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Dose independent characterization of renal stones by means of dual energy computed tomography and machine learning: an ex-vivo study

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Abstract

Objectives

To predict the main component of pure and mixed kidney stones using dual-energy computed tomography and machine learning.

Methods

200 kidney stones with a known composition as determined by infrared spectroscopy were examined using a non-anthropomorphic phantom on a spectral detector computed tomography scanner. Stones were of either pure (monocrystalline, n = 116) or compound (dicrystalline, n = 84) composition. Image acquisition was repeated twice using both, normal and low-dose protocols, respectively (ND/LD). Conventional images and low and high keV virtual monoenergetic images were reconstructed. Stones were semi-automatically segmented. A shallow neural network was trained using data from ND1 acquisition split into training (70%), testing (15%) and validation-datasets (15%). Performance for ND2 and both LD acquisitions was tested. Accuracy on a per-voxel and a per-stone basis was calculated.

Results

Main components were: Whewellite (n = 80), weddellite (n = 21), Ca-phosphate (n = 39), cysteine (n = 20), struvite (n = 13), uric acid (n = 18) and xanthine stones (n = 9). Stone size ranged from 3 to 18 mm. Overall accuracy for predicting the main component on a per-voxel basis attained by ND testing dataset was 91.1%. On independently tested acquisitions, accuracy was 87.1–90.4%.

Conclusions

Even in compound stones, the main component can be reliably determined using dual energy CT and machine learning, irrespective of dose protocol.

Key Points

• Spectral Detector Dual Energy CT and Machine Learning allow for an accurate prediction of stone composition.

• Ex-vivo study demonstrates the dose independent assessment of pure and compound stones.

• Lowest accuracy is reported for compound stones with struvite as main component.

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Abbreviations

CI:

Conventional images

CT:

Computed tomography

CTDIvol :

Volumetric computed tomography dose index

DSCT:

Dual source computed tomography

LD1/2 :

Low dose acquisition 1/2

ML:

Machine learning

ND1/2 :

Normal dose acquisition 1/2

NN:

Neural networks

ROC:

Receiver operator characteristics

ROI:

Region of interest

SDCT:

Spectral detector computed tomography

UA:

Uric acid

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Acknowledgements

The authors would like to thank Manuel Ritter and Albrecht Hesse for providing probes of rare stones. The authors would also like to thank Jasmin A. Holz for her support.

Funding

Else Kröner-Fresenius-Stiftung (Grant 2018_EKMS34 to NGH and 2016-Kolleg-19 to SL).

Author information

Authors and Affiliations

  1. Faculty of Medicine and University Hospital Cologne, Institute for Diagnostic and Interventional Radiology, University of Cologne, Kerpener Str. 62, 50937, Cologne, Germany

    Nils Große Hokamp, Simon Lennartz, Daniel Pinto dos Santos, David Maintz & Stefan Haneder

  2. Else Kröner Forschungskolleg Clonal Evolution in Cancer, University Hospital Cologne, Cologne, Germany

    Simon Lennartz

  3. Faculty of Medicine and University Hospital Cologne, Department of Urology, University of Cologne, Cologne, Germany

    Johannes Salem & Axel Heidenreich

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  1. Nils Große Hokamp
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Correspondence to Nils Große Hokamp.

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Guarantor

The scientific guarantor of this publication is Dr. Nils Große Hokamp.

Conflict of interest

NGH: Speakers honoraria from Philips Healthcare. Travel Support from Philips Healthcare. SL: Research and travel support from Philips Healthcare. DM: Speakers honoraria from Philips Healthcare. All other authors: Nothing to disclose.

Statistics and biometry

NGH, SL, DPS have significant statistical expertise.

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Written informed consent was not required for this study because this is a non-human research.

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Institutional Review Board approval was not required because this is a non-human research.

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Große Hokamp, N., Lennartz, S., Salem, J. et al. Dose independent characterization of renal stones by means of dual energy computed tomography and machine learning: an ex-vivo study. Eur Radiol 30, 1397–1404 (2020). https://doi.org/10.1007/s00330-019-06455-7

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