Are quantitative radiomics features comparable to semantic radiology features for pre-operative risk classification of thymic epithelial tumours?

Thymic epithelial tumours (TETs) are rare and exhibit varied behaviour and prognosis based on their histological subtype, as classified by the World Health Organization (WHO). These subtypes are further categorized into low-risk and high-risk groups. Low-risk thymomas generally allow for complete surgical resection without adjuvant therapy, while high-risk types often require multimodal treatment due to their aggressive nature. This study aims to evaluate the role of CT radiomics in discriminating between high- and low-risk TETs. MethodsThis retrospective study included patients treated in a single hospital in India who underwent surgical resection of TETs from 2010 to 2024. Inclusion criteria were confirmed TET diagnosis, had pre-operative CT scans, and had medical and post-operative histopathological confirmation. Conventional CT (semantic) features were manually extracted from radiology reports, while radiomic features were obtained using PyRadiomics. The data was randomly split for training and a hold-out validation set stratified by class. Three classification models were evaluated, each using clinical, semantic, and radiomic features with LASSO regularization. The performance of the models was assessed using Area under the Receiver Operating Curve (AUC), sensitivity, and specificity on the test set. ResultsOut of 195 enrolled patients, 132 met inclusion criteria and were divided into training (n = 100) and a validation set (n=32). The clinical model included age, presence of Pure Red Cell Aplasia and weight loss, achieving an AUC of 0.69 (95% CI: 0.49-0.87), sensitivity of 0.73 (95% CI: 0.46-1.00), and specificity of 0.53 (95% CI: 0.29-0.76) in the holdout set. The radiomics model included 90th percentile and sphericity as key predictors with AUC of 0.77 (95% CI: 0.56-0.94), sensitivity of 0.82 (95% CI: 0.55-1.00), and specificity of 0.72 (95% CI: 0.52-0.91). The semantic model performed best with AUC of 0.82 (95% CI: 0.62- 0.96), sensitivity of 0.82 (95% CI: 0.55-1.00), and specificity of 0.77 (95% CI: 0.57-0.91). Discussion and ConclusionThe findings indicate that radiomic features could be valuable in preoperative risk assessment for TETs. Although the conventional CT features-based Semantic models demonstrated superior predictive capability, there is a risk of subjectivity and inter-observer disagreement.