Abstract
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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1142-1151 |
| Number of pages | 10 |
| Journal | Abdominal Radiology |
| Volume | 41 |
| Issue number | 6 |
| DOIs | |
| State | Published - Jun 1 2016 |
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Keywords
- Angiomyolipoma
- Computed tomography
- Kidney
- Renal cell carcinoma
ASJC Scopus subject areas
- Radiology Nuclear Medicine and imaging
- Gastroenterology
- Urology
- Radiological and Ultrasound Technology
Cite this
CT negative attenuation pixel distribution and texture analysis for detection of fat in small angiomyolipoma on unenhanced CT. / Takahashi, Naoki; Takeuchi, Mitsuru; Sasaguri, Kohei; Leng, Shuai; Froemming, Adam; Kawashima, Akira.
In: Abdominal Radiology, Vol. 41, No. 6, 01.06.2016, p. 1142-1151.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - CT negative attenuation pixel distribution and texture analysis for detection of fat in small angiomyolipoma on unenhanced CT
AU - Takahashi, Naoki
AU - Takeuchi, Mitsuru
AU - Sasaguri, Kohei
AU - Leng, Shuai
AU - Froemming, Adam
AU - Kawashima, Akira
PY - 2016/6/1
Y1 - 2016/6/1
N2 - 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.
AB - 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.
KW - Angiomyolipoma
KW - Computed tomography
KW - Kidney
KW - Renal cell carcinoma
UR - http://www.scopus.com/inward/record.url?scp=84975259888&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84975259888&partnerID=8YFLogxK
U2 - 10.1007/s00261-016-0714-y
DO - 10.1007/s00261-016-0714-y
M3 - Article
C2 - 27015866
AN - SCOPUS:84975259888
VL - 41
SP - 1142
EP - 1151
JO - Abdominal Radiology
JF - Abdominal Radiology
SN - 2366-004X
IS - 6
ER -