Hyperattenuating adrenal lesions in lung cancer: biphasic CT with unenhanced and 1-min enhanced images reliably predicts benign lesions

Abstract

Objectives

To investigate usefulness of biphasic computed tomography (CT) in characterizing hyperattenuating adrenal lesions in lung cancer.

Methods

This retrospective study included 239 patients with lung cancer who underwent adrenal CT for hyperattenuating (> 10 Hounsfield unit) adrenal lesions. Adrenal CT comprised unenhanced and 1-min and 15-min enhanced images. We dichotomized adrenal lesions depending on benign or metastatic lesions. Reference standard for benignity was histologic confirmation or ≥ 6-month stability on follow-up CT. Two independent readers analyzed absolute (APW) or relative percentage wash-out (RPW) using triphasic CT, and enhancement ratio (ER) or percentage wash-in (PWI) using biphasic CT (i.e., unenhanced and 1-min enhanced CT). Criteria for benignity were as follows: criteria 1, (a) APW ≥ 60% or (b) RPW ≥ 40%, and criteria 2, (a) ER > 3 and (b) PWI > 200%. We analyzed area under the curve (AUC) and accuracy for benignity, and inter-reader agreement.

Results

Proportion of benign adrenal lesion was 71.1% (170/239). For criteria 1 and 2, AUCs were 0.872 (95% confidence interval [CI], 0.822–0.911) and 0.886 (95% CI, 0.838–0.923), respectively, for reader 1 (p = 0.566) and 0.816 (95% CI, 0.761–0.863) and 0.814 (95% CI, 0.759–0.862), respectively, for reader 2 (p = 0.955), and accuracies were 87.9% (210/239) and 86.2% (206/239), respectively, for reader 1 (p = 0.479) and 81.2% (194/239) and 80.3% (192/239), respectively, for reader 2 (p = 0.763). Weighted kappa was 0.725 (95% CI, 0.634–0.816) for criteria 1 and 0.736 (95% CI, 0.649–0.824) for criteria 2.

Conclusion

Biphasic CT can reliably characterize hyperattenuating adrenal lesions in patients with lung cancer.

Key Points

• Criteria from biphasic computed tomography (CT) for diagnosing benign adrenal lesions were enhancement ratio of > 3 and percentage wash-in of > 200%.

• In the analysis by two independent readers, area under the curve between criteria 1 and 2 was not significantly different (0.872 and 0.886 for reader 1; 0.816 and 0.814, for reader 2; p > 0.05 for each comparison).

• Wash-in characteristics from biphasic CT are helpful to predict benign adrenal lesions in lung cancer.

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Abbreviations

APW:

Absolute percentage wash-out

CECT:

Contrast-enhanced computed tomography

CT:

Computed tomography

ER:

Enhancement ratio

FDG:

Fluorodeoxyglucose

HU:

Hounsfield unit

LOESS:

Locally estimated scatterplot smoothing

NPV:

Negative predictive value

PET:

Positron emission tomography

PPV:

Positive predictive value

PWI:

Percentage wash-in

ROI:

Region of interest

RPW:

Relative percentage wash-out

UCT:

Unenhanced computed tomography

References

  1. 1.

    Riihimaki M, Hemminki A, Fallah M et al (2014) Metastatic sites and survival in lung cancer. Lung Cancer 86:78–84

    CAS  Article  Google Scholar 

  2. 2.

    Oikawa A, Takahashi H, Ishikawa H, Kurishima K, Kagohashi K, Satoh H (2012) Application of conditional probability analysis to distant metastases from lung cancer. Oncol Lett 3:629–634

    Article  Google Scholar 

  3. 3.

    Expert Panel on Thoracic Imaging, de Groot PM, Chung JH et al (2019) ACR Appropriateness Criteria((R)) noninvasive clinical staging of primary lung cancer. J Am Coll Radiol 16:S184–S195

  4. 4.

    Cohen SL, Ward TJ, Cham MD (2020) The relationship between CT scout landmarks and lung boundaries on chest CT: guidelines for minimizing excess z-axis scan length. Eur Radiol 30:581–587

    Article  Google Scholar 

  5. 5.

    Detterbeck FC, Boffa DJ, Kim AW, Tanoue LT (2017) The eighth edition lung cancer stage classification. Chest 151:193–203

    Article  Google Scholar 

  6. 6.

    Elsayes KM, Emad-Eldin S, Morani AC, Jensen CT (2017) Practical approach to adrenal imaging. Radiol Clin North Am 55:279–301

    Article  Google Scholar 

  7. 7.

