Identification of gastroenteropancreatic neuroendocrine tumor patients with high liver tumor burden based on clinicopathological features
Abstract Background Metastatic liver tumor burden (LTB) is a prognostic factor affecting the survival of gastroenteropancreatic neuroendocrine tumors (GEP-NETs), but evaluation of the LTB usually depends on radiologic and functional imaging. This study aimed to develop a clinical model based on easi...
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BMC
2025-07-01
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| Series: | BMC Cancer |
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| Online Access: | https://doi.org/10.1186/s12885-025-14535-9 |
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| author | Nuerailaguli Jumai Luohai Chen Xiaoxuan Lin Qiao He Man Liu Yuan Lin Yanji Luo Yu Wang Min-hu Chen Xiangsong Zhang Zhirong Zeng Ning Zhang |
| author_facet | Nuerailaguli Jumai Luohai Chen Xiaoxuan Lin Qiao He Man Liu Yuan Lin Yanji Luo Yu Wang Min-hu Chen Xiangsong Zhang Zhirong Zeng Ning Zhang |
| author_sort | Nuerailaguli Jumai |
| collection | DOAJ |
| description | Abstract Background Metastatic liver tumor burden (LTB) is a prognostic factor affecting the survival of gastroenteropancreatic neuroendocrine tumors (GEP-NETs), but evaluation of the LTB usually depends on radiologic and functional imaging. This study aimed to develop a clinical model based on easily accessible clinicopathological markers to predict LTB level in GEP-NET patients. Methods LTB was quantified based on 68Ga-DOTANOC PET/CT scan. The optimal cut-off value for high and low-LTB was determined based on our previous study. Serum levels of liver enzymes and tumor biomarkers were obtained within one week before PET/CT scan. The whole dataset was divided into training set and validation set. LASSO regression method was used to select predictors, and multivariate logistic regression was used to develop a clinical model which was further visualized by constructing a nomogram. Area under the curve (AUC) was applied to assess the accuracy of the constructed model. Results We retrospectively enrolled 200 patients with well-differentiated GEP-NETs. Ki-67 index, GGT (gamma-glutamyltransferase), LDH (lactate dehydrogenase), and NSE (neuron-specific enolase) were selected through the LASSO regression method, and a nomogram was built based on these variables. The predictive model yielded an AUC of 0.785 (95% CI, [0.708–0.862]) in the training set, and 0.783 (95% CI, [0.644–0.923]) in the validation set. Additionally, with the optimal cut-off values based on the nomogram total points, patients were categorized as LTBhigh (total points ≥ 26.2) and LTBlow (total points < 26.2) groups presenting significantly different OS. Conclusion A clinically applicable nomogram incorporating four clinicopathological characteristics was constructed to predict GEP-NET patients with high-LTB. The nomogram may help clinicians identify patients with high-LTB and take optimal treatment measures to improve prognosis. |
| format | Article |
| id | doaj-art-e641a5cecc0d48cb85f9d892a6cd4396 |
| institution | DOAJ |
| issn | 1471-2407 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Cancer |
| spelling | doaj-art-e641a5cecc0d48cb85f9d892a6cd43962025-08-20T03:05:03ZengBMCBMC Cancer1471-24072025-07-012511910.1186/s12885-025-14535-9Identification of gastroenteropancreatic neuroendocrine tumor patients with high liver tumor burden based on clinicopathological featuresNuerailaguli Jumai0Luohai Chen1Xiaoxuan Lin2Qiao He3Man Liu4Yuan Lin5Yanji Luo6Yu Wang7Min-hu Chen8Xiangsong Zhang9Zhirong Zeng10Ning Zhang11Department of Gastroenterology, The First Affiliated Hospital, Sun Yat-sen UniversityDepartment of Gastroenterology, The First Affiliated Hospital, Sun Yat-sen UniversityDepartment of Gastroenterology, The First Affiliated Hospital, Sun Yat-sen UniversityDepartment of Nuclear Medicine, The First Affiliated Hospital, Sun Yat-sen UniversityDepartment of Gastroenterology, The First Affiliated Hospital, Sun Yat-sen UniversityDepartment