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Diagnostic performance of dual-energy CT and subtraction CT for renal lesion detection and characterization

  • Ali Pourvaziri
  • Anushri Parakh
  • Amirkasra Mojtahed
  • Avinash Kambadakone
  • Dushyant Vasudeo SahaniEmail author
Computed Tomography
  • 90 Downloads

Abstract

Purpose

To compare the effect of dual-energy CT (DECT) material density datasets on diagnostic performance, readers’ confidence, and interpretation time for renal lesion detection and characterization in comparison to subtraction CT (SCT).

Material and methods

One hundred fourteen patients (69/45 = M/F, mean age = 67 years) who underwent contrast-enhanced DECT between January 2015 and February 2018 for suspected renal mass were included retrospectively. For each patient, three radiologists assessed three image datasets: group A, material density iodine (MDI) + material density water (MDW); group B, SCT only; and group C, SCT + true unenhanced phase + virtual monochromatic images at 65 keV. Readers evaluated image quality (4-point scale), the number of lesions, and likely diagnosis. Reading times were recorded. Quantitatively, iodine concentration (IC from MDI) and delta Hounsfield units (ΔHU) for all lesions were measured. Diagnostic accuracy was compared using the area under the receiver operating characteristic curve (AUC). Image quality and interpretation time were compared with Kruskal-Wallis and t tests.

Results

Study cohort (230 lesions; mean size = 23.63 mm (5–116 mm)) consisted of 60 enhancing, 158 non-enhancing, and 12 lipid-dominant angiomyolipoma lesions. Significantly higher image quality was demonstrated for MDI compared to SCT (mean score = 3.82 vs. 3; p < 0.05). Comparable diagnostic accuracy was observed for group A (AUC = 0.88) and group C (AUC = 0.87) and was higher compared to that for group B (AUC = 0.75). Group A was read faster than group C (41.49 s vs. 71.45 s per exam; p < 0.05). Both IC and ΔHU values had high accuracy (AUC = 0.97) for differentiating enhancing vs. non-enhancing lesions; however, IC enabled differentiation of clear cell renal cell carcinoma from other enhancing lesions with moderate accuracy (AUC = 0.73).

Conclusion

MDI images increase readers’ confidence for renal lesion detection and characterization while providing a more efficient radiologist workflow, irrespective of readers’ experience.

Key Points

• Material density iodine (MDI) images enable faster interpretation due to high image quality and potentially reduced need for quantitation.

• MDI images increase diagnostic confidence of readers, irrespective of radiologists’ experience.

• High accuracy with dual-energy CT (DECT) can potentially reduce healthcare costs by eliminating the need for additional investigations.

Keywords

Kidney X-ray computed tomography Workflow Neoplasms Radiologist 

Abbreviations

AUC

Area under the receiver operating characteristic curve

cRCC

Clear cell renal cell carcinoma

DECT

Dual-energy CT

dsDECT

Dual-source dual-energy CT

IC

Iodine concentration

IQ

Image quality

MDI

Material density iodine

MDW

Material density water

NIC

Normalized iodine concentration

R-CT

Multiphasic renal mass protocol with conventional CT

ROC

Receiver operating characteristic

ROI

Region of interest

rsDECT

Rapid kV-switching dual-energy CT

SCT

Subtraction CT

TS

Tuberous sclerosis

TUE

True unenhanced

PACS

Picture archiving and communication system

RCC

Renal cell carcinoma

SECT

Stimulated single-energy CT

Notes

Acknowledgements

This work was conducted with support from the Harvard Catalyst | The Harvard Clinical and Translational Science Center (National Center for Advancing Translational Sciences, National Institutes of Health Award UL 1TR002541) and financial contributions from Harvard University and its affiliated academic healthcare centers. The content is solely the responsibility of the authors and does not necessarily represent the official views of Harvard Catalyst, Harvard University, and its affiliated academic healthcare centers, or the National Institutes of Health.

Funding

The authors state that this work has not received any funding.

Compliance with ethical standards

Guarantor

The scientific guarantor of this publication is Dushyant Vasudeo Sahani.

Conflict of interest

Dushyant Vasudeo Sahani, MD has the following disclosures which are not relevant to this work: research grant support from GE, Philips, and Bayer Healthcare; royalties (Elsevier); and being a consultant of Allena Pharmaceuticals, GE Healthcare. Other authors have no relevant disclosure. The data was handled by an investigator with no conflict of interest.

