Ex Vivo Renal Stone Characterization with Single-Source Dual-Energy Computed Tomography: A Multiparametric Approach

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• 作者：J. Scott Kriegshauser ; MD^a ; ^{skriegshauser@mayo.edu" class="auth_mail" title="E-mail the corresponding author} ; Alvin C. Silva ; MD^b ; Robert G. Paden ; MS^a ; Miao He ; PhD^c ; Mitchell R. Humphreys ; MD^d ; Steven I. Zell ; MD^a ; Yinlin Fu ; PhD^c ; Teresa Wu ; PhD^c
• 关键词：Dual-energy computed tomography ; multiparametric data analysis ; renal stone composition
• 刊名：Academic Radiology
• 出版年：2016
• 出版时间：August 2016
• 年：2016
• 卷：23
• 期：8
• 页码：969-976
• 全文大小：535 K

文摘

We aimed to investigate a multiparametric approach using single-source dual-energy computed tomography (ssDECT) for the characterization of renal stones.

Materials and Methods

ssDECT scans were performed at 80 and 140 kVp on 32 ex vivo kidney stones of 3–10 mm in a phantom. True composition was determined by infrared spectroscopy to be uric acid (UA; n = 14), struvite (n = 7), cystine (n = 7), or calcium oxalate monohydrate (n = 4). Measurements were obtained for up to 52 variables, including mean density at 11 monochromatic keV levels, effective Z, and multiple material basis pairs. The data were analyzed with five multiparametric algorithms. After omitting 8 stones smaller than 5 mm, the remaining 24-stone dataset was similarly analyzed. Both stone datasets were also analyzed with a subset of 14 commonly used variables in the same fashion.

Results

For the 32-stone dataset, the best method for distinguishing UA from non-UA stones was 97% accurate, and for distinguishing the non-UA subtypes was 72% accurate. For the 24-stone dataset, the best method for distinguishing UA from non-UA stones was 100% accurate, and for distinguishing the non-UA subtypes was 75% accurate.

Conclusion

Multiparametric ssDECT methods can distinguish UA from non-UA stones of 5 mm or larger with 100% accuracy. The best model to distinguish the non-UA renal stone subtypes was 75% accurate. Further refinement of this multiparametric approach may increase the diagnostic accuracy of separating non-UA subtypes and assist in the development of a clinical paradigm for in vivo use.