A logistic regression model to predict long-term survival for borderline resectable pancreatic cancer patients with upfront surgery
Abstract Background The machine learning model, which has been widely applied in prognosis assessment, can comprehensively evaluate patient status for accurate prognosis classification. There still has been a debate about which predictive strategy is better in patients with borderline resectable pan...
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BMC
2025-02-01
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| Series: | Cancer Imaging |
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| Online Access: | https://doi.org/10.1186/s40644-025-00830-y |
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| author | Jin-Can Huang Shao-Cheng Lyu Bing Pan Han-Xuan Wang You-Wei Ma Tao Jiang Qiang He Ren Lang |
| author_facet | Jin-Can Huang Shao-Cheng Lyu Bing Pan Han-Xuan Wang You-Wei Ma Tao Jiang Qiang He Ren Lang |
| author_sort | Jin-Can Huang |
| collection | DOAJ |
| description | Abstract Background The machine learning model, which has been widely applied in prognosis assessment, can comprehensively evaluate patient status for accurate prognosis classification. There still has been a debate about which predictive strategy is better in patients with borderline resectable pancreatic cancer (BRPC). In the present study, we establish a logistic regression model, aiming to predict long-term survival and identify related prognostic factors in patients with BRPC who underwent upfront surgery. Methods Medical records of patients with BRPC who underwent upfront surgery with portal vein resection and reconstruction from Jan. 2011 to Dec. 2020 were reviewed. Based on postoperative overall survival (OS), patients were divided into the short-term group (≤ 2 years) and the long-term group (> 2 years). Univariate and multivariate analyses were performed to compare perioperative variables and long-term prognoses between groups to identify related independent prognostic factors. All patients are randomly divided into the training set and the validation set at a 7:3 ratio. The logistic regression model was established and evaluated for accuracy through the above variables in the training set and the validation set, respectively, and was visualized by Nomograms. Meanwhile, the model was further verified and compared for accuracy, the area under the curve (AUC) of the receiver operating characteristic curves (ROC), and calibration analysis. Then, we plotted and sorted perioperative variables by SHAP value to identify the most important variables. The first 4 most important variables were compared with the above independent prognostic factors. Finally, other models including support vector machines (SVM), random forest, decision tree, and XGBoost were also constructed using the above 4 variables. 10-fold stratified cross-validation and the AUC of ROC were performed to compare accuracy between models. Results 104 patients were enrolled in the study, and the median OS was 15.5 months, the 0.5-, 1-, and 2- years OS were 81.7%, 57.7%, and 30.8%, respectively. In the long-term group (n = 32) and short-term group (n = 72), the overall median survival time and the 1-, 2-, 3- years overall survival were 38 months, 100%, 100%, 61.3% and 10 months, 38.9%, 0%, 0%, respectively. 4 variables, including age, vascular invasion length, vascular morphological malformation, and local lymphadenopathy were confirmed as independent risk factors between the two groups following univariate and multivariate analysis. The AUC between the training set (n = 72) and the validation set (n = 32) were 0.881 and 0.875. SHAP value showed that the above variables were the first 4 most important. The AUC following 10-fold stratified cross-validation in the logistic regression (0.864) is better than SVM (0.693), random forest (0.789), decision tree (0.790), and XGBoost (0.726). Conclusion Age, vascular invasion length, vascular morphological malformation, and local lymphadenopathy were independent risk factors for long-term survival of BRPC patients with upfront surgery. The logistic regression model plays a predictive role in long-term survival and may further assist surgeons in deciding the treatment option for BRPC patients. |
| format | Article |
| id | doaj-art-b725aff174174c95a04d81c59c8f3faf |
| institution | Kabale University |
| issn | 1470-7330 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | BMC |
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| series | Cancer Imaging |
