Abstract
There is no existing worldwide published method for mediastinum compartment classification based on transverse section images for the differential diagnosis of mediastinal tumors. Herein, we describe a new method for anatomic mediastinal compartment classification using transverse section computed tomography (CT) images and the use of this method to classify mediastinal lesions, and thus evaluate whether the method is sufficiently user-friendly and useful. In a publication of the Japanese Association for Research on the Thymus (JART), we proposed the following four mediastinal compartments based on transverse CT images: superior portion of mediastinum, anterior mediastinum (prevascular zone), middle mediastinum (peri-tracheoesophageal zone), and posterior mediastinum (paravertebral zone). In the present study, we retrospectively analyzed 445 pathologically proven mediastinal mass lesions, and categorized them into the proposed four compartments by consensus reading. Mass lesions were classified into compartments based on the location of the lesion centroid, and each lesion was satisfactorily categorized into a compartment. Almost all thymic epithelial tumors (99%, 244/246), all 24 thymic malignant lymphomas and a majority of germ cell neoplasms (93%, 54/58) were classified as being in the anterior mediastinum compartment. The majority of intrathoracic goiters (82%, 14/17) were categorized as being in the superior portion of the mediastinum compartment. Approximately two-thirds of mass lesions in the middle mediastinum were cysts, including foregut and pericardial cysts. Approximately 80% of 37 mass lesions in the posterior mediastinum were neurogenic tumors. Correspondingly, 29 of the 49 neurogenic tumors (60%) were categorized as being in the posterior mediastinum, while 10 (20%) were in the superior portion of the mediastinum, 4 (8%) in the anterior mediastinum, and 6 (12%) in the middle mediastinum. Our findings showed that the newly proposed mediastinal compartment classification using transverse images appears to be user-friendly enough for practical clinical application and may be helpful in differential diagnoses.