The First Clinical College of Jinan University, Guangzhou 510630, China; Department of Radiology, The Second Clinical Medical School of North Sichuan Medical College, Nanchong Central Hospital, Nanchong, Sichuan 637000, China.
Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan 637000, China.
Eur J Radiol. 2024 Jun;175:111479. doi: 10.1016/j.ejrad.2024.111479. Epub 2024 Apr 22.
To construct and validate CT radiomics model based on the peritumoral adipose region of gastric adenocarcinoma to preoperatively predict lymph node metastasis (LNM).
293 consecutive gastric adenocarcinoma patients receiving radical gastrectomy with lymph node dissection in two medical institutions were stratified into a development set (from Institution A, n = 237), and an external validation set (from Institution B, n = 56). Volume of interest of peritumoral adipose region was segmented on preoperative portal-phase CT images. The least absolute shrinkage and selection operator method and stepwise logistic regression were used to select features and build radiomics models. Manual classification was performed according to routine CT characteristics. A classifier incorporating the radiomics score and CT characteristics was developed for predicting LNM. Area under the receiver operating characteristic curve (AUC) was used to show discrimination between tumors with and without LNM, and the calibration curves and Brier score were used to evaluate the predictive accuracy. Violin plots were used to show the distribution of radiomics score.
AUC values of radiomics model to predict LNM were 0.938, 0.905, and 0.872 in the training, internal test, and external validation sets, respectively, higher than that of manual classification (0.674, all P values < 0.01). The radiomics score of the positive LNM group were higher than that of the negative group in all sets (both P-values < 0.001). The classifier showed no improved predictive power compared with the radiomics signature alone with AUC values of 0.916 and 0.872 in the development and external validation sets, respectively. Multivariate analysis showed that radiomics score was an independent predictor.
Radiomics model based on peritumoral adipose region could be a useful approach for preoperative LNM prediction in gastric adenocarcinoma.
构建并验证基于胃腺癌瘤周脂肪区域的 CT 放射组学模型，以术前预测淋巴结转移（LNM）。
将在两家医疗机构接受根治性胃切除术和淋巴结清扫术的 293 例连续胃腺癌患者分层为发展集（来自机构 A，n=237）和外部验证集（来自机构 B，n=56）。在术前门静脉期 CT 图像上对瘤周脂肪区域进行感兴趣区分割。使用最小绝对收缩和选择算子方法和逐步逻辑回归选择特征并构建放射组学模型。根据常规 CT 特征进行手动分类。开发了一种结合放射组学评分和 CT 特征的分类器，用于预测 LNM。受试者工作特征曲线下面积（AUC）用于显示有和无 LNM 肿瘤之间的区分，校准曲线和 Brier 评分用于评估预测准确性。小提琴图用于显示放射组学评分的分布。
在训练、内部测试和外部验证集中，预测 LNM 的放射组学模型的 AUC 值分别为 0.938、0.905 和 0.872，高于手动分类（均 P 值<0.01）。在所有集（均 P 值<0.001）中，阳性 LNM 组的放射组学评分均高于阴性组。与放射组学特征相比，分类器的 AUC 值分别为 0.916 和 0.872，在发展和外部验证集中均未显示出预测能力的提高。多变量分析表明，放射组学评分是独立的预测因子。
基于瘤周脂肪区域的放射组学模型可能是胃腺癌术前预测 LNM 的有用方法。