of Pathology, The First Affiliated Hospital, Sun Yat-sen UniversityDepartment of Radiology, The First Affiliated Hospital, Sun Yat-sen UniversityDepartment of Interventional Oncology, The First Affiliated Hospital, Sun Yat-sen UniversityDepartment of Gastroenterology, The First Affiliated Hospital, Sun Yat-sen UniversityDepartment of Nuclear Medicine, The First Affiliated Hospital, Sun Yat-sen UniversityDepartment of Gastroenterology, The First Affiliated Hospital, Sun Yat-sen UniversityDepartment of Gastroenterology, The First Affiliated Hospital, Sun Yat-sen UniversityAbstract Background Metastatic liver tumor burden (LTB) is a prognostic factor affecting the survival of gastroenteropancreatic neuroendocrine tumors (GEP-NETs), but evaluation of the LTB usually depends on radiologic and functional imaging. This study aimed to develop a clinical model based on easily accessible clinicopathological markers to predict LTB level in GEP-NET patients. Methods LTB was quantified based on 68Ga-DOTANOC PET/CT scan. The optimal cut-off value for high and low-LTB was determined based on our previous study. Serum levels of liver enzymes and tumor biomarkers were obtained within one week before PET/CT scan. The whole dataset was divided into training set and validation set. LASSO regression method was used to select predictors, and multivariate logistic regression was used to develop a clinical model which was further visualized by constructing a nomogram. Area under the curve (AUC) was applied to assess the accuracy of the constructed model. Results We retrospectively enrolled 200 patients with well-differentiated GEP-NETs. Ki-67 index, GGT (gamma-glutamyltransferase), LDH (lactate dehydrogenase), and NSE (neuron-specific enolase) were selected through the LASSO regression method, and a nomogram was built based on these variables. The predictive model yielded an AUC of 0.785 (95% CI, [0.708–0.862]) in the training set, and 0.783 (95% CI, [0.644–0.923]) in the validation set. Additionally, with the optimal cut-off values based on the nomogram total points, patients were categorized as LTBhigh (total points ≥ 26.2) and LTBlow (total points < 26.2) groups presenting significantly different OS. Conclusion A clinically applicable nomogram incorporating four clinicopathological characteristics was constructed to predict GEP-NET patients with high-LTB. The nomogram may help clinicians identify patients with high-LTB and take optimal treatment measures to improve prognosis.https://doi.org/10.1186/s12885-025-14535-9Neuroendocrine tumorsLiver metastasisTumor burdenPrognosis |
| spellingShingle | Nuerailaguli Jumai Luohai Chen Xiaoxuan Lin Qiao He Man Liu Yuan Lin Yanji Luo Yu Wang Min-hu Chen Xiangsong Zhang Zhirong Zeng Ning Zhang Identification of gastroenteropancreatic neuroendocrine tumor patients with high liver tumor burden based on clinicopathological features BMC Cancer Neuroendocrine tumors Liver metastasis Tumor burden Prognosis |
| title | Identification of gastroenteropancreatic neuroendocrine tumor patients with high liver tumor burden based on clinicopathological features |
| title_full | Identification of gastroenteropancreatic neuroendocrine tumor patients with high liver tumor burden based on clinicopathological features |
| title_fullStr | Identification of gastroenteropancreatic neuroendocrine tumor patients with high liver tumor burden based on clinicopathological features |
| title_full_unstemmed | Identification of gastroenteropancreatic neuroendocrine tumor patients with high liver tumor burden based on clinicopathological features |
| title_short | Identification of gastroenteropancreatic neuroendocrine tumor patients with high liver tumor burden based on clinicopathological features |
| title_sort | identification of gastroenteropancreatic neuroendocrine tumor patients with high liver tumor burden based on clinicopathological features |
| topic | Neuroendocrine tumors Liver metastasis Tumor burden Prognosis |
| url | https://doi.org/10.1186/s12885-025-14535-9 |
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