Background Renal cell carcinoma (RCC) is a prevalent malignancy with highly variable outcomes. MicroRNA-15a
(miR-15a) has emerged as a promising prognostic biomarker in RCC, linked to angiogenesis, apoptosis, and proliferation.
Radiogenomics integrates radiological features with molecular data to non-invasively predict biomarkers, offering
valuable insights for precision medicine. This study aimed to develop a machine learning-assisted radiogenomic
model to predict miR-15a expression in RCC.
Methods A retrospective analysis was conducted on 64 RCC patients who underwent preoperative multiphase
contrast-enhanced CT or MRI. Radiological features, including tumor size, necrosis, and nodular enhancement, were
evaluated. MiR-15a expression was quantified using real-time qPCR from archived tissue samples. Polynomial regression
and Random Forest models were employed for prediction, and hierarchical clustering with K-means analysis
was used for phenotypic stratification. Statistical significance was assessed using non-parametric tests and machine
learning performance metrics.
Results Tumor size was the strongest radiological predictor of miR-15a expression (adjusted R2 = 0.8281, p < 0.001).
High miR-15a levels correlated with aggressive features, including necrosis and nodular enhancement (p < 0.05),
while lower levels were associated with cystic components and macroscopic fat. The Random Forest regression
model explained 65.8% of the variance in miR-15a expression ( R2 = 0.658). For classification, the Random Forest classifier
demonstrated exceptional performance, achieving an AUC of 1.0, a precision of 1.0, a recall of 0.9, and an F1-score
of 0.95. Hierarchical clustering effectively segregated tumors into aggressive and indolent phenotypes, consistent
with clinical expectations.
Conclusions Radiogenomic analysis using machine learning provides a robust, non-invasive approach to predicting
miR-15a expression, enabling enhanced tumor stratification and personalized RCC management. These findings
underscore the clinical utility of integrating radiological and molecular data, paving the way for broader adoption
of precision medicine in oncology.