Predictive Features of Thymic Carcinoma and High-Risk Thymomas Using Random Forest Analysis.

Purpose: To determine the predictive features of thymic carcinomas and high-risk thymomas using random forest algorithm.

Methods: A total of 137 patients with pathologically confirmed high-risk thymomas and thymic carcinomas were enrolled in this study. Three clinical features and 20 computed tomography features were reviewed. The association between computed tomography features and pathological patterns was analyzed by univariate analysis and random forest. The predictive efficiency of the random forest algorithm was evaluated by receiver operating characteristic curve analysis.

Results: There were 92 thymic carcinomas and 45 high-risk thymomas in this study. In univariate analysis, patient age, presence of myasthenia gravis, lesion shape, enhancement pattern, presence of necrosis or cystic change, mediastinal invasion, vessel invasion, lymphadenopathy, pericardial effusion, and distant organ metastasis were found to be statistically different between high-risk thymomas and thymic carcinomas (all P < 0.01). Random forest suggested that tumor shape, lymphadenopathy, and the presence of pericardial effusion were the key features in tumor differentiation. The predictive accuracy for the test data and whole data was 94.73% and 96.35%, respectively. Further receiver operating characteristic curve analysis showed the area under the curve was 0.957 (95% confidence interval, 0.986-0.929).

Conclusions: The random forest model in the present study has high efficiency in predictive diagnosis of thymic carcinomas and high-risk thymomas. Tumor shape, lymphadenopathy, and pericardial effusion are the key features for tumor differentiation. Thymic tumors with irregular shape, the presence of lymphadenopathy, and pericardial effusion are highly indicative of thymic carcinomas.