Discriminating Clear Cell From Non-Clear Cell Renal Cell Carcinoma: A Machine Learning Approach Using...

The aim of this investigation is to assess the clinical usefulness of a machine learning model using contrast-enhanced ultrasound (CEUS) radiomics in discriminating clear cell renal cell carcinoma (ccRCC) from non-ccRCC.

A total of 292 patients with pathologically confirmed RCC subtypes underwent CEUS (development set. n = 231; validation set, n = 61) in a retrospective study. Radiomics features were derived from CEUS images acquired during the cortical and parenchymal phases. Radiomics models were developed using logistic regression (LR), support vector machine, decision tree, naive Bayes, gradient boosting machine, and random forest. The suitable model was identified based on the area under the receiver operating characteristic curve (AUC). Appropriate clinical CEUS features were identified through univariate and multivariate LR analyses to develop a clinical model. By integrating radiomics and clinical CEUS features, a combined model was established. A comprehensive evaluation of the models' performance was conducted.

After the reduction and selection process were applied to 2250 radiomics features, the final set of 8 features was considered valuable. Among the models, the LR model had the highest performance on the validation set and showed good robustness. In both the development and validation sets, both the radiomics (AUC, 0.946 and 0.927) and the combined models (AUC, 0.949 and 0.925) outperformed the clinical model (AUC, 0.851 and 0.768), showing higher AUC values (all p < 0.05). The combined model exhibited favorable calibration and clinical benefit.

The combined model integrating clinical CEUS and CEUS radiomics features demonstrated good diagnostic performance in discriminating ccRCC from non-ccRCC.