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<p>This study assessed the value of radiomics analysis in differentiating clear cell renal cell carcinoma (ccRCC) from renal oncocytoma (RO) using multi-phase contrast-enhanced CT. A retrospective analysis included 43 ccRCC and 43 RO cases (2013–2024). Preoperative three-phase CT scans (corticomedullary [CP], nephrographic [NP], excretory [EP]) were analyzed. Tumor regions of interest (ROIs) were semi-automatically segmented in 3D-Slicer, with texture features extracted via IBEX software. Receiver operating characteristic (ROC) curves and area under the curve (AUC) values were calculated for selected parameters in each phase. A support vector machine (SVM) classifier trained on texture parameters underwent diagnostic evaluation via ROC analysis. All phases showed high diagnostic accuracy (AUC &gt; 0.9), with NP demonstrating the highest performance (AUC = 0.952; accuracy, 0.88; sensitivity, 0.91; specificity, 0.87). Intensity histogram IH_Skewness differed significantly between ccRCC and RO in CP and NP (<i>P</i> &lt; 0.01 for both), with AUC values of 0.75 (CP) and 0.79 (NP). Combining LASSO dimension reduction with SVM using multi-phase CT radiomics features enabled the effective differentiation between ccRCC and RO, highlighting texture analysis as a promising clinical tool.</p>

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Radiomics to Differentiate Renal Oncocytoma from Clear Cell Renal Cell Carcinoma on Contrast-Enhanced CT: A Preliminary Study

  • 期刊论文

  • 发表日期：

    2026年2月2日

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  • Fang Liu,
  • Longwei Jia,
  • Xiaoming Zhou,
  • Lan Yu

摘要

This study assessed the value of radiomics analysis in differentiating clear cell renal cell carcinoma (ccRCC) from renal oncocytoma (RO) using multi-phase contrast-enhanced CT. A retrospective analysis included 43 ccRCC and 43 RO cases (2013–2024). Preoperative three-phase CT scans (corticomedullary [CP], nephrographic [NP], excretory [EP]) were analyzed. Tumor regions of interest (ROIs) were semi-automatically segmented in 3D-Slicer, with texture features extracted via IBEX software. Receiver operating characteristic (ROC) curves and area under the curve (AUC) values were calculated for selected parameters in each phase. A support vector machine (SVM) classifier trained on texture parameters underwent diagnostic evaluation via ROC analysis. All phases showed high diagnostic accuracy (AUC > 0.9), with NP demonstrating the highest performance (AUC = 0.952; accuracy, 0.88; sensitivity, 0.91; specificity, 0.87). Intensity histogram IH_Skewness differed significantly between ccRCC and RO in CP and NP (P < 0.01 for both), with AUC values of 0.75 (CP) and 0.79 (NP). Combining LASSO dimension reduction with SVM using multi-phase CT radiomics features enabled the effective differentiation between ccRCC and RO, highlighting texture analysis as a promising clinical tool.