BACKGROUND In the clinical management of advanced gastric cancer (AGC), preoperative identification of early recurrence after curative resection is essential. Thus, we aimed to create a CT-based radiomic model to predict early recurrence in AGC patients preoperatively. MATERIALS AND METHODS We enrolled 669 consecutive patients (302 in the training set, 219 in the internal test set and 148 in the external test set) with clinicopathologically confirmed AGC from two centers. Radiomic features were extracted from preoperative diagnostic CT images. Machine learning methods were applied to shrink feature size and build a predictive radiomic signature. We incorporated the radiomic signature and clinical risk factors into a nomogram using multivariable logistic regression analysis. The area under the curve (AUC) of operating characteristics (ROC), accuracy, and calibration curves were assessed to evaluate the nomogram's performance in discriminating early recurrence. RESULTS A radiomic signature, including three hand crafted features and six deep learning features, was significantly associated with early recurrence (p-value <0.0001 for all sets). In addition, clinical N stage, carbohydrate antigen 199 levels, carcinoembryonic antigen levels, and Borrmann type were considered useful predictors for early recurrence. The nomogram, combining all these predictors, showed powerful prognostic ability in the training set and two test sets with AUCs of 0.831 (95% CI, 0.786-0.876), 0.826 (0.772-0.880) and 0.806 (0.732-0.881), respectively. The predicted risk yielded good agreement with the observed recurrence probability. CONCLUSIONS By incorporating a radiomic signature and clinical risk factors, we created a radiomic nomogram to predict early recurrence in patients with AGC, preoperatively, which may serve as a potential tool to guide personalized treatment.

中文翻译：

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用于术前预测晚期胃癌早期复发的基于 CT 的放射组学列线图的开发和验证

背景 在晚期胃癌 (AGC) 的临床管理中，术前识别根治性切除后的早期复发至关重要。因此，我们旨在创建一个基于 CT 的放射组学模型来预测术前 AGC 患者的早期复发。材料与方法 我们从两个中心招募了 669 名经临床病理证实为 AGC 的连续患者（训练集 302 例，内部测试集 219 例，外部测试集 148 例）。从术前诊断 CT 图像中提取影像学特征。机器学习方法被应用于缩小特征尺寸并构建预测放射组学特征。我们使用多变量逻辑回归分析将放射组学特征和临床风险因素纳入列线图中。工作特性（ROC）的曲线下面积（AUC），准确性，并评估校准曲线以评估列线图在区分早期复发方面的性能。结果 一个放射组学特征，包括三个手工制作的特征和六个深度学习特征，与早期复发显着相关（所有组的 p 值 <0.0001）。此外，临床 N 分期、碳水化合物抗原 199 水平、癌胚抗原水平和 Borrmann 类型被认为是早期复发的有用预测因子。列线图结合所有这些预测因子，在训练集和两个测试集显示出强大的预后能力，AUC 分别为 0.831（95% CI，0.786-0.876）、0.826（0.772-0.880）和 0.806（0.732-0.881）预测的风险与观察到的复发概率非常吻合。