Statistics and biometry

Hang Lee, Ph.D. kindly provided the statistical advice for this manuscript. One of the authors has significant statistical expertise.

Informed consent

Written informed consent was waived by the institutional review board.

Ethical approval

Institutional review board approval was obtained.

Methodology

• Retrospective

• diagnostic study

• performed in one institution

References

  1. 1.
    Al Harbi F, Tabatabaeefar L, Jewett MA, Finelli A, O’Malley M, Atri M (2016) Enhancement threshold of small (< 4 cm) solid renal masses on CT. AJR Am J Roentgenol 206:554–558CrossRefGoogle Scholar
  2. 2.
    Apfaltrer P, Meyer M, Meier C et al (2012) Contrast-enhanced dual-energy CT of gastrointestinal stromal tumors: is iodine-related attenuation a potential indicator of tumor response? Invest Radiol 47:65CrossRefGoogle Scholar
  3. 3.
    Ascenti G, Mileto A, Krauss B et al (2013) Distinguishing enhancing from nonenhancing renal masses with dual-source dual-energy CT: iodine quantification versus standard enhancement measurements. Eur Radiol 23:2288–2295CrossRefGoogle Scholar
  4. 4.
    Baerends E, Oostveen LJ, Smit CT et al (2018) Comparing dual energy CT and subtraction CT on a phantom: which one provides the best contrast in iodine maps for sub-centimetre details? Eur Radiol 28:5051–5059Google Scholar
  5. 5.
    Birnbaum BA, Maki DD, Chakraborty DP, Jacobs JE, Babb JS (2002) Renal cyst pseudoenhancement: evaluation with an anthropomorphic body CT phantom. Radiology 225:83–90CrossRefGoogle Scholar
  6. 6.
    Birnbaum BA, Hindman N, Lee J, Babb JS (2007) Multi–detector row CT attenuation measurements: assessment of intra- and interscanner variability with an anthropomorphic body CT phantom. Radiology 242:109–119CrossRefGoogle Scholar
  7. 7.
    Borhani AA, Kulzer M, Iranpour N et al (2017) Comparison of true unenhanced and virtual unenhanced (VUE) attenuation values in abdominopelvic single-source rapid kilovoltage-switching spectral CT. Abdom Radiol (NY) 42:710–717CrossRefGoogle Scholar
  8. 8.
    Cha D, Kim CK, Park JJ, Park BK (2016) Evaluation of hyperdense renal lesions incidentally detected on single-phase post-contrast CT using dual-energy CT. Br J Radiol 89:20150860CrossRefGoogle Scholar
  9. 9.
    Chandarana H, Megibow AJ, Cohen BA et al (2011) Iodine quantification with dual-energy CT: phantom study and preliminary experience with renal masses. AJR Am J Roentgenol 196:W693–W700CrossRefGoogle Scholar
  10. 10.
    Chandler A, Wei W, Herron DH, Anderson EF, Johnson VE, Ng CS (2011) Semiautomated motion correction of tumors in lung CT-perfusion studies. Acad Radiol 18:286–293CrossRefGoogle Scholar
  11. 11.
    Chandler A, Wei W, Anderson EF, Herron DH, Ye Z, Ng CS (2012) Validation of motion correction techniques for liver CT perfusion studies. Br J Radiol 85:e514–e522CrossRefGoogle Scholar
  12. 12.
    Dillman JR, Caoili EM, Cohan RH, Ellis JH, Francis IR, Schipper MJ (2008) Detection of upper tract urothelial neoplasms: sensitivity of axial, coronal reformatted, and curved-planar reformatted image-types utilizing 16-row multi-detector CT urography. Abdom Imaging 33:707–716CrossRefGoogle Scholar
  13. 13.
    Ding A, Eisenberg JD, Pandharipande PV (2011) The economic burden of incidentally detected findings. Radiol Clin North Am 49:257–265CrossRefGoogle Scholar
  14. 14.
    Eisner B, Kambadakone A, Samir A, Sahani D (2010) 619 subtraction ct improves radiologist confidence in evaluation of enhancing renal lesions. J Urol 183:e243–e244Google Scholar
  15. 15.