    Remer EM, Obuchowski N, Ellis JD, Rice TW, Adelstein DJ, Baker ME (2000) Adrenal mass evaluation in patients with lung carcinoma: a cost-effectiveness analysis. AJR Am J Roentgenol 174:1033–1039

    CAS  Article  Google Scholar 

  8. 8.

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

    Article  Google Scholar 

  9. 9.

    Boland GW, Dwamena BA, Jagtiani Sangwaiya M et al (2011) Characterization of adrenal masses by using FDG PET: a systematic review and meta-analysis of diagnostic test performance. Radiology 259:117–126

    Article  Google Scholar 

  10. 10.

    Fischer B, Lassen U, Mortensen J et al (2009) Preoperative staging of lung cancer with combined PET-CT. N Engl J Med 361:32–39

    CAS  Article  Google Scholar 

  11. 11.

    Vikram R, Yeung HD, Macapinlac HA, Iyer RB (2008) Utility of PET/CT in differentiating benign from malignant adrenal nodules in patients with cancer. AJR Am J Roentgenol 191:1545–1551

    Article  Google Scholar 

  12. 12.

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

    CAS  Article  Google Scholar 

  13. 13.

    Mayo-Smith WW, Song JH, Boland GL et al (2017) Management of incidental adrenal masses: a white paper of the ACR Incidental Findings Committee. J Am Coll Radiol 14:1038–1044

    Article  Google Scholar 

  14. 14.

    Schieda N, Siegelman ES (2017) Update on CT and MRI of Adrenal Nodules. AJR Am J Roentgenol 208:1206–1217

    Article  Google Scholar 

  15. 15.

    Koo HJ, Choi HJ, Kim HJ, Kim SO, Cho KS (2014) The value of 15-minute delayed contrast-enhanced CT to differentiate hyperattenuating adrenal masses compared with chemical shift MR imaging. Eur Radiol 24:1410–1420

    Article  Google Scholar 

  16. 16.

    Platzek I, Sieron D, Plodeck V, Borkowetz A, Laniado M, Hoffmann RT (2019) Chemical shift imaging for evaluation of adrenal masses: a systematic review and meta-analysis. Eur Radiol 29:806–817

    Article  Google Scholar 

  17. 17.

    Ho LM, Samei E, Mazurowski MA et al (2019) Can texture analysis be used to distinguish benign from malignant adrenal nodules on unenhanced CT, contrast-enhanced CT, or in-phase and opposed-phase MRI? AJR Am J Roentgenol 212:554–561

    Article  Google Scholar 

  18. 18.

    Shi B, Zhang GM, Xu M, Jin ZY, Sun H (2019) Distinguishing metastases from benign adrenal masses: what can CT texture analysis do? Acta Radiol 60:1553–1561

    Article  Google Scholar 

  19. 19.

    Foti G, Faccioli N, Manfredi R, Mantovani W, Mucelli RP (2010) Evaluation of relative wash-in ratio of adrenal lesions at early biphasic CT. AJR Am J Roentgenol 194:1484–1491

    Article  Google Scholar 

  20. 20.

    Boland GW (2010) Adrenal imaging: why, when, what, and how? Part 1. Why and when to image? AJR Am J Roentgenol 195:W377–W381

    Article  Google Scholar 

  21. 21.

    Boland GW, Blake MA, Hahn PF, Mayo-Smith WW (2008) Incidental adrenal lesions: principles, techniques, and algorithms for imaging characterization. Radiology 249:756–775

    Article  Google Scholar 

  22. 22.

    Pena CS, Boland GW, Hahn PF, Lee MJ, Mueller PR (2000) Characterization of indeterminate (lipid-poor) adrenal masses: use of washout characteristics at contrast-enhanced CT. Radiology 217:798–802

    CAS  Article  Google Scholar 

  23. 23.

    Yun M, Kim W, Alnafisi N, Lacorte L, Jang S, Alavi A (2001) 18F-FDG PET in characterizing adrenal lesions detected on CT or MRI. J Nucl Med 42:1795–1799

    CAS  PubMed  Google Scholar 

  24. 24.

    Chong S, Lee KS, Kim HY et al (2006) Integrated PET-CT for the characterization of adrenal gland lesions in cancer patients: diagnostic efficacy and interpretation pitfalls. Radiographics 26:1811–1824 discussion 1824-1816

    Article  Google Scholar 

  25. 25.

