Institute of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Hangzhou, China.
Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences, Hangzhou, China.
Biomed Eng Online. 2020 Nov 27;19(1):89. doi: 10.1186/s12938-020-00833-9.
Anterior mediastinal cysts (AMC) are often misdiagnosed as thymomas and undergo surgical resection, which caused unnecessary treatment and medical resource waste. The purpose of this study is to explore potential possibility of computed tomography (CT)-based radiomics for the diagnosis of AMC and type B1 and B2 thymomas.
A group of 188 patients with pathologically confirmed AMC (106 cases misdiagnosed as thymomas in CT) and thymomas (82 cases) and underwent routine chest CT from January 2010 to December 2018 were retrospectively analyzed. The lesions were manually delineated using ITK-SNAP software, and radiomics features were performed using the artificial intelligence kit (AK) software. A total of 180 tumour texture features were extracted from enhanced CT and unenhanced CT, respectively. The general test, correlation analysis, and LASSO were used to features selection and then the radiomics signature (radscore) was obtained. The combined model including radscore and independent clinical factors was developed. The model performances were evaluated on discrimination, calibration curve.
Two radscore models were constructed from the unenhanced and enhanced phases based on the selected four and three features, respectively. The AUC, sensitivity, and specificity of the enhanced radscore model were 0.928, 89.3%, and 83.8% in the training dataset and 0.899, 84.6%, and 87.5% in the test dataset (higher than the unenhanced radscore model). The combined model of enhanced CT including radiomics features and independent clinical factors yielded an AUC, sensitivity and specificity of 0.941, 82.1%, and 94.6% in the training dataset and 0.938, 92.3%, and 87.5% in the test dataset (higher than the unenhanced combined model and enhanced radscore model).
The study suggested the possibility that the combined model in enhanced CT provided a potential tool to facilitate the differential diagnosis of AMC and type B1 and B2 thymomas.
前纵隔囊肿（AMC）常被误诊为胸腺瘤并进行手术切除，造成不必要的治疗和医疗资源浪费。本研究旨在探讨基于 CT 的放射组学在 AMC 和 B1、B2 型胸腺瘤诊断中的应用。
回顾性分析 2010 年 1 月至 2018 年 12 月期间经病理证实的 AMC（106 例 CT 误诊为胸腺瘤）和胸腺瘤（82 例）患者的 CT 资料。使用 ITK-SNAP 软件手动勾画病灶，使用人工智能套件（AK）软件进行放射组学特征分析。分别从增强 CT 和未增强 CT 中提取 180 个肿瘤纹理特征。采用一般检验、相关性分析和 LASSO 进行特征选择，得到放射组学特征（radscore）。构建包括 radscore 和独立临床因素的联合模型。在判别、校准曲线方面评估模型性能。
基于选择的 4 个和 3 个特征，分别从前增强和后增强期构建了两个 radscore 模型。增强 radscore 模型在训练集和测试集的 AUC、敏感性和特异性分别为 0.928、89.3%和 83.8%，0.899、84.6%和 87.5%（高于未增强 radscore 模型）。增强 CT 联合模型包括放射组学特征和独立临床因素，在训练集和测试集的 AUC、敏感性和特异性分别为 0.941、82.1%和 94.6%，0.938、92.3%和 87.5%（高于未增强联合模型和增强 radscore 模型）。
研究表明，增强 CT 联合模型为 AMC 和 B1、B2 型胸腺瘤的鉴别诊断提供了一种潜在的工具。
