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CT texture analysis for the differentiation of papillary renal cell carcinoma subtypes

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

Abstract

Purpose

The objective of this study was to investigate whether computed tomography texture analysis can be used to differentiate papillary renal cell carcinoma (PRCC) subtypes.

Method

Sixty-two PRCC tumors were retrospectively evaluated, with 30 type 1 tumors and 32 type 2 tumors. Texture parameters quantified from three-phase contrast-enhanced CT images were compared with least absolute shrinkage and selection operator (LASSO) regression. Receiver operating characteristic (ROC) analysis was performed, and the area under the ROC curve (AUC) was calculated for each parameter. The selected texture parameters of each phase were used to generate support vector machine (SVM) classifiers. Decision curve analysis (DCA) of the classification was performed.

Results

The two texture parameters with the top two AUC values were – 333–7 Correlation (AUC = 0.772) and 45–7 Entropy (AUC = 0.753) in the corticomedullary phase, 333–4 Correlation (AUC = 0.832) and 45–7 Entropy (AUC = 0.841) in the nephrographic phase, and 135–7 Entropy (AUC = 0.858) and – 333–1 InformationMeasureCorr2 (AUC = 0.849) in the excretory phase. Entropy and Correlation have a high correlation with the two types of PRCC and are increased in type 2 PRCC. A model incorporating the texture parameters with the top two AUC values in each phase produced an AUC of 0.922 with an accuracy of 84% (sensitivity = 89% and specificity = 80%). The nephrographic-phase model and the model combining the texture parameters of the three phases can differentiate the two types with the largest net benefit.

Conclusions

Computed tomography texture analysis can be used to distinguish type 2 PRCC from type 1 with high accuracy, which may be clinically important.

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Abbreviations

AUC:

Area under curve

CT:

Computed tomography

DCA:

Decision curve analysis

ICC:

Intraclass correlation coefficient

LASSO:

Least absolute shrinkage and selection operator

MRI:

Magnetic resonance imaging

PRCC:

Papillary renal cell carcinoma

RCC:

Renal cell carcinoma

ROC:

Receiver operating characteristic

ROI:

Region of interest

SVM:

Support vector machine

TA:

Texture analysis

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Authors and Affiliations

  1. Department of Radiology, The Affiliated Hospital of Qingdao University, No. 1677, Wu Tai Shan Road, Huangdao District, Qingdao, Shandong, China

    Chongfeng Duan, Lei Niu, Gang Wang, Jiping Zhao, Fang Liu, Xuejun Liu, Yande Ren & Xiaoming Zhou

  2. Department of Information Management, The Affiliated Hospital of Qingdao University, Qingdao, China

    Nan Li

Authors
  1. Chongfeng Duan
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  2. Nan Li
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  3. Lei Niu
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  4. Gang Wang
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  5. Jiping Zhao
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  6. Fang Liu
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  7. Xuejun Liu
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  8. Yande Ren
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  9. Xiaoming Zhou
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Correspondence to Xiaoming Zhou.

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Cite this article

Duan, C., Li, N., Niu, L. et al. CT texture analysis for the differentiation of papillary renal cell carcinoma subtypes. Abdom Radiol 45, 3860–3868 (2020). https://doi.org/10.1007/s00261-020-02588-2

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  • DOI: https://doi.org/10.1007/s00261-020-02588-2

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