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Radiomics analysis based on single phase and different phase combinations of radiomics features from tri-phasic CT to distinguish renal oncocytoma from chromophobe renal cell carcinoma

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

Abstract

Objectives

To investigate different radiomics models based on single phase and the different phase combinations of radiomics features from 3D tri-phasic CT to distinguish RO from chRCC.

Methods

A total of 96 patients (30 RO and 66 chRCC) were enrolled in this study. Radiomics features were extracted from unenhanced phase (UP), corticomedullary phase (CMP), and nephrographic phase (NP) CT images. Feature selection was based on the least absolute shrinkage and selection operator regression (LASSO) method. The selected features were used to develop different radiomics models using logistic regression (LR) analysis, including model 1 (UP), model 2(CMP), model 3(NP), model 4(UP+CMP), model 5(UP+NP), model 6(CMP+NP), and model 7(UP+CMP+NP). The radiomics model demonstrating the highest discrimination performance was utilized to construct the combined model (model 8) with clinical factors. A nomogram based on the model 8 was established. To evaluate the diagnostic performance of the different models, the receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used. Delong's test was utilized to assess the statistical significance of the AUC improvement across the models.

Results

Among the seven radiomics models, model 7 exhibited the highest AUC of 0.84 (95% CI 0.69, 0.99), and model 7 demonstrated a significantly superior AUC compared to the other radiomics models (all P < 0.05). The AUC values of radiomics models based on two phases (model4, mode5, mode6) were greater than the models based on single phase (model1, mode2, mode3) (all P < 0.05). Model 3 illustrated the best performance of the three radiomics models based on single phase with an AUC of 0.76 (95% CI 0.57, 099). Model 6 illustrated the best performance of the three radiomics models based on two-phases combination with an AUC of 0.83 (0.66, 0.99). Model 8 achieved an AUC of 0.93 (95% CI 0.83, 1.00) which is higher than those all radiomics models.

Conclusion

Radiomics models based on combination of radiomics features from UP, CMP, and NP can be a useful and promising technique to differentiate RO from chRCC. Moreover, the model combining clinical factors and radiomics features showed better classification performance to distinguish them.

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Author notes

  1. Suping Yang and Yuanxi Jian contributed equally to this work and share first authorship.

Authors and Affiliations

  1. Department of Radiology, The Second Affiliated Hospital of Kunming Medical University, Kunming, China

    Suping Yang, Yuanxi Jian, Fan Yang, Rui Liu, Wenqing Zhang, Jiaping Wang, Xin Tan, Junlin Wu, Yuan Chen & Xiaowen Zhou

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  1. Suping Yang
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  2. Yuanxi Jian
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  3. Fan Yang
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  5. Wenqing Zhang
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Correspondence to Yuanxi Jian.

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Yang, S., Jian, Y., Yang, F. et al. Radiomics analysis based on single phase and different phase combinations of radiomics features from tri-phasic CT to distinguish renal oncocytoma from chromophobe renal cell carcinoma. Abdom Radiol 49, 182–191 (2024). https://doi.org/10.1007/s00261-023-04053-2

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  • DOI: https://doi.org/10.1007/s00261-023-04053-2

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