    Kumar R, Xiu Y, Yu JQ et al (2004) 18F-FDG PET in evaluation of adrenal lesions in patients with lung cancer. J Nucl Med 45:2058–2062

    PubMed  Google Scholar 

  26. 26.

    Blake MA, Singh A, Setty BN et al (2006) Pearls and pitfalls in interpretation of abdominal and pelvic PET-CT. Radiographics 26:1335–1353

    Article  Google Scholar 

  27. 27.

    Delong ER, Delong DM, Clarkepearson DI (1988) Comparing the areas under 2 or more correlated receiver operating characteristic curves - a nonparametric approach. Biometrics 44:837–845

    CAS  Article  Google Scholar 

  28. 28.

    Wang W, Davis CS, Soong SJ (2006) Comparison of predictive values of two diagnostic tests from the same sample of subjects using weighted least squares. Stat Med 25:2215–2229

    Article  Google Scholar 

  29. 29.

    Leisenring W, Alonzo T, Pepe MS (2000) Comparisons of predictive values of binary medical diagnostic tests for paired designs. Biometrics 56:345–351

    CAS  Article  Google Scholar 

  30. 30.

    Jakobsson U, Westergren A (2005) Statistical methods for assessing agreement for ordinal data. Scand J Caring Sci 19:427–431

    Article  Google Scholar 

  31. 31.

    Mukaka MM (2012) Statistics corner: a guide to appropriate use of correlation coefficient in medical research. Malawi Med J 24:69–71

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Cleveland WS, Devlin SJ (1988) Locally weighted regression - an approach to regression-analysis by local fitting. J Am Stat Assoc 83:596–610

    Article  Google Scholar 

  33. 33.

    Boland GW (2011) Adrenal imaging: why, when, what, and how? Part 2. What technique? AJR Am J Roentgenol 196:W1–W5

    Article  Google Scholar 

  34. 34.

    Szolar DH, Kammerhuber FH (1998) Adrenal adenomas and nonadenomas: assessment of washout at delayed contrast-enhanced CT. Radiology 207:369–375

    CAS  Article  Google Scholar 

  35. 35.

    Garcia-Garrigos E, Arenas-Jimenez JJ, Sanchez-Paya J (2018) Best protocol for combined contrast-enhanced thoracic and abdominal CT for lung cancer: a single-institution randomized crossover clinical trial. AJR Am J Roentgenol 210:1226–1234

    Article  Google Scholar 

  36. 36.

    Connolly MJ, McInnes MDF, El-Khodary M, McGrath TA, Schieda N (2017) Diagnostic accuracy of virtual non-contrast enhanced dual-energy CT for diagnosis of adrenal adenoma: a systematic review and meta-analysis. Eur Radiol 27:4324–4335

    Article  Google Scholar 

  37. 37.

    Han WK, Na JC, Park SY (2020) Low-dose CT angiography using ASiR-V for potential living renal donors: a prospective analysis of image quality and diagnostic accuracy. Eur Radiol 30:798–805

    Article  Google Scholar 

  38. 38.

    Greffier J, Hamard A, Pereira F et al (2020) Image quality and dose reduction opportunity of deep learning image reconstruction algorithm for CT: a phantom study. Eur Radiol 30:3951–3959

    Article  Google Scholar 

  39. 39.

    Blake MA, Cronin CG, Boland GW (2010) Adrenal imaging. AJR Am J Roentgenol 194:1450–1460

    Article  Google Scholar 

  40. 40.

    Adams MC, Turkington TG, Wilson JM, Wong TZ (2010) A systematic review of the factors affecting accuracy of SUV measurements. AJR Am J Roentgenol 195:310–320

    Article  Google Scholar 

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Acknowledgments

This study was supported by Samsung Medical Center Grant (SMO1190081).

We would like to thank Editage (www.editage.co.kr) for English language editing.

Funding

This study was supported by Samsung Medical Center Grant (SMO1190081).

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Correspondence to Sung Yoon Park.

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The scientific guarantor of this publication is Sung Yoon Park, M.D.

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The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

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No complex statistical methods were necessary for this paper.

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Written informed consent was waived by the Institutional Review Board.

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• retrospective

• cross sectional study

• performed at one institution

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Lee, H.Y., Oh, Y.L. & Park, S.Y. Hyperattenuating adrenal lesions in lung cancer: biphasic CT with unenhanced and 1-min enhanced images reliably predicts benign lesions. Eur Radiol (2021). https://doi.org/10.1007/s00330-020-07648-1

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Keywords

  • Adrenal
  • Lung cancer
  • Tomography
  • Metastasis