Zhang Chengzhou, Yang Qinglin, Lin Fan, Ma Heng, Zhang Haicheng, Zhang Ran, Wang Ping, Mao Ning
Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, China.
School of Medical Imaging, Binzhou Medical University, Yantai, China.
Front Oncol. 2021 Dec 10;11:744021. doi: 10.3389/fonc.2021.744021. eCollection 2021.
This study aimed to distinguish preoperatively anterior mediastinal thymic cysts from thymic epithelial tumors a computed tomography (CT)-based radiomics nomogram.
This study analyzed 74 samples of thymic cysts and 116 samples of thymic epithelial tumors as confirmed by pathology examination that were collected from January 2014 to December 2020. Among the patients, 151 cases (scanned at CT 1) were selected as the training cohort, and 39 cases (scanned at CT 2 and 3) served as the validation cohort. Radiomics features were extracted from pre-contrast CT images. Key features were selected by SelectKBest and least absolute shrinkage and selection operator and then used to build a radiomics signature (Rad-score). The radiomics nomogram developed herein multivariate logistic regression analysis incorporated clinical factors, conventional CT findings, and Rad-score. Its performance in distinguishing the samples of thymic cysts from those of thymic epithelial tumors was assessed discrimination, calibration curve, and decision curve analysis (DCA).
The radiomics nomogram, which incorporated 16 radiomics features and 3 conventional CT findings, including lesion edge, lobulation, and CT value, performed better than Rad-score, conventional CT model, and the clinical judgment by radiologists in distinguishing thymic cysts from thymic epithelial tumors. The area under the receiver operating characteristic (ROC) curve of the nomogram was 0.980 [95% confidence interval (CI), 0.963-0.993] in the training cohort and 0.992 (95% CI, 0.969-1.000) in the validation cohort. The calibration curve and the results of DCA indicated that the nomogram has good consistency and valuable clinical utility.
The CT-based radiomics nomogram presented herein may serve as an effective and convenient tool for differentiating thymic cysts from thymic epithelial tumors. Thus, it may aid in clinical decision-making.
本研究旨在基于计算机断层扫描（CT）的放射组学列线图在术前区分前纵隔胸腺囊肿与胸腺上皮肿瘤。
本研究分析了2014年1月至2020年12月期间收集的经病理检查确诊的74例胸腺囊肿样本和116例胸腺上皮肿瘤样本。在这些患者中，151例（在CT 1扫描）被选为训练队列，39例（在CT 2和3扫描）作为验证队列。从平扫CT图像中提取放射组学特征。通过SelectKBest和最小绝对收缩和选择算子选择关键特征，然后用于构建放射组学特征（Rad评分）。本文开发的放射组学列线图通过多变量逻辑回归分析纳入了临床因素、传统CT表现和Rad评分。通过判别分析、校准曲线和决策曲线分析（DCA）评估其区分胸腺囊肿样本与胸腺上皮肿瘤样本的性能。
纳入16个放射组学特征和3个传统CT表现（包括病变边缘、分叶和CT值）的放射组学列线图在区分胸腺囊肿与胸腺上皮肿瘤方面比Rad评分、传统CT模型和放射科医生的临床判断表现更好。列线图在训练队列中的受试者操作特征（ROC）曲线下面积为0.980 [95%置信区间（CI），0.963 - 0.993]，在验证队列中为0.992（95% CI，0.969 - 1.000）。校准曲线和DCA结果表明列线图具有良好的一致性和有价值的临床实用性。
本文提出的基于CT的放射组学列线图可作为区分胸腺囊肿与胸腺上皮肿瘤的有效且便捷的工具。因此，它可能有助于临床决策。
