Differentiating kidney stones from phleboliths in unenhanced low-dose computed tomography using radiomics and machine learning

  • Thomas De PerrotEmail author
  • Jeremy Hofmeister
  • Simon Burgermeister
  • Steve P. Martin
  • Gregoire Feutry
  • Jacques Klein
  • Xavier Montet
Imaging Informatics and Artificial Intelligence



Distinguishing between kidney stones and phleboliths can constitute a diagnostic challenge in patients undergoing unenhanced low-dose CT (LDCT) for acute flank pain. We sought to investigate the accuracy of radiomics and a machine-learning classifier in differentiating between kidney stones and phleboliths on LDCT.


Radiomics features were extracted following a semi-automatic segmentation of kidney stones and phleboliths for two independent consecutive cohorts of patients undergoing LDCT for acute flank pain.

Radiomics features from the first cohort of patients (n = 369) were ultimately used to train a machine-learning model designed to distinguish kidney stones (n = 211) from phleboliths (n = 201). Classification performance was assessed on the second independent cohort (i.e., testing set) (kidney stones n = 24; phleboliths n = 23) using positive and negative predictive values (PPV and NPV), area under the receiver operating curves (AUC), and permutation testing.


Our machine-learning classification model trained on radiomics features achieved an overall accuracy of 85.1% on the independent testing set, with an AUC of 0.902, PPV of 81.5%, and NPV of 90.0%. Classification accuracy was significantly better than chance on permutation testing (p < 0.05, permutation p value).


Radiomics and machine learning enable accurate differentiation between kidney stones and phleboliths on LDCT in patients presenting with acute flank pain.

Key Points

Combining a machine-learning algorithm with radiomics features extracted for abdominopelvic calcification on LDCT offers a highly accurate method for discriminating phleboliths from kidney stones.

Our radiomics and machine-learning model proved robust for CT acquisition and reconstruction protocol when tested in comparison with an external independent cohort of patients with acute flank pain.

The high performance of the radiomics-based automatic classification model in differentiating phleboliths from kidney stones indicates its potential as a future diagnostic tool for equivocal abdominopelvic calcifications in the setting of suspected renal colic.


Urinary tract Lithiasis Machine learning Artificial intelligence 



Area under the curve




Low-dose computed tomography


Principal component analysis


Receiver operating characteristics curve


Volume of interest


Compliance with ethical standards


The scientific guarantor of this publication is Prof. Xavier Montet.

Conflict of interest

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.

Statistics and biometry

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.


