PDF(Pubmed)
Abstract：
UNASSIGNED: This study aimed to develop and validate a clinical and imaging-based nomogram for preoperatively predicting perineural invasion (PNI) in advanced gastric cancer.
UNASSIGNED: A retrospective cohort of 351 patients with advanced gastric cancer who underwent surgical resection was included. Multivariable logistic regression analysis was conducted to identify independent risk factors for PNI and to construct the nomogram. The performance of the nomogram was assessed using calibration curves, the concordance index (C-index), the area under the curve (AUC), and decision curve analysis (DCA). The disparity in disease-free survival (DFS) between the nomogram-predicted PNI-positive group and the nomogram-predicted PNI-negative group was evaluated using the Log-Rank test and Kaplan-Meier analysis.
UNASSIGNED: Extramural vascular invasion (EMVI), Borrmann classification, tumor thickness, and the systemic inflammation response index (SIRI) emerged as independent risk factors for PNI. The nomogram model demonstrated a commendable AUC value of 0.838. Calibration curves exhibited excellent concordance, with a C-index of 0.814. DCA indicated that the model provided good clinical net benefit. The DFS of the nomogram-predicted PNI-positive group was significantly lower than that of the nomogram-predicted PNI-negative group (p < 0.001).
UNASSIGNED: This study successfully developed a preoperative nomogram model that not only effectively predicted PNI in gastric cancer but also facilitated postoperative risk stratification.
UNASSIGNED: A retrospective cohort of 351 patients with advanced gastric cancer who underwent surgical resection was included. Multivariable logistic regression analysis was conducted to identify independent risk factors for PNI and to construct the nomogram. The performance of the nomogram was assessed using calibration curves, the concordance index (C-index), the area under the curve (AUC), and decision curve analysis (DCA). The disparity in disease-free survival (DFS) between the nomogram-predicted PNI-positive group and the nomogram-predicted PNI-negative group was evaluated using the Log-Rank test and Kaplan-Meier analysis.
UNASSIGNED: Extramural vascular invasion (EMVI), Borrmann classification, tumor thickness, and the systemic inflammation response index (SIRI) emerged as independent risk factors for PNI. The nomogram model demonstrated a commendable AUC value of 0.838. Calibration curves exhibited excellent concordance, with a C-index of 0.814. DCA indicated that the model provided good clinical net benefit. The DFS of the nomogram-predicted PNI-positive group was significantly lower than that of the nomogram-predicted PNI-negative group (p < 0.001).
UNASSIGNED: This study successfully developed a preoperative nomogram model that not only effectively predicted PNI in gastric cancer but also facilitated postoperative risk stratification.
摘要：
■本研究旨在开发和验证基于临床和影像学的列线图，用于术前预测晚期胃癌的神经周浸润（PNI）。
■纳入351例接受手术切除的晚期胃癌患者的回顾性队列。进行多变量逻辑回归分析以确定PNI的独立危险因素并构建列线图。使用校准曲线评估列线图的性能，一致性指数(C指数)，曲线下面积（AUC），和决策曲线分析(DCA)。使用Log-Rank检验和Kaplan-Meier分析评估了列线图预测的PNI阳性组与列线图预测的PNI阴性组之间的无病生存（DFS）差异。
■壁外血管侵犯（EMVI），Borrmann分类,肿瘤厚度,全身炎症反应指数（SIRI）是PNI的独立危险因素。列线图模型显示了0.838的值得推荐的AUC值。校准曲线表现出优异的一致性，C指数为0.814。DCA表明该模型提供了良好的临床净效益。列线图预测的PNI阳性组的DFS显著低于列线图预测的PNI阴性组(p<0.001)。
■这项研究成功地开发了一种术前列线图模型，该模型不仅有效地预测了胃癌中的PNI，而且促进了术后风险分层。
■纳入351例接受手术切除的晚期胃癌患者的回顾性队列。进行多变量逻辑回归分析以确定PNI的独立危险因素并构建列线图。使用校准曲线评估列线图的性能，一致性指数(C指数)，曲线下面积（AUC），和决策曲线分析(DCA)。使用Log-Rank检验和Kaplan-Meier分析评估了列线图预测的PNI阳性组与列线图预测的PNI阴性组之间的无病生存（DFS）差异。
■壁外血管侵犯（EMVI），Borrmann分类,肿瘤厚度,全身炎症反应指数（SIRI）是PNI的独立危险因素。列线图模型显示了0.838的值得推荐的AUC值。校准曲线表现出优异的一致性，C指数为0.814。DCA表明该模型提供了良好的临床净效益。列线图预测的PNI阳性组的DFS显著低于列线图预测的PNI阴性组(p<0.001)。
■这项研究成功地开发了一种术前列线图模型，该模型不仅有效地预测了胃癌中的PNI，而且促进了术后风险分层。
