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

Abstract

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.

The feasibility of differentiating colorectal cancer from normal and inflammatory thickening colon wall using CT texture analysis

“…In our study, we used two-dimensional TA on ROIs in three axial sections rather than three-dimensional analysis on the entire thickened colon volume, which would be less sensitive to lesions or colon wall variations. We chose to use two-dimensional CT images, which are easy and convenient to access in the clinic, and some previous work has not verified whether three-dimensional TA is better than two-dimensional TA 30 . Our future research will focus on increasing the number of cases and comparing the test efficiency between three-dimensional TA and two-dimensional TA.…”

Review of Value of CT Texture Analysis and Machine Learning in Differentiating Fat-Poor Renal Angiomyolipoma from Renal Cell Carcinoma

“…The sensitivity and specificity of identifying RCC ranged from 87% to 93% and from 63% to 75%, respectively ( 5 ). Yang et al applied least absolute shrinkage and selection operator LR to develop a 2D texture model (AUC, 0.811; 95% CI, 0.695–0.927) and a 3D texture model (AUC, 0.915; 95% CI, 0.838–0.993), which showed good discrimination and calibration in distinguishing fp-AML from clear cell RCC ( 53 ). Takahashi et al built a LR model with entropy, demographic data, shape features, and subjective heterogeneity factors and differentiated small fp-AML from RCC with a sensitivity and specificity of 50% and 98%, respectively ( 49 ).…”

RadCloud—An Artificial Intelligence-Based Research Platform Integrating Machine Learning-Based Radiomics, Deep Learning, and Data Management

“…In addition, the RadCloud platform help build CT radiomics models for distinguishing benign rental angiomyolipoma without visible fat from malignant ccRCC [19], discriminating low-grade and highgrade ccRCCs [20,21], differentiating sarcomatoidrenal cell carcinoma (SRCC) and ccRCC [22], and distinguishing benign fat-poor angiomyolipoma (fpAML) from malignant chromophobe renal cell carcinoma (chRCC) [23].…”