Skip to main content
SpringerLink
Log in

Automated radiomic analysis of CT images to predict likelihood of spontaneous passage of symptomatic renal stones

  • Original Article
  • Published:
Emergency Radiology Aims and scope Submit manuscript

Abstract

Purpose

To evaluate the ability of a semi-automated radiomic analysis software in predicting the likelihood of spontaneous passage of urinary stones compared with manual measurements.

Methods

Symptomatic patients visiting the emergency department with suspected stones in either kidney or ureters who underwent a CT scan were included. Patients were followed for up to 6 months for the outcome of a trial of passage. Maximum stone diameters in axial and coronal images were measured manually. Stone length, width, height, max diameter, volume, the mean and standard deviation of the Hounsfield units, and morphologic features were also measured using automated radiomic analysis software. Multivariate models were developed using these data to predict subsequent spontaneous stone passage, with results expressed as the area under a receiver operating curve (AUC).

Results

One hundred eighty-four patients (69 females) with a median age of 56 years were included. Spontaneous stone passage occurred in 114 patients (62%). Univariate analysis demonstrated an AUC of 0.83 and 0.82 for the maximum stone diameter determined manually in the axial and coronal planes, respectively. Multivariate models demonstrated an AUC of 0.82 for a model including manual measurement of maximum stone diameter in axial and coronal planes. The same AUC was found for a model including automatic measurement of maximum height and diameter of the stone. Further addition of morphological parameters measured automatically did not increase AUC beyond 0.83.

Conclusion

The semi-automated radiomic analysis of urinary stones shows similar accuracy compared with manual measurements for predicting urinary stone passage. Further studies are needed to predict clinical impacts of reporting the likelihood of urinary stone passage and improving inter-observer variation using automatic radiomic analysis software.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Data Availability

Available upon request from the corresponding author.

References

  1. Bultitude M, Rees J (2012) Management of renal colic. BMJ 345:e5499

    Article  Google Scholar 

  2. Smith RC, Verga M, McCarthy S, Rosenfield AT (1996) Diagnosis of acute flank pain: value of unenhanced helical CT. AJR Am J Roentgenol 166(1):97–101

    Article  CAS  Google Scholar 

  3. Turk C, Petrik A, Sarica K, Seitz C, Skolarikos A, Straub M, Knoll T (2016) EAU guidelines on diagnosis and conservative management of urolithiasis. Eur Urol 69(3):468–474

    Article  Google Scholar 

  4. Niemann T, Kollmann T, Bongartz G (2008) Diagnostic performance of low-dose CT for the detection of urolithiasis: a meta-analysis. AJR Am J Roentgenol 191(2):396–401

    Article  Google Scholar 

  5. Tamm EP, Silverman PM, Shuman WP (2003) Evaluation of the patient with flank pain and possible ureteral calculus. Radiology 228(2):319–329

    Article  Google Scholar 

  6. Ray AA, Ghiculete D, Pace KT, Honey RJ (2010) Limitations to ultrasound in the detection and measurement of urinary tract calculi. Urology 76(2):295–300

    Article  Google Scholar 

  7. Ganesan V, De S, Greene D, Torricelli FC, Monga M (2017) Accuracy of ultrasonography for renal stone detection and size determination: is it good enough for management decisions? BJU Int 119(3):464–469

    Article  Google Scholar 

  8. Coll DM, Varanelli MJ, Smith RC (2002) Relationship of spontaneous passage of ureteral calculi to stone size and location as revealed by unenhanced helical CT. AJR Am J Roentgenol 178(1):101–103

    Article  Google Scholar 

  9. Association AU Kidney Stones https://www.auanet.org/education/kidney-stones.cfm. Accessed 02/23/2016

  10. Kampa RJ, Ghani KR, Wahed S, Patel U, Anson KM (2005) Size matters: a survey of how urinary-tract stones are measured in the UK. J Endourol 19(7):856–860

    Article  Google Scholar 

  11. Nazim SM, Ather MH, Khan N (2014) Measurement of ureteric stone diameter in different planes on multidetector computed tomography--impact on the clinical decision making. Urology 83(2):288–292

