Related Experiment Video For Angiomyolipoma
Updated: Mar 23, 2026
Novel In Vivo Micro-Computed Tomography Imaging Techniques for Assessing the Progression of Non-Alcoholic Fatty Liver Disease
Published on: March 24, 2023
CT negative attenuation pixel distribution and texture analysis for detection of fat in small angiomyolipoma on
Naoki Takahashi1, Mitsuru Takeuchi2, Kohei Sasaguri2
1Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA. takahashi.naoki@mayo.edu.
Purpose:
The purpose of the paper is to evaluate if CT pixel distribution and texture analysis can identify fat in angiomyolipoma (AML) on unenhanced CT.
Methods:
Thirty-seven patients with 38 AMLs and 75 patients with 83 renal cell carcinomas (RCCs) were evaluated. Region of interest (ROI) was manually placed over renal mass on unenhanced CT. In-house software generated multiple overlapping small-ROIs of various sizes within whole-lesion-ROI. Maximal number of pixels under cutoff attenuation values in the multiple small-ROIs was calculated. Skewness of CT attenuation histogram was calculated from whole-lesion-ROI. Presence of fat in renal mass was also evaluated subjectively. Performance of subjective evaluation and objective methods for identifying fat was compared using McNemar test.
Results:
Macroscopic fat was identified in 15/38 AMLs and 1/83 RCCs by both subjective evaluation and by CT negative pixel distribution analysis (p = 1.0). Optimal threshold was ≥6 pixels below -30 HU within 13-pixel-ROI. Skewness of < -0.4 in whole-lesion-ROI identified fat in 10/38 AMLs and 0/83 RCCs. By combining CT negative pixel distribution analysis and skewness, fat was identified in 20/38 AMLs and 1/83 RCCs, but the difference to the subjective method was not statistically significant (p = 0.07).
Conclusion:
CT negative attenuation pixel distribution analysis does not identify fat in AML beyond subjective evaluation. Addition of skewness by texture analysis may help improve identifying fat in AML.
