Acute Angle of Multilobulated Contours Improves the Risk Classification of Thymomas

[Background] Computed tomography (CT) plays an important role in identifying and characterizing thymomas. It has been mainly used during preoperative evaluation for clinical staging. However, the reliable prediction of histological risk types of thymoma based on CT imaging features requires further study. In this study, we developed and validated a nomogram based on CT imaging and included new indices for individualized preoperative prediction of the risk classification of thymomas. [Methods] We conducted a retrospective, multicenter study, including 229 patients from two Chinese medical centers. All patients underwent cross-sectional CT imaging within 2 weeks before surgery. The results of pathological assessments were retrieved from existing reports of the excised lesions. We evaluated the tumor perimeter contacted the lung (TPCL) and measure the new quantitative indicators, the acute angle (AA) formed by adjacent lobulations. Two predictive models of risk classification were created using the method of least absolute shrinkage and selection operator (LASSO) in a training cohort for features selection. The model with smaller Akaike information criterion was then used to create individualized imaging nomogram, which we evaluated regarding its prediction ability and clinical utility. [Results] A new CT imaging-based model incorporating AA was developed and validated, which had improved predictive performance during risk classification of thymomas when compared to a model using traditional imaging predictors. The new imaging nomogram with AA demonstrated its clinical utility using decision curve analysis. [Conclusions] AA can improve the performance of a CT-based predictive model during preoperative risk classification of thymomas and should be considered a new imaging marker for evaluating and treating patients with thymomas. On the contrary, TPCL is not useful as a predictor for risk classification of thymomas in this study.