Department of Radiology and Radiation Oncology, Niigata University Graduate School of Medical and Dental Sciences, 754, Ichibancho, Asahimachidori, Chuo-ku, Niigata, 951-8510, Japan.
Division of Pathology, Niigata University Medical and Dental Hospital, Niigata, Japan.
AJR Am J Roentgenol. 2020 Feb;214(2):341-347. doi: 10.2214/AJR.19.21844. Epub 2019 Nov 6.
The purpose of this study is to differentiate between low- and high-risk types of thymoma using quantitative 3D shape analysis of CT images. This retrospective study included 44 patients with a pathologic diagnosis of thymoma. Two radiologists semiautomatically contoured CT images of the tumors and evaluated 3D shape parameters-namely, quantitative indicators of surface smoothness, including sphericity, ellipsoidality, and discrete compactness. The visual CT findings that were analyzed included longest diameter, shape (round-oval, lobulated, or irregular), calcification, cystic or necrotic changes, and enhancement pattern (homogeneous or heterogeneous). The difference and discriminating performance between low-risk (types A, AB, and B1) and high-risk (types B2 and B3) thymomas were statistically assessed. Interobserver agreement was determined using the concordance correlation coefficient. Twenty-three low-risk and 21 high-risk thymomas were identified on the basis of pathologic findings. The median values of sphericity and ellipsoidality were significantly higher for low-risk thymomas than for high-risk thymomas (for sphericity, 0.566 vs 0.517; for ellipsoidality, 0.941 vs 0.875; < 0.05 for both). The AUC values of sphericity and ellipsoidality were 0.704 and 0.712, respectively. The best cutoff values were 0.528 and 0.919 for sphericity and ellipsoidality, respectively. Risk assessment combining these cutoff values and the mode of tumor detection (incidental detection or detection based on the presence of symptoms) improved the AUC value to 0.856 (sensitivity, 81.0% [17 of 21 patients]; specificity, 82.6% [19 of 23 patients]). All 3D shape parameters showed almost perfect interobserver agreement (concordance correlation coefficient, > 0.90). The visual CT findings were not significantly different between low- and high-risk thymomas ( > 0.05 for all). Quantitative 3D shape analysis has excellent reproducibility, and combining this technique with information on the detection mode helps differentiate low- from high-risk thymomas.
本研究旨在通过 CT 图像的三维定量形状分析，区分低危型和高危型胸腺瘤。该回顾性研究纳入了 44 例经病理诊断为胸腺瘤的患者。两名放射科医生半自动地勾画了肿瘤的 CT 图像，并评估了三维形状参数，即表面光滑度的定量指标，包括球形度、椭球度和离散紧密度。分析的视觉 CT 发现包括最长直径、形状（圆形-椭圆形、分叶状或不规则）、钙化、囊性或坏死改变以及增强模式（均匀或不均匀）。统计评估了低危（A、AB 和 B1 型）和高危（B2 和 B3 型）胸腺瘤之间的差异和判别性能。使用一致性相关系数确定观察者间的一致性。根据病理结果，确定了 23 例低危和 21 例高危胸腺瘤。低危胸腺瘤的球形度和椭球度中位数明显高于高危胸腺瘤（球形度为 0.566 比 0.517；椭球度为 0.941 比 0.875；均 < 0.05）。球形度和椭球度的 AUC 值分别为 0.704 和 0.712。球形度和椭球度的最佳截断值分别为 0.528 和 0.919。结合这些截断值和肿瘤检测方式（偶然发现或基于症状发现）的风险评估，将 AUC 值提高至 0.856（敏感性为 81.0%[21 例患者中的 17 例]；特异性为 82.6%[23 例患者中的 19 例]）。所有三维形状参数均显示出极好的观察者间一致性（一致性相关系数>0.90）。低危和高危胸腺瘤之间的视觉 CT 发现无显著差异（所有均 >0.05）。三维定量形状分析具有极好的可重复性，结合该技术与检测方式的信息有助于区分低危和高危胸腺瘤。