• retrospective

• experimental

• performed at one institution


  1. 1.
    Ziemba JB, Matlaga BR (2017) Epidemiology and economics of nephrolithiasis. Investig Clin Urol 58:299–306CrossRefGoogle Scholar
  2. 2.
    Poletti PA, Platon A, Rutschmann OT, Schmidlin FR, Iselin CE, Becker CD (2007) Low-dose versus standard-dose CT protocol in patients with clinically suspected renal colic. AJR Am J Roentgenol 188:927–933CrossRefGoogle Scholar
  3. 3.
    Luk AC, Cleaveland P, Olson L, Neilson D, Srirangam SJ (2017) Pelvic phlebolith: a trivial pursuit for the urologist? J Endourol 31:342–347CrossRefGoogle Scholar
  4. 4.
    Traubici J, Neitlich JD, Smith RC (1999) Distinguishing pelvic phleboliths from distal ureteral stones on routine unenhanced helical CT: is there a radiolucent center? AJR Am J Roentgenol 172:13–17CrossRefGoogle Scholar
  5. 5.
    Humphry GM (1896) Urinary calculi: their formation and structure. J Anat Physiol 30:296–311PubMedPubMedCentralGoogle Scholar
  6. 6.
    Williams JC Jr, McAteer JA, Evan AP, Lingeman JE (2010) Micro-computed tomography for analysis of urinary calculi. Urol Res 38:477–484CrossRefGoogle Scholar
  7. 7.
    Prien EL, Prien EL Jr (1968) Composition and structure of urinary stone. Am J Med 45:654–672CrossRefGoogle Scholar
  8. 8.
    Summers RM (2016) Texture analysis in radiology: does the emperor have no clothes? Abdom Radiol (NY).
  9. 9.
    Parekh V, Jacobs MA (2016) Radiomics: a new application from established techniques. Expert Rev Precis Med Drug Dev 1:207–226CrossRefGoogle Scholar
  10. 10.
    Larue RT, Defraene G, De Ruysscher D, Lambin P, van Elmpt W (2017) Quantitative radiomics studies for tissue characterization: a review of technology and methodological procedures. Br J Radiol 90:20160665CrossRefGoogle Scholar
  11. 11.
    Aerts HJ, Velazquez ER, Leijenaar RT et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006CrossRefGoogle Scholar
  12. 12.
    Haralick RM, Shanmugam K, Dinstein I (1973) Textural features for image classification. IEEE Trans Syst Man Cybern SMC-3:610–621Google Scholar
  13. 13.
    van Griethuysen JJM, Fedorov A, Parmar C et al (2017) Computational radiomics system to decode the radiographic phenotype. Cancer Res 77:e104–e107CrossRefGoogle Scholar
  14. 14.
    Cawley GC, Talbot NLC (2010) On over-fitting in model selection and subsequent selection bias in performance evaluation. J Mach Learn Res 11:2079–2107Google Scholar
  15. 15.
    Pedregosa F, Varoquaux G, Gramfort A et al (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825–2830Google Scholar
  16. 16.
    Kim JC (2001) Central lucency of pelvic phleboliths: comparison of radiographs and noncontrast helical CT. Clin Imaging 25:122–125CrossRefGoogle Scholar
  17. 17.
    Williams JC Jr, Lingeman JE, Coe FL, Worcester EM, Evan AP (2015) Micro-CT imaging of Randall’s plaques. Urolithiasis 43(Suppl 1):13–17CrossRefGoogle Scholar
  18. 18.
    Zarse CA, McAteer JA, Tann M et al (2004) Helical computed tomography accurately reports urinary stone composition using attenuation values: in vitro verification using high-resolution micro-computed tomography calibrated to fourier transform infrared microspectroscopy. Urology 63:828–833CrossRefGoogle Scholar
  19. 19.
    Boridy IC, Nikolaidis P, Kawashima A, Goldman SM, Sandler CM (1999) Ureterolithiasis: value of the tail sign in differentiating phleboliths from ureteral calculi at nonenhanced helical CT. Radiology 211:619–621CrossRefGoogle Scholar
  20. 20.
    Heneghan JP, Dalrymple NC, Verga M, Rosenfield AT, Smith RC (1997) Soft-tissue “rim” sign in the diagnosis of ureteral calculi with use of unenhanced helical CT. Radiology 202:709–711CrossRefGoogle Scholar
  21. 21.
    Beig N, Patel J, Prasanna P et al (2018) Radiogenomic analysis of hypoxia pathway is predictive of overall survival in glioblastoma. Sci Rep 8(7)Google Scholar
  22. 22.
    Thawani R, McLane M, Beig N et al (2018) Radiomics and radiogenomics in lung cancer: a review for the clinician. Lung Cancer 115:34–41CrossRefGoogle Scholar
  23. 23.
    Zhao B, Tan Y, Tsai WY et al (2016) Reproducibility of radiomics for deciphering tumor phenotype with imaging. Sci Rep 6:23428CrossRefGoogle Scholar
  24. 24.
    Parmar C, Rios Velazquez E, Leijenaar R et al (2014) Robust Radiomics feature quantification using semiautomatic volumetric segmentation. PLoS One 9:e102107CrossRefGoogle Scholar
  25. 25.
    Incoronato M, Aiello M, Infante T et al (2017) Radiogenomic analysis of oncological data: a technical survey. Int J Mol Sci 18Google Scholar

Copyright information

© European Society of Radiology 2019

Authors and Affiliations

  1. 1.Division of Radiology, Diagnostic DepartmentGeneva University HospitalsGenevaSwitzerland
  2. 2.Division of Urology, Department of SurgeryGeneva University HospitalsGenevaSwitzerland

Personalised recommendations