# Computed Tomography-Based Radiomics Diagnostic Model for Fat-Poor Small Renal Tumor Subtypes

## TL;DR

This study uses CT-based radiomics and machine learning to accurately classify subtypes of small renal tumors, improving diagnostic precision in renal oncology.

## Contribution

The novel contribution is a radiomics-based machine learning model using single-phase CT scans for classifying fat-poor renal tumor subtypes.

## Key findings

- XGBoost achieved the best classification performance with an average AU-PRC of 0.757.
- The model showed strong performance for angiomyolipoma with an AU-ROC of 0.824.
- Single-phase CT and feature optimization proved effective for tumor subtype classification.

## Abstract

Background: Differentiating histologic subtypes of fat-poor small renal masses using conventional imaging remains difficult due to their overlapping radiologic characteristics. We aimed to develop a machine learning-based diagnostic model using CT-derived radiomic features to classify the five most common renal tumor subtypes: clear cell RCC (ccRCC), papillary RCC (pRCC), chromophobe RCC (chRCC), angiomyolipoma (AML), and oncocytoma. Methods: A total of 499 patients with pathologically confirmed renal tumors who underwent preoperative contrast-enhanced CT and nephrectomy were retrospectively analyzed. Results: We extracted and analyzed radiomic features from 1548 multi-phase CT scans from 499 patients, focusing on fat-poor tumors. Five machine learning classifiers including Linear SVM, Rbf SVM, Random Forest, and XGBoost were involved. Among the models, XGBoost showed the best classification performance, with an average AU-PRC: mean = 0.757, standard error = 0.033 and a renal angiomyolipoma-specific AU-ROC: mean = 0.824, standard error = 0.023. These results outperformed other single-phase CT radiomic feature-based machine learning models trained with 20% of principal components. Conclusions: This study demonstrates the effectiveness of radiomics-based machine learning in classifying renal tumor subtypes and highlights the potential of AI in medical imaging. The findings, particularly the utility of single-phase CT and feature optimization, offer valuable insights for future precision medicine approaches. Such methods may support more personalized diagnosis and treatment planning in renal oncology.