Gao Jian, Wang Si-Yang, Ao Yong-Qiang, Jiang Jia-Hao, Lin Miao, Wang Shuai, Shi Hong-Cheng, Ding Jian-Yong
Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China.
Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China.
Interdiscip Cardiovasc Thorac Surg. 2025 Mar 5;40(3). doi: 10.1093/icvts/ivaf065.
This study aimed to explore the possibility of positron emission tomography/computed tomography (PET-CT) in identifying histological classification of thymic tumours.
Patients diagnosed as thymic tumours and accepted PET-CT scans were included. Thymic tumours were classified into three subgroups: low-risk thymoma (A, AB and B1), high-risk thymoma (B2, B3) and thymic carcinoma (TC). Logistic regression analysis was performed to identify potential factors differentiating the classification of thymic tumours. The receiver operating characteristic curve was applied to assess the diagnosis efficiency and the cut-off value.
From 2015 to 2023, a total of 176 patients, including 75 cases of low-risk thymoma, 60 cases of high-risk thymoma and 41 cases of TC, were included. The logistic regression models suggested maximum standardized uptake value (SUVmax) as a potential factor differentiating the three subgroups. Moreover, the receiver operating characteristic curve identified the SUVmax in differentiating low-risk thymoma vs high-risk thymoma (area under the curve [AUC]: 0.845, 95% CI: 0.776-0.913, specificity: 0.907, sensitivity: 0.716), low-risk thymoma vs TC (AUC: 0.976, 95% CI: 0.953-0.999, specificity: 0.933, sensitivity: 0.951) and high-risk thymoma vs TC (AUC: 0.84, 95% CI: 0.761-0.92, specificity: 0.865, sensitivity: 0.703), respectively. SUVmax was also an independent factor identifying thymic tumours with or without lymph node metastasis. The cut-off of 10 in SUVmax could well identify lymph node metastasis with the positive predict value of 0.684 and negative predict value of 0.981.
SUVmax is a reliable factor in distinguishing different histological subgroups and identifying lymph node metastasis in thymic tumours.
本研究旨在探讨正电子发射断层扫描/计算机断层扫描（PET-CT）在鉴别胸腺肿瘤组织学分类方面的可能性。
纳入诊断为胸腺肿瘤并接受PET-CT扫描的患者。胸腺肿瘤分为三个亚组：低风险胸腺瘤（A、AB和B1）、高风险胸腺瘤（B2、B3）和胸腺癌（TC）。进行逻辑回归分析以确定区分胸腺肿瘤分类的潜在因素。应用受试者操作特征曲线评估诊断效率和临界值。
2015年至2023年，共纳入176例患者，包括75例低风险胸腺瘤、60例高风险胸腺瘤和41例胸腺癌。逻辑回归模型表明最大标准化摄取值（SUVmax）是区分这三个亚组的潜在因素。此外，受试者操作特征曲线确定SUVmax在鉴别低风险胸腺瘤与高风险胸腺瘤（曲线下面积[AUC]：0.845，95%可信区间：0.776-0.913，特异性：0.907，敏感性：0.716）、低风险胸腺瘤与胸腺癌（AUC：0.976，95%可信区间：0.953-0.999，特异性：0.933，敏感性：0.951）以及高风险胸腺瘤与胸腺癌（AUC：0.84，95%可信区间：0.761-0.92，特异性：0.865，敏感性：0.703）方面分别具有鉴别意义。SUVmax也是鉴别有无淋巴结转移的胸腺肿瘤的独立因素。SUVmax的临界值为10时能够很好地鉴别淋巴结转移，阳性预测值为0.684，阴性预测值为0.981。
SUVmax是区分胸腺肿瘤不同组织学亚组以及鉴别胸腺肿瘤淋巴结转移的可靠因素。
