OBJECTIVES To build a dual-energy CT (DECT)-based deep learning radiomics nomogram for lymph node metastasis (LNM) prediction in gastric cancer. MATERIALS AND METHODS Preoperative DECT images were retrospectively collected from 204 pathologically confirmed cases of gastric adenocarcinoma (mean age, 58 years; range, 28-81 years; 157 men [mean age, 60 years; range, 28-81 years] and 47 women [mean age, 54 years; range, 28-79 years]) between November 2011 and October 2018, They were divided into training (n = 136) and test (n = 68) sets. Radiomics features were extracted from monochromatic images at arterial phase (AP) and venous phase (VP). Clinical information, CT parameters, and follow-up data were collected. A radiomics nomogram for LNM prediction was built using deep learning approach and evaluated in test set using ROC analysis. Its prognostic performance was determined with Harrell's concordance index (C-index) based on patients' outcomes. RESULTS The dual-energy CT radiomics signature was associated with LNM in two sets (Mann-Whitney U test, p < 0.001) and an achieved area under the ROC curve (AUC) of 0.71 for AP and 0.76 for VP in test set. The nomogram incorporated the two radiomics signatures and CT-reported lymph node status exhibited AUCs of 0.84 in the training set and 0.82 in the test set. The C-indices of the nomogram for progression-free survival and overall survival prediction were 0.64 (p = 0.004) and 0.67 (p = 0.002). CONCLUSION The DECT-based deep learning radiomics nomogram showed good performance in predicting LNM in gastric cancer. Furthermore, it was significantly associated with patients' prognosis. KEY POINTS • This study investigated the value of deep learning dual-energy CT-based radiomics in predicting lymph node metastasis in gastric cancer. • The dual-energy CT-based radiomics nomogram outweighed the single-energy model and the clinical model. • The nomogram also exhibited a significant prognostic ability for patient survival and enriched radiomics studies.

中文翻译：

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基于双能 CT 的深度学习放射组学可以提高胃癌淋巴结转移风险预测。

目的 建立基于双能 CT (DECT) 的深度学习放射组学诺模图，用于胃癌淋巴结转移 (LNM) 预测。材料和方法 回顾性收集了 204 例经病理证实的胃腺癌病例的术前 DECT 图像（平均年龄 58 岁；范围 28-81 岁；157 名男性 [平均年龄 60 岁；范围 28-81 岁] 和 47 名女性[平均年龄，54 岁；范围，28-79 岁]）2011 年 11 月至 2018 年 10 月，他们被分为训练（n = 136）和测试（n = 68）组。从动脉期 (AP) 和静脉期 (VP) 的单色图像中提取放射组学特征。收集临床信息、CT 参数和随访数据。使用深度学习方法构建了用于 LNM 预测的放射组学列线图，并使用 ROC 分析在测试集中进行了评估。它的预后性能是根据患者的结果用 Harrell 一致性指数（C 指数）确定的。结果 在两组中，双能 CT 放射组学特征与 LNM 相关（Mann-Whitney U 检验，p < 0.001），测试集中 AP 的 ROC 曲线下面积 (AUC) 为 0.71，VP 为 0.76。列线图结合了两个放射组学特征，CT 报告的淋巴结状态在训练集中显示 AUC 为 0.84，在测试集中显示为 0.82。无进展生存期和总生存期预测列线图的 C 指数分别为 0.64 (p = 0.004) 和 0.67 (p = 0.002)。结论 基于 DECT 的深度学习放射组学列线图在预测胃癌 LNM 方面表现出良好的性能。此外，它与患者的预后显着相关。要点 • 本研究调查了基于深度学习双能 CT 的放射组学在预测胃癌淋巴结转移中的价值。• 基于双能量CT 的放射组学列线图优于单能量模型和临床模型。• 列线图还展示了对患者生存和丰富的放射组学研究的重要预后能力。