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Background <p>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.</p> Methods <p>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.</p> Results <p>Tumor size was the strongest radiological predictor of miR-15a expression (adjusted R<sup>2</sup> = 0.8281, <i>p</i> &lt; 0.001). High miR-15a levels correlated with aggressive features, including necrosis and nodular enhancement (<i>p</i> &lt; 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 (R<sup>2</sup> = 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.</p> Conclusions <p>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.</p>

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Machine learning-assisted radiogenomic analysis for miR-15a expression prediction in renal cell carcinoma

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月20日

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  • Yulian Mytsyk,
  • Paweł Kowal,
  • Yuriy Kobilnyk,
  • Mateusz Lesny,
  • Michał Skrzypczyk,
  • Dmytro Stroj,
  • Victor Dosenko,
  • Olena Kucheruk

摘要

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.