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Differentiating pheochromocytoma from lipid-poor adrenocortical adenoma by CT texture analysis: feasibility study

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Abstract

Objectives

To investigate the feasibility of using CT texture analysis (CTTA) to differentiate pheochromocytoma from lipid-poor adrenocortical adenoma (lp-ACA).

Methods

Ninety-eight pheochromocytomas and 66 lp-ACAs were included in this retrospective study. CTTA was performed on unenhanced and enhanced images. Receiver operating characteristic (ROC) analysis was performed, and the area under the ROC curve (AUC) was calculated for texture parameters that were significantly different for the objective. Diagnostic accuracies were evaluated using the cutoff values of texture parameters with the highest AUCs.

Results

Compared to lp-ACAs, pheochromocytomas had significantly higher mean gray-level intensity (Mean), entropy, and mean of positive pixels (MPP), but lower skewness and kurtosis on unenhanced images (P < 0.001). On enhanced images, these texture-quantifiers followed a similar trend where Mean, entropy, and MPP were higher, but skewness and kurtosis were lower in pheochromocytomas. Standard deviation (SD) was also significantly higher in pheochromocytomas on enhanced images. Mean and MPP quantified from no filtration on unenhanced CT images yielded the highest AUC of 0.86 ± 0.03 (95% CI 0.81–0.91) at a cutoff value of 34.0 for Mean and MPP, respectively (sensitivity = 79.6%, specificity = 83.3%, accuracy = 81.1%).

Conclusions

It was feasible to use CTTA to differentiate pheochromocytoma from lp-ACA.

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References

  1. Korivi BR (2013) Cross-sectional imaging work-up of adrenal masses. World J Radiol 5:88. doi:10.4329/wjr.v5.i3.88

    Article  PubMed  PubMed Central  Google Scholar 

  2. Bovio S, Cataldi A, Reimondo G, et al. (2006) Prevalence of adrenal incidentaloma in a contemporary computerized tomography series. J Endocrinol Invest 29:298–302. doi:10.1007/BF03344099

    Article  CAS  PubMed  Google Scholar 

  3. Lattin GE, Sturgill ED, Tujo CA, et al. (2014) From the radiologic pathology archives: adrenal tumors and tumor-like conditions in the adult: radiologic-pathologic correlation. Radiographics 34:805–829. doi:10.1148/rg.343130127

    Article  PubMed  Google Scholar 

  4. Johnson PT, Horton KM, Fishman EK (2009) Adrenal mass imaging with multidetector CT: pathologic conditions, pearls, and pitfalls. Radiographics 29:1333–1351. doi:10.1148/rg.295095027

    Article  PubMed  Google Scholar 

  5. Blake MA, Krishnamoorthy SK, Boland GW, et al. (2003) Low-density pheochromocytoma on CT: a Mimicker of adrenal adenoma. Am J Roentgenol 181:1663–1668. doi:10.2214/AJR.181.6.1811663

    Article  Google Scholar 

  6. Patel J, Davenport MS, Cohan RH, Caoili EM (2013) Can established CT attenuation and washout criteria for adrenal adenoma accurately exclude pheochromocytoma? Am J Roentgenol 201:122–127. doi:10.2214/AJR.12.9620

    Article  Google Scholar 

  7. Schieda N, Alrashed A, Flood TA, et al. (2016) Comparison of quantitative MRI and CT washout analysis for differentiation of adrenal pheochromocytoma from adrenal adenoma. Am J Roentgenol 206:1–8. doi:10.2214/AJR.15.15318

    Article  Google Scholar 

  8. Allen BC, Francis IR (2015) Adrenal imaging and intervention. Radiol Clin North Am 53:1021–1035. doi:10.1016/j.rcl.2015.05.004

    Article  PubMed  Google Scholar 

  9. Zeiger MA, Thompson GB, Duh Q, et al. (2009) The american association of clinical endocrinologists and american association of endocrine surgeons medical guidelines for the management of adrenal incidentalomas. Endocr Pract 15(suppl 1):1–20. doi:10.4158/EP.15.S1.1

    Article  PubMed  Google Scholar 

  10. Young WF Jr (2007) Clinical practice: the incidentally discovered adrenal mass. N Engl J Med 356:601–610

    Article  CAS  PubMed  Google Scholar 

  11. Garrett RW, Nepute JC, El Hayek M, Albert SG (2016) Adrenal incidentalomas: clinical controversies and modified recommendations. Am J Roentgenol 206:1170–1178. doi:10.2214/AJR.15.15475

