Purpose: The aim of this study was to construct and validate a nomogram for preoperatively predicting perineural invasion (PNI) in gastric cancer based on machine learning, and to investigate the impact of PNI on the overall survival (OS) of gastric cancer patients. Methods: Data were collected from 162 gastric patients and analyzed retrospectively, and radiomics features were extracted from contrast-enhanced computed tomography (CECT) scans. A group of 42 patients from the Cancer Imaging Archive (TCIA) were selected as the validation set. Univariable and multivariable analyses were used to analyze the risk factors for PNI. Thet-test, Max-Relevance and Min-Redundancy (mRMR) and the least absolute shrinkage and selection operator (LASSO) were used to select radiomics features. Radscores were calculated and logistic regression was applied to construct predictive models. A nomogram was developed by combining clinicopathological risk factors and the radscore. The area under the curve (AUC) values of receiver operating characteristic (ROC) curves, calibration curves and clinical decision curves were employed to evaluate the performance of the models. Kaplan–Meier analysis was used to study the impact of PNI on OS. Results: The univariable and multivariable analyses showed that the T stage, N stage and radscore were independent risk factors for PNI (p< 0.05). A nomogram based on the T stage, N stage and radscore was developed. The AUC of the combined model yielded 0.851 in the training set, 0.842 in the testing set and 0.813 in the validation set. The Kaplan–Meier analysis showed a statistically significant difference in OS between the PNI group and the non-PNI group (p< 0.05). Conclusions: A machine learning-based radiomics–clinicopathological model could effectively predict PNI in gastric cancer preoperatively through a non-invasive approach, and gastric cancer patients with PNI had relatively poor prognoses.
目的：本研究旨在基于机器学习构建并验证一种用于术前预测胃癌神经侵犯（PNI）的列线图，并探讨PNI对胃癌患者总生存期（OS）的影响。方法：回顾性收集162例胃癌患者资料进行分析，并从增强计算机断层扫描（CECT）图像中提取影像组学特征。另从癌症影像档案库（TCIA）选取42例患者作为验证集。采用单因素及多因素分析探究PNI的危险因素。通过t检验、最大相关最小冗余算法（mRMR）以及最小绝对收缩与选择算子（LASSO）筛选影像组学特征。计算影像组学评分并应用逻辑回归构建预测模型。结合临床病理危险因素与影像组学评分建立列线图。采用受试者工作特征曲线下面积、校准曲线及临床决策曲线评估模型性能。通过Kaplan-Meier分析研究PNI对OS的影响。结果：单因素及多因素分析显示T分期、N分期和影像组学评分是PNI的独立危险因素（p<0.05）。基于T分期、N分期和影像组学评分构建了列线图。联合模型在训练集、测试集和验证集的曲线下面积分别为0.851、0.842和0.813。Kaplan-Meier分析显示PNI组与非PNI组的总生存期存在统计学差异（p<0.05）。结论：基于机器学习的影像组学-临床病理模型可通过无创方式有效术前预测胃癌神经侵犯，且伴有PNI的胃癌患者预后相对较差。
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