MRI radiomics and machine learning for prediction of adherent perinephric fat

Motivation: Sticky perinephric fat (SPF) poses a surgical challenge for patients with renal cell carcinoma and the pre-operative identification of SPF is of clinical interest. Goal(s): The aim of this study was to investigate the effectiveness of using MRI-based radiomics features in predicting the presence of SPF. Approach: Machine learning algorithms were trained using radiomics features from T1-weighted contrast-enhanced MRI images and clinical factors (gender and BMI). Results: The promising results on internal and external test sets pave the way to validate the current approach in a larger data set. Impact: Machine learning models trained with MRI-derived radiomics features can provide a tool for preoperative prediction of sticky perinephric fat. The results from this study suggest that this approach may assist in improving surgical prognosis and outcomes.