Ma Yu-Hui, Zhang Jie, Yan Wei-Qiang, Lan Jiang-Tao, Feng Xiu-Long, Wang Shu-Mei, Yang Guang, Hu Yu-Chuan, Cui Guang-Bin
Department of Radiology, Tangdu Hospital, Air Force Medical University (Fourth Military Medical University), Xi'an, Shaanxi, China.
Functional and Molecular Imaging Key Lab of Shaanxi Province, Xi'an, Shaanxi, China.
Front Oncol. 2023 Sep 11;13:1239419. doi: 10.3389/fonc.2023.1239419. eCollection 2023.
To explore the characteristics and risk factors for major mediastinal vessel invasion in different risk grades of thymic epithelial tumors (TETs) based on computed tomography (CT) imaging, and to develop prediction models of major mediastinal artery and vein invasion.
One hundred and twenty-two TET patients confirmed by histopathological analysis who underwent thorax CT were enrolled in this study. Clinical and CT data were retrospectively reviewed for these patients. According to the abutment degree between the tumor and major mediastinal vessels, the arterial invasion was divided into grade I, II, and III (< 25%, 25 - 49%, and ≥ 50%, respectively); the venous invasion was divided into grade I and II (< 50% and ≥ 50%). The degree of vessel invasion was compared among different defined subtypes or stages of TETs using the chi-square tests. The risk factors associated with TET vascular invasion were identified using multivariate logistic regression analysis.
Based on logistic regression analysis, male patients (β = 1.549; odds ratio, 4.824) and the pericardium or pleural invasion (β = 2.209; odds ratio, 9.110) were independent predictors of 25% artery invasion, and the midline location (β = 2.504; odds ratio, 12.234) and mediastinal lymphadenopathy (β = 2.490; odds ratio, 12.06) were independent predictors of 50% artery invasion. As for 50% venous invasion, the risk factors include midline location (β = 2.303; odds ratio, 10.0), maximum tumor diameter larger than 5.9 cm (β = 4.038; odds ratio, 56.736), and pericardial or pleural effusion (β = 1.460; odds ratio, 4.306). The multivariate logistic model obtained relatively high predicting efficacy, and the area under the curve (AUC), sensitivity, and specificity were 0.944, 84.6%, and 91.7% for predicting 50% artery invasion, and 0.913, 81.8%, and 86.0% for 50% venous invasion in TET patients, respectively.
Several CT features can be used as independent predictors of ≥50% artery or venous invasion. A multivariate logistic regression model based on CT features is helpful in predicting the vascular invasion grades in patients with TET.
基于计算机断层扫描（CT）影像探讨不同风险等级胸腺上皮肿瘤（TETs）中主要纵隔血管侵犯的特征及危险因素，并建立主要纵隔动静脉侵犯的预测模型。
本研究纳入122例经组织病理学分析确诊且接受胸部CT检查的TET患者。对这些患者的临床和CT数据进行回顾性分析。根据肿瘤与主要纵隔血管的毗邻程度，将动脉侵犯分为I、II、III级（分别为<25%、25%-49%和≥50%）；静脉侵犯分为I级和II级（<50%和≥50%）。采用卡方检验比较不同定义的TET亚型或分期之间的血管侵犯程度。使用多因素逻辑回归分析确定与TET血管侵犯相关的危险因素。
基于逻辑回归分析，男性患者（β=1.549；比值比，4.824）和心包或胸膜侵犯（β=2.209；比值比，9.110）是动脉侵犯达25%的独立预测因素，中线位置（β=2.504；比值比，12.234）和纵隔淋巴结肿大（β=2.490；比值比，12.06）是动脉侵犯达50%的独立预测因素。对于静脉侵犯达50%，危险因素包括中线位置（β=2.303；比值比，10.0）、最大肿瘤直径大于5.9 cm（β=4.038；比值比，56.736）和心包或胸腔积液（β=1.460；比值比，4.306）。多因素逻辑模型具有较高的预测效能，预测TET患者动脉侵犯达50%时的曲线下面积（AUC）、敏感性和特异性分别为0.944、84.6%和91.7%，预测静脉侵犯达50%时分别为0.913、81.8%和86.0%。
多种CT特征可作为动脉或静脉侵犯≥50%的独立预测因素。基于CT特征的多因素逻辑回归模型有助于预测TET患者的血管侵犯等级。