| spelling | doaj-art-b725aff174174c95a04d81c59c8f3faf2025-02-09T12:52:44ZengBMCCancer Imaging1470-73302025-02-0125111310.1186/s40644-025-00830-yA logistic regression model to predict long-term survival for borderline resectable pancreatic cancer patients with upfront surgeryJin-Can Huang0Shao-Cheng Lyu1Bing Pan2Han-Xuan Wang3You-Wei Ma4Tao Jiang5Qiang He6Ren Lang7Hepatobiliary, Pancreas & Spleen Surgery Department, Beijing Chao-Yang Hospital, Capital Medical UniversityHepatobiliary, Pancreas & Spleen Surgery Department, Beijing Chao-Yang Hospital, Capital Medical UniversityHepatobiliary, Pancreas & Spleen Surgery Department, Beijing Chao-Yang Hospital, Capital Medical UniversityHepatobiliary, Pancreas & Spleen Surgery Department, Beijing Chao-Yang Hospital, Capital Medical UniversityHepatobiliary, Pancreas & Spleen Surgery Department, Beijing Chao-Yang Hospital, Capital Medical UniversityHepatobiliary, Pancreas & Spleen Surgery Department, Beijing Chao-Yang Hospital, Capital Medical UniversityHepatobiliary, Pancreas & Spleen Surgery Department, Beijing Chao-Yang Hospital, Capital Medical UniversityHepatobiliary, Pancreas & Spleen Surgery Department, Beijing Chao-Yang Hospital, Capital Medical UniversityAbstract Background The machine learning model, which has been widely applied in prognosis assessment, can comprehensively evaluate patient status for accurate prognosis classification. There still has been a debate about which predictive strategy is better in patients with borderline resectable pancreatic cancer (BRPC). In the present study, we establish a logistic regression model, aiming to predict long-term survival and identify related prognostic factors in patients with BRPC who underwent upfront surgery. Methods Medical records of patients with BRPC who underwent upfront surgery with portal vein resection and reconstruction from Jan. 2011 to Dec. 2020 were reviewed. Based on postoperative overall survival (OS), patients were divided into the short-term group (≤ 2 years) and the long-term group (> 2 years). Univariate and multivariate analyses were performed to compare perioperative variables and long-term prognoses between groups to identify related independent prognostic factors. All patients are randomly divided into the training set and the validation set at a 7:3 ratio. The logistic regression model was established and evaluated for accuracy through the above variables in the training set and the validation set, respectively, and was visualized by Nomograms. Meanwhile, the model was further verified and compared for accuracy, the area under the curve (AUC) of the receiver operating characteristic curves (ROC), and calibration analysis. Then, we plotted and sorted perioperative variables by SHAP value to identify the most important variables. The first 4 most important variables were compared with the above independent prognostic factors. Finally, other models including support vector machines (SVM), random forest, decision tree, and XGBoost were also constructed using the above 4 variables. 10-fold stratified cross-validation and the AUC of ROC were performed to compare accuracy between models. Results 104 patients were enrolled in the study, and the median OS was 15.5 months, the 0.5-, 1-, and 2- years OS were 81.7%, 57.7%, and 30.8%, respectively. In the long-term group (n = 32) and short-term group (n = 72), the overall median survival time and the 1-, 2-, 3- years overall survival were 38 months, 100%, 100%, 61.3% and 10 months, 38.9%, 0%, 0%, respectively. 4 variables, including age, vascular invasion length, vascular morphological malformation, and local lymphadenopathy were confirmed as independent risk factors between the two groups following univariate and multivariate analysis. The AUC between the training set (n = 72) and the validation set (n = 32) were 0.881 and 0.875. SHAP value showed that the above variables were the first 4 most important. The AUC following 10-fold stratified cross-validation in the logistic regression (0.864) is better than SVM (0.693), random forest (0.789), decision tree (0.790), and XGBoost (0.726). Conclusion Age, vascular invasion length, vascular morphological malformation, and local lymphadenopathy were independent risk factors for long-term survival of BRPC patients with upfront surgery. The logistic regression model plays a predictive role in long-term survival and may further assist surgeons in deciding the treatment option for BRPC patients.https://doi.org/10.1186/s40644-025-00830-yBorderline resectable pancreatic cancerRisk factorUpfront surgeryNomogramLogistic regression modelMachine learning |
| spellingShingle | Jin-Can Huang Shao-Cheng Lyu Bing Pan Han-Xuan Wang You-Wei Ma Tao Jiang Qiang He Ren Lang A logistic regression model to predict long-term survival for borderline resectable pancreatic cancer patients with upfront surgery Cancer Imaging Borderline resectable pancreatic cancer Risk factor Upfront surgery Nomogram Logistic regression model Machine learning |
| title | A logistic regression model to predict long-term survival for borderline resectable pancreatic cancer patients with upfront surgery |
| title_full | A logistic regression model to predict long-term survival for borderline resectable pancreatic cancer patients with upfront surgery |
| title_fullStr | A logistic regression model to predict long-term survival for borderline resectable pancreatic cancer patients with upfront surgery |
| title_full_unstemmed | A logistic regression model to predict long-term survival for borderline resectable pancreatic cancer patients with upfront surgery |
| title_short | A logistic regression model to predict long-term survival for borderline resectable pancreatic cancer patients with upfront surgery |
| title_sort | logistic regression model to predict long term survival for borderline resectable pancreatic cancer patients with upfront surgery |
| topic | Borderline resectable pancreatic cancer Risk factor Upfront surgery Nomogram Logistic regression model Machine learning |
| url | https://doi.org/10.1186/s40644-025-00830-y |
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