    Feuerlein S, Heye TJ, Bashir MR, Boll DT (2012) Iodine quantification using dual-energy multidetector computed tomography imaging: phantom study assessing the impact of iterative reconstruction schemes and patient habitus on accuracy. Invest Radiol 47:656CrossRefGoogle Scholar
  16. 16.
    Goodsitt MM, Christodoulou EG, Larson SC (2011) Accuracies of the synthesized monochromatic CT numbers and effective atomic numbers obtained with a rapid kVp switching dual energy CT scanner. Med Phys 38:2222–2232CrossRefGoogle Scholar
  17. 17.
    Graser A, Johnson TR, Hecht EM et al (2009) Dual-energy CT in patients suspected of having renal masses: can virtual nonenhanced images replace true nonenhanced images? Radiology 252:433–440CrossRefGoogle Scholar
  18. 18.
    Graser A, Becker CR, Staehler M et al (2010) Single-phase dual-energy CT allows for characterization of renal masses as benign or malignant. Invest Radiol 45:399Google Scholar
  19. 19.
    Herts BR, Silverman SG, Hindman NM et al (2018) Management of the incidental renal mass on CT: a white paper of the ACR Incidental Findings Committee. J Am Coll Radiol 15:264–273CrossRefGoogle Scholar
  20. 20.
    Hock LM, Lynch J, Balaji KC (2002) Increasing incidence of all stages of kidney cancer in the last 2 decades in the United States: an analysis of surveillance, epidemiology and end results program data. J Urol 167:57–60CrossRefGoogle Scholar
  21. 21.
    Hollingsworth JM, Miller DC, Daignault S, Hollenbeck BK (2006) Rising incidence of small renal masses: a need to reassess treatment effect. J Natl Cancer Inst 98:1331–1334CrossRefGoogle Scholar
  22. 22.
    Jamis-Dow CA, Choyke PL, Jennings SB, Linehan WM, Thakore KN, Walther MM (1996) Small (< or = 3-cm) renal masses: detection with CT versus US and pathologic correlation. Radiology 198:785–788CrossRefGoogle Scholar
  23. 23.
    Jayson M, Sanders H (1998) Increased incidence of serendipitously discovered renal cell carcinoma. Urology 51:203–205CrossRefGoogle Scholar
  24. 24.
    Jinzaki M, Tanimoto A, Mukai M et al (2000) Double-phase helical CT of small renal parenchymal neoplasms: correlation with pathologic findings and tumor angiogenesis. J Comput Assist Tomogr 24:835CrossRefGoogle Scholar
  25. 25.
    Kambadakone A, Arasu VA, Samir AE et al (2012) Qualitative assessment of enhancement in a renal mass: contribution of subtraction CT. J Comput Assist Tomogr 36:381CrossRefGoogle Scholar
  26. 26.
    Kaza RK, Caoili EM, Cohan RH, Platt JF (2011) Distinguishing enhancing from nonenhancing renal lesions with fast kilovoltage-switching dual-energy CT. AJR Am J Roentgenol 197:1375–1381CrossRefGoogle Scholar
  27. 27.
    Koonce JD, Vliegenthart R, Schoepf UJ et al (2014) Accuracy of dual-energy computed tomography for the measurement of iodine concentration using cardiac CT protocols: validation in a phantom model. Eur Radiol 24:512–518CrossRefGoogle Scholar
  28. 28.
    Mahmood U, Horvat N, Horvat JV et al (2018) Rapid switching kVp dual energy CT: value of reconstructed dual energy CT images and organ dose assessment in multiphasic liver CT exams. Eur J Radiol 102:102–108CrossRefGoogle Scholar
  29. 29.
    Manoharan D, Sharma S, Das CJ, Kumar R, Singh G, Kumar P (2018) Single-acquisition triple-bolus dual-energy CT protocol for comprehensive evaluation of renal masses: a single-center randomized noninferiority trial. AJR Am J Roentgenol 211:W1–W11CrossRefGoogle Scholar
  30. 30.
    Marin D, Davis D, Roy Choudhury K et al (2017) Characterization of small focal renal lesions: diagnostic accuracy with single-phase contrast-enhanced dual-energy CT with material attenuation analysis compared with conventional attenuation measurements. Radiology 284:737–747CrossRefGoogle Scholar
  31. 31.