    Article  Google Scholar 

  12. Hayano K, Tian F, Kambadakone AR, et al. (2015) Texture analysis of non-contrast-enhanced computed tomography for assessing angiogenesis and survival of soft tissue sarcoma. J Comput Assist Tomogr 39:607–612. doi:10.1097/RCT.0000000000000239

    Article  PubMed  PubMed Central  Google Scholar 

  13. Tian F, Hayano K, Kambadakone AR, Sahani DV (2015) Response assessment to neoadjuvant therapy in soft tissue sarcomas: using CT texture analysis in comparison to tumor size, density, and perfusion. Abdom Imaging 40:1705–1712

    Article  PubMed  Google Scholar 

  14. Dang M, Lysack JT, Wu T, et al. (2015) MRI texture analysis predicts p53 status in head and neck squamous cell carcinoma. AJNR Am J Neuroradiol 36:166–170

    Article  CAS  PubMed  Google Scholar 

  15. Ganeshan B, Goh V, Mandeville HC, Hoskin PJ, Miles KA (2013) Non-small cell lung cancer: histopathologic correlates for texture. Radiology 266:326–336

    Article  PubMed  Google Scholar 

  16. Hodgdon T, Mcinnes MDF, Schieda N, et al. (2015) Can quantitative CT texture analysis be used to differentiate fat-poor renal angiomyolipoma from renal cell carcinoma on unenhanced CT images? Radiology 276:787–796

    Article  PubMed  Google Scholar 

  17. Lubner MG, Stabo N, Abel EJ, et al. (2016) CT textural analysis of large primary renal cell carcinomas: pretreatment tumor heterogeneity correlates with histologic findings and clinical outcomes. Am J Roentgenol 207:96–105. doi:10.2214/AJR.15.15451

    Article  Google Scholar 

  18. Zhang GM, Sun H, Shi B, Jin ZY, Xue HD (2017) Quantitative CT texture analysis for evaluating histologic grade of urothelial carcinoma. Abdom Radiol (NY) 42:561–568. doi:10.1007/s00261-016-0897-2

    Article  Google Scholar 

  19. Boland GW, Lee MJ, Gazelle GS, et al. (1998) Characterization of adrenal masses using unenhanced CT: an analysis of the CT literature. Am J Roentgenol 171:201–204

    Article  CAS  Google Scholar 

  20. Miles KA, Ganeshan B, Hayball MP (2013) CT texture analysis using the filtration-histogram method: what do the measurements mean? Cancer Imaging 13:400–406. doi:10.1102/1470-7330.2013.9045

    Article  PubMed  PubMed Central  Google Scholar 

  21. Davnall F, Yip CSP, Ljungqvist G, et al. (2012) Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice? Insights Imaging 3:573–589. doi:10.1007/s13244-012-0196-6

    Article  PubMed  PubMed Central  Google Scholar 

  22. Ng F, Ganeshan B, Kozarski R, Miles KA, Goh V (2013) Assessment of primary colorectal cancer heterogeneity by using whole-tumor texture analysis: contrast-enhanced CT texture as a biomarker of 5-year survival. Radiology 266:177–184

    Article  PubMed  Google Scholar 

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Acknowledgements

Z. Y. Jin has received research grants from the Ministry of Health in China.

Author information

Author notes

  1. Gu-Mu-Yang Zhang and Bing Shi have contributed equally to this study.

Authors and Affiliations

  1. Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Shuaifuyuan No. 1, Wangfujing Street, Dongcheng District, Beijing, 100730, China

    Gu-Mu-Yang Zhang, Bing Shi, Hao Sun, Zheng-Yu Jin & Hua-Dan Xue

Authors
  1. Gu-Mu-Yang Zhang
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  2. Bing Shi
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  3. Hao Sun
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  4. Zheng-Yu Jin
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Corresponding authors

Correspondence to Hao Sun, Zheng-Yu Jin or Hua-Dan Xue.

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Funding

This study was funded by the Health Industry Special Scientific Research Project of China (Grant No.: 201402019).

Conflict of interest

The authors declare that they have 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 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

For this type of study, informed consent was waived by the institutional review board.

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Zhang, GMY., Shi, B., Sun, H. et al. Differentiating pheochromocytoma from lipid-poor adrenocortical adenoma by CT texture analysis: feasibility study. Abdom Radiol 42, 2305–2313 (2017). https://doi.org/10.1007/s00261-017-1118-3

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  • DOI: https://doi.org/10.1007/s00261-017-1118-3

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