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Purpose <p>This study aims to assess the computed tomography (CT) features of transcription factor E3 gene fusion renal cell carcinoma (TFE3-RCC) and develop a radiomics-based model for preoperative discrimination between TFE3-RCCs and clear cell renal cell carcinomas (ccRCCs).</p> Methods <p>A total of 34 pathologically confirmed TFE3-RCC and 98 ccRCC cases with preoperative renal CT were enrolled. Subjective image analysis was performed to determine the CT parameters that can distinguish TFE3-RCCs from ccRCCs. A random forest classifier was evaluated through 5-fold cross-validation to establish the radiomics model. Diagnostic performance was assessed using receiver operating characteristic (ROC) curve analysis.</p> Results <p>Patients with TFE3-RCC were significantly younger (36.2 ± 14.4 vs. 56.8 ± 13.5 years, <i>P</i> &lt; 0.001). Ten of the 34 TFE3-RCCs had calcification, whereas only four in the ccRCC group did (29.4% vs. 4.1%, <i>P</i> &lt; 0.001). Additionally, the TOC enhancement ratio in the corticomedullary phase were significantly lower in the TFE3-RCC group compared with the ccRCC group (0.61 ± 0.11 vs. 0.90 ± 0.13, <i>p</i> &lt; 0.001). The random forest classifier achieved stable performance with robust cross-validation metrics (AUC = 0.82 ± 0.17, accuracy = 85% ± 13%).</p> Conclusion <p>The CT radiomics model exhibits favorable diagnostic performance for noninvasively differentiating TFE3-RCC from ccRCC, supporting its potential utility in preoperative personalized management of renal masses.</p>

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Differentiating Xp11.2/TFE3 translocation renal cell carcinoma from clear cell carcinoma: performance of a radiomics model

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月22日

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  • Yi Liu,
  • Kexin Wang,
  • Yaofeng Zhang,
  • Jiangtao Liu,
  • Zuqiang Xi,
  • Xiangpeng Wang,
  • Yanfei Yu,
  • Xiaoying Wang

摘要

Purpose

This study aims to assess the computed tomography (CT) features of transcription factor E3 gene fusion renal cell carcinoma (TFE3-RCC) and develop a radiomics-based model for preoperative discrimination between TFE3-RCCs and clear cell renal cell carcinomas (ccRCCs).

Methods

A total of 34 pathologically confirmed TFE3-RCC and 98 ccRCC cases with preoperative renal CT were enrolled. Subjective image analysis was performed to determine the CT parameters that can distinguish TFE3-RCCs from ccRCCs. A random forest classifier was evaluated through 5-fold cross-validation to establish the radiomics model. Diagnostic performance was assessed using receiver operating characteristic (ROC) curve analysis.

Results

Patients with TFE3-RCC were significantly younger (36.2 ± 14.4 vs. 56.8 ± 13.5 years, P < 0.001). Ten of the 34 TFE3-RCCs had calcification, whereas only four in the ccRCC group did (29.4% vs. 4.1%, P < 0.001). Additionally, the TOC enhancement ratio in the corticomedullary phase were significantly lower in the TFE3-RCC group compared with the ccRCC group (0.61 ± 0.11 vs. 0.90 ± 0.13, p < 0.001). The random forest classifier achieved stable performance with robust cross-validation metrics (AUC = 0.82 ± 0.17, accuracy = 85% ± 13%).

Conclusion

The CT radiomics model exhibits favorable diagnostic performance for noninvasively differentiating TFE3-RCC from ccRCC, supporting its potential utility in preoperative personalized management of renal masses.