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

  • Imaging Informatics and Artificial Intelligence
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

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.

Methods

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.

Results

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).

Conclusion

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.

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Abbreviations

AUC:

Area under the curve

CV:

Cross-validation

LDCT:

Low-dose computed tomography

PCA:

Principal component analysis

ROC:

Receiver operating characteristics curve

VOI:

Volume of interest

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Authors and Affiliations

  1. Division of Radiology, Diagnostic Department, Geneva University Hospitals, Geneva, Switzerland

    Thomas De Perrot, Jeremy Hofmeister, Simon Burgermeister, Steve P. Martin, Gregoire Feutry & Xavier Montet

  2. Division of Urology, Department of Surgery, Geneva University Hospitals, Geneva, Switzerland

    Jacques Klein

Authors
  1. Thomas De Perrot
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  2. Jeremy Hofmeister
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  3. Simon Burgermeister
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  4. Steve P. Martin
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  5. Gregoire Feutry
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  6. Jacques Klein
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  7. Xavier Montet
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Correspondence to Thomas De Perrot.

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

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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.

Statistics and biometry

One of the authors has significant statistical expertise.

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Written informed consent was waived by the Institutional Review Board.

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• retrospective

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• performed at one institution

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De Perrot, T., Hofmeister, J., Burgermeister, S. et al. Differentiating kidney stones from phleboliths in unenhanced low-dose computed tomography using radiomics and machine learning. Eur Radiol 29, 4776–4782 (2019). https://doi.org/10.1007/s00330-019-6004-7

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  • DOI: https://doi.org/10.1007/s00330-019-6004-7

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