Clinical significance of positron emission tomography-computed tomography in the classification of thymic tumors.

Objectives: This study aimed to explore the possibility of positron emission tomography/computed tomography (PET-CT) in identifying histological classification of thymic tumors.

Methods: Patients diagnosed as thymic tumors and accepted PET-CT scans were included. Thymic tumors 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 tumors. The Receiver Operating Curve was applied to assess the diagnosis efficiency and the cut-off value.

Results: 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 unptake value (SUVmax) as a potential factor differentiating the three subgroups. Moreover, the Receiver operating curve identified that SUVmax in differentiating low risk thymoma vs high risk thymoma (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 tumors 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.

Conclusions: SUVmax is a reliable factor in distinguishing different histological subgroups and identifying lymph node metastasis in thymic tumors.