    Matsumoto K, Jinzaki M, Tanami Y, Ueno A, Yamada M, Kuribayashi S (2011) Virtual monochromatic spectral imaging with fast kilovoltage switching: improved image quality as compared with that obtained with conventional 120-kVp CT. Radiology 259:257–262CrossRefGoogle Scholar
  32. 32.
    McCarthy CJ, Baliyan V, Kordbacheh H, Sajjad Z, Sahani D, Kambadakone A (2016) Radiology of renal stone disease. Int J Surg 36:638–646CrossRefGoogle Scholar
  33. 33.
    Mileto A, Nelson RC, Samei E et al (2014) Impact of dual-energy multi–detector row CT with virtual monochromatic imaging on renal cyst pseudoenhancement: in vitro and in vivo study. Radiology 272:767–776CrossRefGoogle Scholar
  34. 34.
    Mileto A, Marin D, Ramirez-Giraldo et al (2014b) Accuracy of contrast-enhanced dual-energy MDCT for the assessment of iodine uptake in renal lesions. AJR Am J Roentgenol 202:W466–W474CrossRefGoogle Scholar
  35. 35.
    Mohr B, Brink M, Oostveen LJ, Schuijf JD, Prokop M (2016) Lung iodine mapping by subtraction with image registration allowing for tissue sliding. Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 978442Google Scholar
  36. 36.
    Patel BN, Bibbey A, Choudhury KR, Leder RA, Nelson RC, Marin D (2017) Characterization of small (< 4 cm) focal renal lesions: diagnostic accuracy of spectral analysis using single-phase contrast-enhanced dual-energy CT. AJR Am J Roentgenol 209:815–825CrossRefGoogle Scholar
  37. 37.
    Pelgrim GJ, van Hamersvelt RW, Willemink MJ et al (2017) Accuracy of iodine quantification using dual energy CT in latest generation dual source and dual layer CT. Eur Radiol 27:3904–3912CrossRefGoogle Scholar
  38. 38.
    Porter ME (2010) What is value in health care? N Engl J Med 363:2477–2481CrossRefGoogle Scholar
  39. 39.
    Savci G, Yazici Z, Sahin N, Akgöz S, Tuncel E (2006) Value of chemical shift subtraction MRI in characterization of adrenal masses. AJR Am J Roentgenol 186:130–135CrossRefGoogle Scholar
  40. 40.
    Siegel CL, Fisher AJ, Bennett HF (1999) Interobserver variability in determining enhancement of renal masses on helical CT. AJR Am J Roentgenol 172:1207–1212CrossRefGoogle Scholar
  41. 41.
    Tada S, Yamagishi J, Kobayashi H, Hata Y, Kobari T (1983) The incidence of simple renal cyst by computed tomography. Clin Radiol 34:437–439CrossRefGoogle Scholar
  42. 42.
    Vasudevan A, Davies RJ, Shannon BA, Cohen RJ (2006) Incidental renal tumours: the frequency of benign lesions and the role of preoperative core biopsy. BJU Int 97:946–949CrossRefGoogle Scholar
  43. 43.
    Yoshioka K, Tanaka R, Takagi H et al (2016) Diagnostic accuracy of a modified subtraction coronary CT angiography method with short breath-holding time: a feasibility study. Br J Radiol 89:20160489CrossRefGoogle Scholar
  44. 44.
    Yu L, Christner JA, Leng S, Wang J, Fletcher JG, McCollough CH (2011) Virtual monochromatic imaging in dual-source dual-energy CT: radiation dose and image quality. Med Phys 38:6371–6379CrossRefGoogle Scholar
  45. 45.
    Zarzour JG, Milner D, Valentin R et al (2017) Quantitative iodine content threshold for discrimination of renal cell carcinomas using rapid kV-switching dual-energy CT. Abdom Radiol (NY) 42:727–734CrossRefGoogle Scholar

Copyright information

© European Society of Radiology 2019
corrected publication 2019

Authors and Affiliations

  • Ali Pourvaziri
    • 1
  • Anushri Parakh
    • 1
  • Amirkasra Mojtahed
    • 1
  • Avinash Kambadakone
    • 1
  • Dushyant Vasudeo Sahani
    • 2
    Email author
  1. 1.Department of Radiology, Division of Abdominal Imaging and Intervention, Massachusetts General HospitalHarvard Medical SchoolBostonUSA
  2. 2.Department of RadiologyUniversity of WashingtonSeattleUSA

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