| Objective(1) To evaluate the values of signal-energy CT and dual-energy CT for the differentiation of urinary stone composition in vitro, with infrared spectroscopy as the reference standard.(2) To evaluate the values of dual-source dual-energy CT for the differentiation of urinary stone composition in vitro, with infrared spectroscopy as the reference standard.(3) To evaluate the ability of dual-source dual-energy CT to differentiate urinary stone of different compositions in vivo.Methods(1) One-hundred and three urinary stones were placed in pig kidneys and performed using single-energy and dual-energy mode of DSCT. Hounsfield units (HU) of each stone were recorded for the 120, 80 and 140 kV datasets by hand-drawing method. Use HU80kV and HU140kV to calculate HU difference, HU ratio and dual energy index (DEI). Stones were divided into different groups according to infrared spectroscopy stone analysis. HU120kV, HU difference, HU ratio and DEI were compared among the stone groups with one-way ANOVA. Discriminant analysis was used to identify parameters contributing to classifying stone groups.(2) One-hundred and forty-six urinary stones were collected and performed using dual-source dual-energy CT. Hounsfield units (HU) of each stone were recorded for the 80 kV and the 140 kV datasets by hand-drawing method.HU difference, HU ratio and DEI were calculated. Use dual energy software to determine the composition of all stones, the results were compared with infrared spectroscopy. Use one-way ANOVA to compare HU difference, HU ratio and DEI of different stones groups according to infrared spectroscopy.(3) Ninety patients with known or suspected urinary stone disease were scanned using a DSCT scanner in the dual-energy mode. Hounsfield units (HU) of each stone were recorded for the 80 kV and the 140 kV datasets by hand-drawing method.HU difference, HU ratio and DEI were calculated. Urinary stones were classified as UA stones, mixed UA stones, cystine stones and calcium stones based on dual energy software, the results compared with the infrared spectroscopy analysis of stone samples. Use one-way ANOVA to compare HU difference, HU ratio and DEI of different stones groups according to infrared spectroscopy.Results(1) There were 15 UA stones, 10 mixed UA stone, 19 cystine stones, 16 struvite stones, 17 CaP stones, 26 CaOx and brushite stones according to infrared spectroscopy. Statistical differences in HU120kV (467±95), (690±105), (747±93), (838±112), (1045±199), and (1214±228) HU respectively,HU difference (-13±18), (193±63), (235±29), (320±63), (448±98), and (533±116) HU respectively, HU ratio (0.97±0.04, 1.29±0.06, 1.34±0.03, 1.40±0.04, 1.45±0.03, and 1.47±0.03 respectively), and DEI (-0.004±0.006, 0.054±0.014, 0.065±0.006, 0.081±0.011, 0.100±0.013, and 0.111±0.013 respectively) among different stone groups(P<0.001). There were no statistical differences in all measurement between cystine stones and mixed UA stones, between CaP stones and CaOx and brushite stones (P>0.05). There were no statistical differences in HU120kV and HU140kV between struvite stones and cystine or mixed UA stones (P >0.05). With a independent method, the accuracy of correctly classifying stone composition with HU120kV, HU difference, HU ratio and DEI were 48.5%, 61.2%,61.2% and 62.1% respectively. With a stepwise method, HU difference and HU ratio entered the function, with which the accuracy was 66.0%, but there were no statistical differences in accuracy between above methods (X 2 =7.48,P=0.113).(2) There were 15 UA stones, 10 mixed UA stone, 19 cystine stones, 16 struvite stones, 17 CaP stones,22 CaOx stones and 47 mix calcium stones. Statistical differences in HU difference (-13±18), (193±63), (235±29), (320±63), (448±98), (512±97), and (536±79) HU respectively, HU ratio (0.97±0.04, 1.29±0.06, 1.34±0.03, 1.40±0.04, 1.45±0.03, 1.47±0.03, and 1.49±0.03 respectively), and DEI (-0.004±0.006, 0.054±0.014, 0.065±0.006, 0.081±0.011, 0.100±0.013, 0.108±0.011, and 0.112±0.009 respectively) among different stone groups (P<0.001). There were statistical differences in HU difference, HU ratio and DEI between UA stones and the other groups, between CaP or CaOx or mix calcium stones and other four groups, between struvite stones and mixed UA or cystine stones (P<0.05). There were statistical differences in HU ratio and DEI between CaP stones and mix calcium stones (P<0.05). The sensitivity, specificity and accuracy of dual energy software for the detection of UA were 96.0%,100%,and 99.3%, Kappa value was 0.98; for the detection of cystine were 89.5%,96.9%, and 95.9%, