiliated Hospital of Qingdao UniversityDepartment of Radiology, The Affiliated Hospital of Qingdao UniversityDepartment of Hepatobiliary and Pancreatic Surgery, The Affiliated Hospital of Qingdao UniversityDepartment of Hepatobiliary and Pancreatic Surgery, The Affiliated Hospital of Qingdao UniversityDepartment of Hepatobiliary and Pancreatic Surgery, The Affiliated Hospital of Qingdao UniversityDepartment of Hepatobiliary and Pancreatic Surgery, The Affiliated Hospital of Qingdao UniversityAbstract Background Accurately identifying difficult laparoscopic cholecystectomy (DLC) preoperatively remains a clinical challenge. Previous studies utilizing clinical variables or morphological imaging markers have demonstrated suboptimal predictive performance. This study aims to develop an optimal radiomics-clinical model by integrating preoperative CT-based radiomics features with clinical characteristics. Methods A retrospective analysis was conducted on 2,055 patients who underwent laparoscopic cholecystectomy (LC) for cholecystitis at our center. Preoperative CT images were processed with super-resolution reconstruction to improve consistency, and high-throughput radiomic features were extracted from the gallbladder wall region. A combination of radiomic and clinical features was selected using the Boruta-LASSO algorithm. Predictive models were constructed using six machine learning algorithms and validated, with model performance evaluated based on the AUC, accuracy, Brier score, and DCA to identify the optimal model. Model interpretability was further enhanced using the SHAP method. Results The Boruta-LASSO algorithm identified 10 key radiomic and clinical features for model construction, including the Rad-Score, gallbladder wall thickness, fibrinogen, C-reactive protein, and low-density lipoprotein cholesterol. Among the six machine learning models developed, the radiomics-clinical model based on the random forest algorithm demonstrated the best predictive performance, with an AUC of 0.938 in the training cohort and 0.874 in the validation cohort. The Brier score, calibration curve, and DCA confirmed the superior predictive capability of this model, significantly outperforming previously published models. The SHAP analysis further visualized the importance of features, enhancing model interpretability. Conclusion This study developed the first radiomics-clinical random forest model for the preoperative prediction of DLC by machine learning algorithms. This predictive model supports safer and individualized surgical planning and treatment strategies.https://doi.org/10.1186/s13017-025-00635-1Laparoscopic cholecystectomyRadiomicsMachine learningSurgical difficulty prediction |
| spellingShingle | Rui-Tao Sun Chang-Lei Li Yu-Min Jiang Ao-Yun Hao Kui Liu Kun Li Bin Tan Xiao-Nan Yang Jiu-Fa Cui Wen-Ye Bai Wei-Yu Hu Jing-Yu Cao Chao Qu A radiomics-clinical predictive model for difficult laparoscopic cholecystectomy based on preoperative CT imaging: a retrospective single center study World Journal of Emergency Surgery Laparoscopic cholecystectomy Radiomics Machine learning Surgical difficulty prediction |
| title | A radiomics-clinical predictive model for difficult laparoscopic cholecystectomy based on preoperative CT imaging: a retrospective single center study |
| title_full | A radiomics-clinical predictive model for difficult laparoscopic cholecystectomy based on preoperative CT imaging: a retrospective single center study |
| title_fullStr | A radiomics-clinical predictive model for difficult laparoscopic cholecystectomy based on preoperative CT imaging: a retrospective single center study |
| title_full_unstemmed | A radiomics-clinical predictive model for difficult laparoscopic cholecystectomy based on preoperative CT imaging: a retrospective single center study |
| title_short | A radiomics-clinical predictive model for difficult laparoscopic cholecystectomy based on preoperative CT imaging: a retrospective single center study |
| title_sort | radiomics clinical predictive model for difficult laparoscopic cholecystectomy based on preoperative ct imaging a retrospective single center study |
| topic | Laparoscopic cholecystectomy Radiomics Machine learning Surgical difficulty prediction |
| url | https://doi.org/10.1186/s13017-025-00635-1 |
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