    Article  Google Scholar 

  12. Demehri S, Steigner ML, Sodickson AD, Houseman EA, Rybicki FJ, Silverman SG (2012) CT-based determination of maximum ureteral stone area: a predictor of spontaneous passage. AJR Am J Roentgenol 198(3):603–608

    Article  Google Scholar 

  13. Duan X, Wang J, Qu M, Leng S, Liu Y, Krambeck A, McCollough C (2012) Kidney stone volume estimation from computerized tomography images using a model based method of correcting for the point spread function. J Urol 188(3):989–995

    Article  Google Scholar 

  14. Manzoor MAP, Agrawal AK, Singh B, Mujeeburahiman M, Rekha PD (2019) Morphological characteristics and microstructure of kidney stones using synchrotron radiation muCT reveal the mechanism of crystal growth and aggregation in mixed stones. PLoS One 14(3):e0214003

    Article  CAS  Google Scholar 

  15. Gucuk A, Uyeturk U (2014) Usefulness of hounsfield unit and density in the assessment and treatment of urinary stones. World J Nephrol 3(4):282–286

    Article  Google Scholar 

  16. Jendeberg J, Geijer H, Alshamari M, Cierzniak B, Liden M (2017) Size matters: the width and location of a ureteral stone accurately predict the chance of spontaneous passage. Eur Radiol 27(11):4775–4785

    Article  Google Scholar 

  17. Sfoungaristos S, Kavouras A, Perimenis P (2012) Predictors for spontaneous stone passage in patients with renal colic secondary to ureteral calculi. Int Urol Nephrol 44(1):71–79

    Article  Google Scholar 

  18. Choi T, Yoo KH, Choi SK, Kim DS, Lee DG, Min GE, Jeon SH, Lee HL, Jeong IK (2015) Analysis of factors affecting spontaneous expulsion of ureteral stones that may predict unfavorable outcomes during watchful waiting periods: what is the influence of diabetes mellitus on the ureter? Korean J Urol 56(6):455–460

    Article  Google Scholar 

  19. Jendeberg J, Geijer H, Alshamari M, Liden M (2018) Prediction of spontaneous ureteral stone passage: automated 3D-measurements perform equal to radiologists, and linear measurements equal to volumetric. Eur Radiol 28(6):2474–2483

    Article  Google Scholar 

  20. Patel SR, Wells S, Ruma J, King S, Lubner MG, Nakada SY, Pickhardt PJ (2012) Automated volumetric assessment by noncontrast computed tomography in the surveillance of nephrolithiasis. Urology 80(1):27–31

    Article  Google Scholar 

Download references

Code availability

Not available.

Funding

Research reported in this work was supported by the National Institutes of Health under award number U54 DK100227 and R01 EB028591. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Health.

Author information

Authors and Affiliations

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

    Payam Mohammadinejad, Andrea Ferrero, David J. Bartlett, Ashish Khandelwal, Roy Marcus, Taylor R. Moen, Cynthia H. McCollough & Joel G. Fletcher

  2. Department of Laboratory Medicine and Pathology, Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN, 55905, USA

    John C. Lieske

  3. Department of Health Sciences Research, Mayo Clinic, Rochester, MN, 55905, USA

    Kristin C. Mara & Felicity T. Enders

Authors
  1. Payam Mohammadinejad
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andrea Ferrero
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. David J. Bartlett
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ashish Khandelwal
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Roy Marcus
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. John C. Lieske
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Taylor R. Moen
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Kristin C. Mara
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Felicity T. Enders
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Cynthia H. McCollough
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Joel G. Fletcher
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Andrea Ferrero.

Ethics declarations

Conflict of interest

JF and CM receive research funding from Siemens Healthcare GmbH. The other authors declare that they have no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mohammadinejad, P., Ferrero, A., Bartlett, D.J. et al. Automated radiomic analysis of CT images to predict likelihood of spontaneous passage of symptomatic renal stones. Emerg Radiol 28, 781–788 (2021). https://doi.org/10.1007/s10140-021-01915-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10140-021-01915-4

Keywords

Search

Navigation