Predicting The Risk Category Of Thymoma With Machine Learning-based Computed Tomography Radiomics Signatures And Their Between-imaging Phase Differences

This article discusses a radiomic and machine learning approach to predict high- and low-risk thymoma using imaging features and clinical characteristics. The study aims to provide clinicians with more refined diagnostic and prognostic insights into thymoma, enabling them to make more precise personalized treatment decisions. The study was conducted on a cohort of 126 patients diagnosed with thymoma and 5 patients diagnosed with thymic carcinoma, with data collected from 2015 to 2023.