Ma Jiuyue, Ma Xiaoqian, Xing Jie, Song Ruyun, Zhang Yang, Liu Mo, Guo Shuilong, Zhang Qian, Wu Jing
Department of Gastroenterology, Beijing Friendship Hospital, Capital Medical University, National Clinical Research Center for Digestive Disease, Beijing Digestive Disease Center, Beijing Key Laboratory for Precancerous Lesions of Digestive Disease, Beijing, 100050, China.
Heliyon. 2024 Aug 2;10(15):e35720. doi: 10.1016/j.heliyon.2024.e35720. eCollection 2024 Aug 15.
The incidence of colorectal neuroendocrine tumors (NETs) is increasing, causing a social burden. At present, there is no specific prognostic model for colorectal NETs. Thus, an accurate model is needed to predict the prognosis of patients with colorectal NETs.
We aimed to create a new nomogram to predict the prognosis of patients with colorectal NETs. Furthermore, we compared nomogram we established and the 8th edition of the AJCC TNM staging system in terms of prediction ability and accuracy.
A total of 3353 patients with colorectal NETs were selected from the Surveillance, Epidemiology, and End Results (SEER) database. Kaplan-Meier analyses were used to assess overall survival (OS) and cancer-specific survival (CSS). Additionally, LASSO regression was used to select variables for constructing the nomogram. Furthermore, the C-index and time-dependent receiver operating characteristic (tdROC) curve were used to evaluate the nomogram. Decision curve analysis (DCA) was performed to compare the clinical utility of the nomogram with that of the TNM system. An external validation cohort (N = 61) was established to evaluate the nomogram's prediction accuracy.
A total of 9 factors (age, sex, marital status, tumor size, T stage, M stage, N stage, grade, and surgery) were selected based on the results of LASSO analysis. The C-indexes of the nomogram in the training and validation sets were 0.807 and 0.775, respectively, which indicated that the nomogram had better prediction accuracy than TNM staging (C-index = 0.700 in the training set and 0.652 in the validation set). The C-index of the nomogram in the external validation cohort was 0.954, indicating that the nomogram had satisfactory prediction accuracy. The results of DCA revealed that the survival nomogram possessed greater utility in clinical practice.
We determined the OS and CSS of patients with colorectal NETs and developed a robust and clinically useful survival nomogram.
结直肠神经内分泌肿瘤（NETs）的发病率正在上升，造成社会负担。目前，尚无针对结直肠NETs的特异性预后模型。因此，需要一个准确的模型来预测结直肠NETs患者的预后。
我们旨在创建一个新的列线图来预测结直肠NETs患者的预后。此外，我们在预测能力和准确性方面比较了我们建立的列线图与美国癌症联合委员会（AJCC）第8版TNM分期系统。
从监测、流行病学和最终结果（SEER）数据库中选取3353例结直肠NETs患者。采用Kaplan-Meier分析评估总生存期（OS）和癌症特异性生存期（CSS）。此外，使用LASSO回归选择用于构建列线图的变量。此外，使用C指数和时间依赖性受试者工作特征（tdROC）曲线评估列线图。进行决策曲线分析（DCA）以比较列线图与TNM系统的临床实用性。建立一个外部验证队列（N = 61）来评估列线图的预测准确性。
根据LASSO分析结果，共选择了9个因素（年龄、性别、婚姻状况、肿瘤大小、T分期、M分期、N分期、分级和手术）。训练集和验证集中列线图的C指数分别为0.807和0.775，这表明列线图比TNM分期具有更好的预测准确性（训练集中C指数 = 0.700，验证集中C指数 = 0.652）。外部验证队列中列线图的C指数为0.954，表明列线图具有令人满意的预测准确性。DCA结果显示，生存列线图在临床实践中具有更大实用性。
我们确定了结直肠NETs患者的OS和CSS，并开发了一个强大且临床有用的生存列线图。
