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Rapid kV-switching single-source dual-energy CT ex vivo renal calculi characterization using a multiparametric approach: refining parameters on an expanded dataset

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Abstract

Purpose

We aimed to determine the best algorithms for renal stone composition characterization using rapid kV-switching single-source dual-energy computed tomography (rsDECT) and a multiparametric approach after dataset expansion and refinement of variables.

Methods

rsDECT scans (80 and 140 kVp) were performed on 38 ex vivo 5- to 10-mm renal stones composed of uric acid (UA; n = 21), struvite (STR; n = 5), cystine (CYS; n = 5), and calcium oxalate monohydrate (COM; n = 7). Measurements were obtained for 17 variables: mean Hounsfield units (HU) at 11 monochromatic keV levels, effective Z, 2 iodine-water material basis pairs, and 3 mean monochromatic keV ratios (40/140, 70/120, 70/140). Analysis included using 5 multiparametric algorithms: Support Vector Machine, RandomTree, Artificial Neural Network, Naïve Bayes Tree, and Decision Tree (C4.5).

Results

Separating UA from non-UA stones was 100% accurate using multiple methods. For non-UA stones, using a 70-keV mean cutoff value of 694 HU had 100% accuracy for distinguishing COM from non-COM (CYS, STR) stones. The best result for distinguishing all 3 non-UA subtypes was obtained using RandomTree (15/17, 88%).

Conclusions

For stones 5 mm or larger, multiple methods can distinguish UA from non-UA and COM from non-COM stones with 100% accuracy. Thus, the choice for analysis is per the user’s preference. The best model for separating all three non-UA subtypes was 88% accurate, although with considerable individual overlap between CYS and STR stones. Larger, more diverse datasets, including in vivo data and technical improvements in material separation, may offer more guidance in distinguishing non-UA stone subtypes in the clinical setting.

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Abbreviations

ANN:

Artificial Neural Network

C4.5:

Decision Tree

COM:

Calcium oxalate monohydrate

CT:

Computed tomography

CYS:

Cystine

DECT:

Dual-energy CT

HU:

Hounsfield unit

NBTree:

Naïve Bayes Tree

rsDECT:

Rapid kV-switching single-source DECT

STR:

Struvite

SVM:

Support Vector Machine

UA:

Uric acid

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Author information

Author notes

  1. Miao He

    Present address: American Express, Phoenix, AZ, USA

  2. Steven I. Zell

    Present address: AZ Tech Radiology, Phoenix, AZ, USA

Authors and Affiliations

  1. Department of Radiology, Mayo Clinic Hospital, 5777 E Mayo Blvd, Phoenix, AZ, 85054, USA

    J. Scott Kriegshauser, Robert G. Paden, Steven I. Zell & Mark D. Sugi

  2. Department of Urology, Mayo Clinic Hospital, Phoenix, AZ, USA

    Mitchell R. Humphreys

  3. Department of Radiology, Mayo Clinic, Scottsdale, AZ, USA

    Alvin C. Silva

  4. School of Computing, Informatics, Decision Systems Engineering, Arizona State University, Tempe, AZ, USA

    Miao He, Yinlin Fu & Teresa Wu

Authors
  1. J. Scott Kriegshauser
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  2. Robert G. Paden
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  3. Miao He
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  4. Mitchell R. Humphreys
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  6. Yinlin Fu
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  7. Teresa Wu
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Corresponding author

Correspondence to J. Scott Kriegshauser.

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The authors have no sources of funding or conflicts of interest to declare.

Ethical approval

All procedures performed in this study involving human participants were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki declaration and its later amendments. This study was compliant with the Health Insurance Portability and Accountability Act of 1996 and was approved by our Institutional Review Board.

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Kriegshauser, J.S., Paden, R.G., He, M. et al. Rapid kV-switching single-source dual-energy CT ex vivo renal calculi characterization using a multiparametric approach: refining parameters on an expanded dataset. Abdom Radiol 43, 1439–1445 (2018). https://doi.org/10.1007/s00261-017-1331-0

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  • DOI: https://doi.org/10.1007/s00261-017-1331-0

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