CT staging and preoperative assessment of resectability for thymic epithelial tumors.

backgroundThe aim of this study was to determine the computed tomography (CT) features potentially helpful for accurate staging and predicting resectability of thymic epithelial tumors (TET).

methodsOne hundred and thirty-eight consecutive TET patients undergoing surgical resection from April 2010 to November 2011 were prospectively entered into a database. All patients were staged according to the Masaoka-Koga staging system. The relationship between CT features with tumor staging and complete resection was reviewed after surgery.

resultsSurgico-pathological staging was stage I in 63, stage II in 32, stage III in 32, and stage IV in 11 patients. Preoperative CT staging was highly consistent with postoperative surgico-pathological staging (Kappa =0.525). Tumor shape, contour, enhancement, with or without invasion of the adjacent structures (mediastinal fat, mediastinal pleura, lung, pericardium, mediastinal vessels, phrenic nerve), and presence of pleural, pericardial effusionor intrapulmonary metastasis were correlated with Masaoka-Koga staging (P<0.05). However, tumor size, internal density or presence of calcification was not associated with staging (P>0.05). Tumor size, presence of calcification and mediastinal lymph node enlargement were not correlated with complete tumor resection (P>0.05). Tumor shape, contour, internal density, enhancement pattern, and invasion of adjacent structures were related to complete resection of the primary tumor in univariate analysis (P<0.05). However, upon multivariate logistic regression, only absence of artery systems invasion was predictive of complete resection (P<0.05).

conclusionsClinical staging of TET could be accurately evaluated with CT features including tumor shape, contour, enhancement pattern, with or without invasion of adjacent structures, and presence of pleural, pericardial effusion or intrapulmonary metastasis. Absence of arterial system invasion on CT was the only predictive feature for predicting complete resection of TET.