Contrast-Enhanced CT Texture Analysis for Distinguishing Fat-Poor Renal Angiomyolipoma From Chromophobe Renal Cell Carcinoma

Objective: To evaluate the value of 2-dimensional (2D) and 3-dimensional (3D) computed tomography texture analysis (CTTA) models in distinguishing fat-poor angiomyolipoma (fpAML) from chromophobe renal cell carcinoma (chRCC). Methods: We retrospectively enrolled 32 fpAMLs and 24 chRCCs. Texture feat...

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Bibliographic Details
Published in:Molecular imaging 2019, Vol.18, p.1536012119883161-1536012119883161
Main Authors: Yang, Guangjie, Gong, Aidi, Nie, Pei, Yan, Lei, Miao, Wenjie, Zhao, Yujun, Wu, Jie, Cui, Jingjing, Jia, Yan, Wang, Zhenguang
Format: Article
Language:English
Subjects:
Algorithms
Angiomyolipoma
Artificial Intelligence in Molecular Imaging Clinics
Calibration
Computed tomography
Confidence intervals
Decision analysis
Diagnostic systems
Feature extraction
Image contrast
Image enhancement
Kidney cancer
Medical imaging
Renal cell carcinoma
Texture
Three dimensional models
Two dimensional models
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Summary:Objective: To evaluate the value of 2-dimensional (2D) and 3-dimensional (3D) computed tomography texture analysis (CTTA) models in distinguishing fat-poor angiomyolipoma (fpAML) from chromophobe renal cell carcinoma (chRCC). Methods: We retrospectively enrolled 32 fpAMLs and 24 chRCCs. Texture features were extracted from 2D and 3D regions of interest in triphasic CT images. The 2D and 3D CTTA models were constructed with the least absolute shrinkage and selection operator algorithm and texture scores were calculated. The diagnostic performance of the 2D and 3D CTTA models was evaluated with respect to calibration, discrimination, and clinical usefulness. Results: Of the 177 and 183 texture features extracted from 2D and 3D regions of interest, respectively, 5 2D features and 8 3D features were selected to build 2D and 3D CTTA models. The 2D CTTA model (area under the curve [AUC], 0.811; 95% confidence interval [CI], 0.695-0.927) and the 3D CTTA model (AUC, 0.915; 95% CI, 0.838-0.993) showed good discrimination and calibration (P > .05). There was no significant difference in AUC between the 2 models (P = .093). Decision curve analysis showed the 3D model outperformed the 2D model in terms of clinical usefulness. Conclusions: The CTTA models based on contrast-enhanced CT images had a high value in differentiating fpAML from chRCC.
ISSN:1536-0121
1535-3508
1536-0121
DOI:10.1177/1536012119883161