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基于增强 CT 的影像组学分析对进展期胃癌隐匿性腹膜转移的术前预测

Radiomics analysis using contrast-enhanced CT for preoperative prediction of occult peritoneal metastasis in advanced gastric cancer.

机构信息

Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, 321 Zhongshan Road, Nanjing, 210008, China.

Department of Gastrointestinal Surgery, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210008, China.

出版信息

Eur Radiol. 2020 Jan;30(1):239-246. doi: 10.1007/s00330-019-06368-5. Epub 2019 Aug 5.

Abstract

OBJECTIVES

To evaluate the predictive value of CT radiomics features derived from the primary tumor in discriminating occult peritoneal metastasis (PM) in advanced gastric cancer (AGC).

METHODS

Preoperative CT images of 233 patients with AGC were retrospectively analyzed. The region of interest (ROI) was manually drawn along the margin of the lesion on the largest slice of venous CT images, and a total of 539 quantified features were extracted automatically. The intra-class correlation coefficient (ICC) and the absolute correlation coefficient (ACC) were calculated for selecting influential features. A multivariate logistic regression model was constructed based on the training cohort, and the testing cohort validated the reliability of the model. Additionally, another model based on the preoperative clinic-pathological features was also developed. The comparison of the diagnostic performance between the two models was performed using ROC analysis and the Akaike information criterion (AIC) value.

RESULTS

Six radiomics features (ID_Energy, LoG(0.5)_Energy, Compactness2, Max Diameter, Orientation, and Surface Area Density) differed significantly between AGCs with and without PM and performed well in distinguishing AGCs with PM from those without PM in the primary cohort (AUC = 0.618-0.658). The radiomics model showed a higher AUC value than each single radiomics feature in the primary cohort (0.741 vs. 0.618-0.658) and similar diagnosis performance in the validation cohort. The radiomics model showed slightly worse diagnostic efficacy than the clinic-pathological model (AUC, 0.724 vs. 0.762).

CONCLUSION

Venous CT radiomics analysis based on the primary tumor provided valuable information for predicting occult PM in AGCs.

KEY POINTS

• Venous CT radiomics analysis provided valuable information for predicting occult peritoneal metastases in advanced gastric cancer. • CT-based T stage was an independent predictive factor of occult peritoneal metastases in advanced gastric cancer. • A radiomics model showed slightly worse diagnostic efficacy than a clinic-pathological model.

摘要

目的

评估源自原发性肿瘤的 CT 放射组学特征在鉴别晚期胃癌(AGC)隐匿性腹膜转移(PM)中的预测价值。

方法

回顾性分析 233 例 AGC 患者的术前 CT 图像。在静脉 CT 图像的最大切片上,手动沿病变边缘绘制感兴趣区(ROI),自动提取总共 539 个定量特征。计算组内相关系数(ICC)和绝对相关系数(ACC),以选择有影响的特征。基于训练队列构建多变量逻辑回归模型,并用测试队列验证模型的可靠性。此外,还建立了另一个基于术前临床病理特征的模型。使用 ROC 分析和 Akaike 信息准则(AIC)值比较两种模型的诊断性能。

结果

在原发性肿瘤中,有隐匿性 PM 的 AGC 与无隐匿性 PM 的 AGC 之间有 6 个放射组学特征(ID_Energy、LoG(0.5)_Energy、Compactness2、Max Diameter、Orientation 和 Surface Area Density)有显著差异,并且在原发性肿瘤中区分有和无隐匿性 PM 的 AGC 方面表现良好(AUC=0.618-0.658)。放射组学模型在原发性肿瘤中的 AUC 值高于每个单一放射组学特征(0.741 比 0.618-0.658),在验证队列中具有相似的诊断性能。放射组学模型的诊断效能略逊于临床病理模型(AUC,0.724 比 0.762)。

结论

基于原发性肿瘤的静脉 CT 放射组学分析为预测 AGC 隐匿性 PM 提供了有价值的信息。

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