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CT negative attenuation pixel distribution and texture analysis for detection of fat in small angiomyolipoma on unenhanced CT

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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.

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Authors and Affiliations

  1. Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA

    Naoki Takahashi, Mitsuru Takeuchi, Kohei Sasaguri, Shuai Leng, Adam Froemming & Akira Kawashima

Authors
  1. Naoki Takahashi
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  2. Mitsuru Takeuchi
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  3. Kohei Sasaguri
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  4. Shuai Leng
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  5. Adam Froemming
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  6. Akira Kawashima
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Corresponding author

Correspondence to Naoki Takahashi.

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Conflict of interest

All authors declare that he/she has no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards.

Appendix

Appendix

$${\text{Entropy}} = \mathop \sum \limits_{{i = i_{\hbox{min} } }}^{{i_{\hbox{max} } }} P\left( i \right)*\log_{2} p(i),$$

where p(i) is the probability of image pixels having CT number of i, with i ranging from minimal CT number i min to maximal CT number i max.

$${\text{Skewness}} = \frac{{\frac{1}{n}\mathop \sum \nolimits_{i = 1}^{n} \left( {x_{i} - \bar{x}} \right)^{3} }}{{\left\{ {\left. {\frac{1}{n}\mathop \sum \nolimits_{i = 1}^{n} \left( {x_{i} - \bar{x}} \right)^{2} } \right\}} \right.^{{\frac{3}{2}}} }}$$
$${\text{Kurtosis}} = \frac{{\frac{1}{n}\mathop \sum \nolimits_{i = 1}^{n} \left( {x_{i} - \bar{x}} \right)^{4} }}{{\left\{ {\left. {\frac{1}{n}\mathop \sum \nolimits_{i = 1}^{n} \left( {x_{i} - \bar{x}} \right)^{2} } \right\}} \right.^{2} }},$$

where \(x_{i}\) is the CT number of each pixel, \(\bar{x}\) is the mean of CT number and n is the number of pixels within the ROI.

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Takahashi, N., Takeuchi, M., Sasaguri, K. et al. CT negative attenuation pixel distribution and texture analysis for detection of fat in small angiomyolipoma on unenhanced CT. Abdom Radiol 41, 1142–1151 (2016). https://doi.org/10.1007/s00261-016-0714-y

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