背景:内窥镜超声检查(EUS)被推荐为评估胃上皮下病变(SEL)的最佳工具;尽管如此,它难以区分胃肠道间质瘤(GIST)与平滑肌瘤和神经鞘瘤。GIST具有恶性潜能,而平滑肌瘤和神经鞘瘤被认为是良性的。
目的:本研究旨在建立基于EUS图像的联合影像组学模型,以区分胃中GIST与平滑肌瘤和神经鞘瘤。
方法:经病理证实的GIST的EUS图像,平滑肌瘤,和神经鞘瘤从五个中心收集。基于随机分裂样本方法(7:3)将胃SELs分为训练数据集和测试数据集。从肿瘤和固有肌层区域提取放射学特征。主成分分析,最小绝对收缩率,和选择运算符用于特征选择。采用支持向量机构建影像组学模型。建立了两个影像组学模型:常规影像组学模型仅包括肿瘤特征,而联合影像组学模型结合了肿瘤和固有肌层区域的特征。
结果:共纳入485例的3933张EUS图像。对于平滑肌瘤和神经鞘瘤的GIST的鉴别诊断,准确性,灵敏度,特异性,受试者工作特性曲线下面积为74.5%,72.2%,78.7%,和0.754,分别EUS专家;76.8%,74.4%,81.0%,和0.830,分别对于传统的放射学模型;和90.9%,91.0%,90.6%,和0.953,分别用于组合放射学模型。对于胃SELs<20mm,准确性,灵敏度,特异性,联合影像组学模型的受试者工作特征曲线下面积为91.4%,91.6%,91.1%,和0.960。
结论:我们开发并验证了一种联合影像组学模型来区分胃GIST与平滑肌瘤和神经鞘瘤。联合影像组学模型显示出比常规影像组学模型更好的诊断性能,可以帮助EUS专家非侵入性诊断胃SELs。特别是胃SEL<20mm。
BACKGROUND: Endoscopic ultrasonography (EUS) is recommended as the best tool for evaluating gastric subepithelial lesions (SELs); nonetheless, it has difficulty distinguishing gastrointestinal stromal tumors (GISTs) from leiomyomas and schwannomas. GISTs have malignant potential, whereas leiomyomas and schwannomas are considered benign.
OBJECTIVE: This study aimed to establish a combined radiomic model based on EUS images for distinguishing GISTs from leiomyomas and schwannomas in the stomach.
METHODS: EUS images of pathologically confirmed GISTs, leiomyomas, and schwannomas were collected from five centers. Gastric SELs were divided into training and testing datasets based on random split-sample method (7:3). Radiomic features were extracted from the tumor and muscularis propria regions. Principal component analysis, least absolute shrinkage, and selection operator were used for feature selection. Support vector machine was used to construct radiomic models. Two radiomic models were built: the conventional radiomic model included tumor features alone, whereas the combined radiomic model incorporated features from the tumor and muscularis propria regions.
RESULTS: A total of 3933 EUS images from 485 cases were included. For the differential diagnosis of GISTs from leiomyomas and schwannomas, the accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve were 74.5%, 72.2%, 78.7%, and 0.754, respectively, for the EUS experts; 76.8%, 74.4%, 81.0%, and 0.830, respectively, for the conventional radiomic model; and 90.9%, 91.0%, 90.6%, and 0.953, respectively, for the combined radiomic model. For gastric SELs <20 mm, the accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve of the combined radiomic model were 91.4%, 91.6%, 91.1%, and 0.960, respectively.
CONCLUSIONS: We developed and validated a combined radiomic model to distinguish gastric GISTs from leiomyomas and schwannomas. The combined radiomic model showed better diagnostic performance than the conventional radiomic model and could assist EUS experts in non-invasively diagnosing gastric SELs, particularly gastric SELs <20 mm.