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A preliminary radiomics model for predicting perirenal fat invasion on renal cell carcinoma with contrast-enhanced CT images

  • Kidneys, Ureters, Bladder, Retroperitoneum
  • Published:
Abdominal Radiology Aims and scope Submit manuscript

Abstract

Objective

The aim is to develop a radiomics model based on contrast-enhanced CT scans for preoperative prediction of perirenal fat invasion (PFI) in patients with renal cell carcinoma (RCC).

Methods

The CT data of 131 patients with pathology-confirmed PFI status (64 positives) were retrospectively collected and randomly assigned to the training and test datasets. The kidneys and the masses were annotated by semi-automatic segmentation. Eight types of regions of interest (ROI) were chosen for the training of the radiomics models. The areas under the curves (AUCs) from the receiver operating characteristic (ROC) curve analysis were used to analyze the diagnostic performance. Eight types of models with different ROIs have been developed. The models with the highest AUC in the test dataset were used for construction of the corresponding final model, and comparison with radiologists’ diagnosis.

Results

The AUCs of the models for each ROI was 0.783–0.926, and there was no statistically significant difference between them (P > 0.05). Model 4 was using the ROI of the outer half-ring which extended along the edge of the mass at the outer edge of the kidney into the perirenal fat space with a thickness of 3 mm. It yielded the highest AUC (0.926) and its diagnostic accuracy was higher than the radiologists’ diagnosis.

Conclusion

We have developed and validated a radiomics model for prediction of PFI on RCC with contrast-enhanced CT scans. The model proved to be more accurate than the radiologists’ diagnosis.

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Funding

This study has received funding by the Scientific Research Seed Fund of Peking University First Hospital [grant numbers 2021SF29].

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Author notes

  1. Jia Liu and Zhiyong Lin both have contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, Peking University First Hospital, No. 8, Xishiku Street, Xicheng District, Beijing, 100034, China

    Jia Liu, Zhiyong Lin, Xiaodong Zhang, He Wang & Xiaoying Wang

  2. School of Basic Medical Sciences, Capital Medical University, Beijing, 100069, China

    Kexin Wang

  3. Department of Urology, Peking University First Hospital, Beijing, 100034, China

    Dong Fang

  4. Beijing Smart Tree Medical Technology Co. Ltd, Beijing, 100011, China

    Yaofeng Zhang & Xiangpeng Wang

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  1. Jia Liu
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Correspondence to He Wang.

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Conflict of interest

Co-author Yaofeng Zhang and Xiangpeng Wang are from a medical technical corporation provided technical support for model development. The data from this study was analyzed and controlled by authors who are from Peking University First Hospital. The authors declare that they have no conflict of interest.

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Liu, J., Lin, Z., Wang, K. et al. A preliminary radiomics model for predicting perirenal fat invasion on renal cell carcinoma with contrast-enhanced CT images. Abdom Radiol 48, 649–658 (2023). https://doi.org/10.1007/s00261-022-03699-8

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  • DOI: https://doi.org/10.1007/s00261-022-03699-8

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