Yin Hongda, Chen Yanan, Zhao Wei, Zhao Fuqiang, Huang Zhijun, Yue Aimin, Wang Zhijie
Abdominal Surgical Oncology Ward, Xinxiang Central Hospital, The Forth Clinical College of Xinxiang Medical University, Xinxiang, 453000, China.
Department of Gastroenterology, Nanchong Central Hospital, The Second Clinical Medical College of North Sichuan Medical College, Nanchong, 637000, China.
Heliyon. 2024 Dec 13;11(1):e41197. doi: 10.1016/j.heliyon.2024.e41197. eCollection 2025 Jan 15.
Advanced lesions are often ignored in well-differentiated colorectal neuroendocrine neoplasms (NENs) smaller than 2 cm, and we aimed to develop an effective nomogram for these lesions.
We extracted data from the Surveillance, Epidemiology, and End Results (SEER) database and used a logistic regression model to identify independent risk factors for advanced disease. All these identified factors were included to construct the prediction model, and the receiver operating characteristic (ROC) curve, calibration plot and DCA curve were utilized to assess the predictive value. The data obtained from the National Cancer Center were utilized for external validation.
In total, 3223 patients were enrolled in the training set, including 2947 (91.4 %) with early disease and 276 (8.6 %) with advanced disease. The logistic analysis showed that age (odds ratio (OR) = 1.486, 95 % confidence interval (CI): 1.102-2.003, P = 0.009), tumor size (OR = 11.071, 95 % CI: 8.229-14.893, P < 0.001), tumor location (OR = 7.882, 95 % CI: 5.784-10.743, P < 0.001) and tumor grade (OR = 1.768, 95 % CI: 1.206-2.593, P = 0.004) were independent variables for advanced disease. All of them were included in the final prediction model. The area under the ROC curve (AUC) was 0.838 (95 % CI: 0.807-0.868). The calibration plot and Hosmer‒Lemeshow test (P = 0.108) indicated favorable consistency between the predicted probabilities and actual probabilities of advanced disease. The Brier score was 0.108, indicating acceptable overall performance. The DCA curve presented a significant clinical net benefit. In the validation set, both the ROC curve and calibration plot exhibited an acceptable discrimination ability (AUC = 0.807 (95 % CI 0.702-0.913) and calibration (Hosmer Lemeshow P = 0.997), respectively.
The prediction model had good value for identifying advanced disease from well-differentiated colorectal NENs smaller than 2 cm.
在直径小于2cm的高分化结直肠神经内分泌肿瘤（NENs）中，进展期病变常被忽视，我们旨在为这些病变开发一种有效的列线图。
我们从监测、流行病学和最终结果（SEER）数据库中提取数据，并使用逻辑回归模型来确定进展期疾病的独立危险因素。将所有这些确定的因素纳入构建预测模型，并利用受试者工作特征（ROC）曲线、校准图和决策曲线分析（DCA）曲线来评估预测价值。从国家癌症中心获得的数据用于外部验证。
总共3223例患者纳入训练集，其中2947例（91.4%）为早期疾病，276例（8.6%）为进展期疾病。逻辑分析显示，年龄（比值比（OR）=1.486，95%置信区间（CI）：1.102 - 2.003，P = 0.009）、肿瘤大小（OR = 11.071，95%CI：8.229 - 14.893，P < 0.001）、肿瘤位置（OR = 7.882，95%CI：5.784 - 10.743，P < 0.001）和肿瘤分级（OR = 1.768，95%CI：1.206 - 2.593，P = 0.004）是进展期疾病的独立变量。所有这些因素都纳入最终预测模型。ROC曲线下面积（AUC）为0.838（95%CI：0.807 - 0.
