Nomogram for preoperative estimation of histologic grade in gastrointestinal neuroendocrine tumors
BackgroundThe treatment strategies and prognosis for gastroenteropancreatic neuroendocrine tumors were associated with tumor grade. Preoperative predictive grading could be of great benefit in the selection of treatment options for patients. However, there is still a lack of effective non-invasive s...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2022-10-01
|
| Series: | Frontiers in Endocrinology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fendo.2022.991773/full |
| _version_ | 1811235479435083776 |
|---|---|
| author | Zhi-Qi Wu Zhi-Qi Wu Yan Li Na-Na Sun Qin Xu Jing Zhou Jing Zhou Kan-Kan Su Kan-Kan Su Hemant Goyal Hua-Guo Xu Hua-Guo Xu |
| author_facet | Zhi-Qi Wu Zhi-Qi Wu Yan Li Na-Na Sun Qin Xu Jing Zhou Jing Zhou Kan-Kan Su Kan-Kan Su Hemant Goyal Hua-Guo Xu Hua-Guo Xu |
| author_sort | Zhi-Qi Wu |
| collection | DOAJ |
| description | BackgroundThe treatment strategies and prognosis for gastroenteropancreatic neuroendocrine tumors were associated with tumor grade. Preoperative predictive grading could be of great benefit in the selection of treatment options for patients. However, there is still a lack of effective non-invasive strategies to detect gastrointestinal neuroendocrine tumors (GI-NETs) grading preoperatively.MethodsThe data on 147 consecutive GI-NETs patients was retrospectively collected from January 1, 2012, to December 31, 2019. Logistic regression was used to construct a predictive model of gastrointestinal neuroendocrine tumor grading using preoperative laboratory and imaging parameters.The validity of the model was assessed by area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA).ResultsThe factors associated with GI-NETs grading were age, tumor size, lymph nodes, neuron-specific enolase (NSE), hemoglobin (HGB) and sex, and two models were constructed by logistic regression for prediction. Combining these 6 factors, the nomogram was constructed for model 1 to distinguish between G3 and G1/2, achieving a good AUC of 0.921 (95% CI: 0.884-0.965), and the sensitivity, specificity, accuracy were 0.9167, 0.8256, 0.8630, respectively. The model 2 was to distinguish between G1 and G2/3, and the variables were age, tumor size, lymph nodes, NSE, with an AUC of 0.847 (95% CI: 0.799-0.915), and the sensitivity, specificity, accuracy were 0.7882, 0.8710, 0.8231, respectively. Two online web servers were established on the basis of the proposed nomogram to facilitate clinical use. Both models showed an excellent calibration curve through 1000 times bootstrapped dataset and the clinical usefulness were confirmed using decision curve analysis.ConclusionThe model served as a valuable non-invasive tool for differentiating between different grades of GI-NETs, personalizing the calculation which can lead to a rational treatment choice. |
| first_indexed | 2024-04-12T11:51:32Z |
| format | Article |
| id | doaj.art-6532db6bab7e4338956d01c316d4bfef |
| institution | Directory Open Access Journal |
| issn | 1664-2392 |
| language | English |
| last_indexed | 2024-04-12T11:51:32Z |
| publishDate | 2022-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Endocrinology |
| spelling | doaj.art-6532db6bab7e4338956d01c316d4bfef2022-12-22T03:34:09ZengFrontiers Media S.A.Frontiers in Endocrinology1664-23922022-10-011310.3389/fendo.2022.991773991773Nomogram for preoperative estimation of histologic grade in gastrointestinal neuroendocrine tumorsZhi-Qi Wu0Zhi-Qi Wu1Yan Li2Na-Na Sun3Qin Xu4Jing Zhou5Jing Zhou6Kan-Kan Su7Kan-Kan Su8Hemant Goyal9Hua-Guo Xu10Hua-Guo Xu11Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, ChinaBranch of National Clinical Research Center for Laboratory Medicine, Nanjing, Jiangsu, ChinaAcademy for Advanced Interdisciplinary Studies, Peking University, Peking, ChinaDepartment of Radiology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, ChinaDepartment of Laboratory Medicine, Jurong Hospital Affiliated to Jiangsu University, Jurong, ChinaDepartment of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, ChinaBranch of National Clinical Research Center for Laboratory Medicine, Nanjing, Jiangsu, ChinaDepartment of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, ChinaBranch of National Clinical Research Center for Laboratory Medicine, Nanjing, Jiangsu, ChinaDepartment of Internal Medicine, Mercer University School of Medicine, Macon, GA, United StatesDepartment of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, ChinaBranch of