Risk Factor and Risk Prediction Modeling of Rectal Neuroendocrine Tumors
10.3971/j.issn.1000-8578.2025.24.1089
- VernacularTitle:直肠神经内分泌肿瘤的危险因素分析及风险预测模型建立
- Author:
Liang XIE
1
;
Chang LIU
1
;
Jianhua LI
2
;
Jianhui LI
3
;
Xin HAO
3
;
Haiyang HUA
3
Author Information
1. Chengde Medical University Graduate School, Chengde 067000, China.
2. Department of Breast, Thyroid and Rectal Surgery, The Second Clinical College of Chengde Medical College (Chengde Central Hospital), Chengde 067000, China.
3. Department of Gastroenterology, The Second Clinical College of Chengde Medical College (Chengde Central Hospital), Chengde 067000, China.
- Publication Type:CLINICALRESEARCH
- Keywords:
Rectal neuroendocrine tumors;
Risk factors;
Predictive model;
Nomogram
- From:
Cancer Research on Prevention and Treatment
2025;52(7):598-604
- CountryChina
- Language:Chinese
-
Abstract:
Objective To analyze the risk factors associated with the occurrence of rectal neuroendocrine tumors (RNETs) and construct a risk prediction model. Methods Clinical data of patients who underwent electronic colonoscopy were collected. The clinical information on patients with and without RNETs were compared, and potential risk factors for RNETs were identified. Binary logistic regression was performed to analyze the relevant risk factors and construct a risk prediction model. Results Among 164 patients, 66 were diagnosed with RNETs, and 98 who did not have such a condition were randomly selected. Univariate logistic regression analysis revealed that age, fatty liver, anxiety and depression, total cholesterol, triglyceride levels, and carcinoembryonic antigen (CEA) were significant factors influencing the occurrence of RNETs (P<0.05). Multivariate logistic regression analysis identified age (P=0.015), anxiety and depression (P=0.031), cholesterol level (P=0.009), fatty liver (P=0.001), and CEA (P<0.001) as independent risk factors for RNETs. The participants were randomly divided into training and test sets at a 7:3 ratio. The training set was used to construct a nomogram-based risk prediction model, and the testing set was used for internal validation. The area under the curve values for the training and testing sets were 0.843 and 0.772, respectively (P>0.05). These findings indicate a good discriminative performance. The calibration curves for the training and testing sets were in good agreement with the 45° standard line, which suggests that the predicted probabilities were consistent with the actual outcomes. Decision curve analysis showed that the model provided a high net benefit within a threshold range of 0.2 to 0.7 for clinical decision making. Conclusion Young age, fatty liver, high CEA levels, high cholesterol levels, and anxiety and depression are independent risk factors for RNETs. The nomogram model constructed based on these risk factors exhibits a strong capability to predict the occurrence of RNETs, and clinical intervention can be considered based on the predicted probability values.