National Clinical Research Center for Laboratory Medicine, Nanjing, Jiangsu, ChinaBackgroundThe treatment strategies and prognosis for gastroenteropancreatic neuroendocrine tumors were associated with tumor grade. Preoperative predictive grading could be of great benefit in the selection of treatment options for patients. However, there is still a lack of effective non-invasive strategies to detect gastrointestinal neuroendocrine tumors (GI-NETs) grading preoperatively.MethodsThe data on 147 consecutive GI-NETs patients was retrospectively collected from January 1, 2012, to December 31, 2019. Logistic regression was used to construct a predictive model of gastrointestinal neuroendocrine tumor grading using preoperative laboratory and imaging parameters.The validity of the model was assessed by area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA).ResultsThe factors associated with GI-NETs grading were age, tumor size, lymph nodes, neuron-specific enolase (NSE), hemoglobin (HGB) and sex, and two models were constructed by logistic regression for prediction. Combining these 6 factors, the nomogram was constructed for model 1 to distinguish between G3 and G1/2, achieving a good AUC of 0.921 (95% CI: 0.884-0.965), and the sensitivity, specificity, accuracy were 0.9167, 0.8256, 0.8630, respectively. The model 2 was to distinguish between G1 and G2/3, and the variables were age, tumor size, lymph nodes, NSE, with an AUC of 0.847 (95% CI: 0.799-0.915), and the sensitivity, specificity, accuracy were 0.7882, 0.8710, 0.8231, respectively. Two online web servers were established on the basis of the proposed nomogram to facilitate clinical use. Both models showed an excellent calibration curve through 1000 times bootstrapped dataset and the clinical usefulness were confirmed using decision curve analysis.ConclusionThe model served as a valuable non-invasive tool for differentiating between different grades of GI-NETs, personalizing the calculation which can lead to a rational treatment choice.https://www.frontiersin.org/articles/10.3389/fendo.2022.991773/fullGI-NETsgradepreoperative estimationserum biomarkernomogram |
| spellingShingle | Zhi-Qi Wu Zhi-Qi Wu Yan Li Na-Na Sun Qin Xu Jing Zhou Jing Zhou Kan-Kan Su Kan-Kan Su Hemant Goyal Hua-Guo Xu Hua-Guo Xu Nomogram for preoperative estimation of histologic grade in gastrointestinal neuroendocrine tumors Frontiers in Endocrinology GI-NETs grade preoperative estimation serum biomarker nomogram |
| title | Nomogram for preoperative estimation of histologic grade in gastrointestinal neuroendocrine tumors |
| title_full | Nomogram for preoperative estimation of histologic grade in gastrointestinal neuroendocrine tumors |
| title_fullStr | Nomogram for preoperative estimation of histologic grade in gastrointestinal neuroendocrine tumors |
| title_full_unstemmed | Nomogram for preoperative estimation of histologic grade in gastrointestinal neuroendocrine tumors |
| title_short | Nomogram for preoperative estimation of histologic grade in gastrointestinal neuroendocrine tumors |
| title_sort | nomogram for preoperative estimation of histologic grade in gastrointestinal neuroendocrine tumors |
| topic | GI-NETs grade preoperative estimation serum biomarker nomogram |
| url | https://www.frontiersin.org/articles/10.3389/fendo.2022.991773/full |
| work_keys_str_mv | AT zhiqiwu nomogramforpreoperativeestimationofhistologicgradeingastrointestinalneuroendocrinetumors AT zhiqiwu nomogramforpreoperativeestimationofhistologicgradeingastrointestinalneuroendocrinetumors AT yanli nomogramforpreoperativeestimationofhistologicgradeingastrointestinalneuroendocrinetumors AT nanasun nomogramforpreoperativeestimationofhistologicgradeingastrointestinalneuroendocrinetumors AT qinxu nomogramforpreoperativeestimationofhistologicgradeingastrointestinalneuroendocrinetumors AT jingzhou nomogramforpreoperativeestimationofhistologicgradeingastrointestinalneuroendocrinetumors AT jingzhou nomogramforpreoperativeestimationofhistologicgradeingastrointestinalneuroendocrinetumors AT kankansu nomogramforpreoperativeestimationofhistologicgradeingastrointestinalneuroendocrinetumors AT kankansu nomogramforpreoperativeestimationofhistologicgradeingastrointestinalneuroendocrinetumors AT hemantgoyal nomogramforpreoperativeestimationofhistologicgradeingastrointestinalneuroendocrinetumors AT huaguoxu nomogramforpreoperativeestimationofhistologicgradeingastrointestinalneuroendocrinetumors AT huaguoxu nomogramforpreoperativeestimationofhistologicgradeingastrointestinalneuroendocrinetumors |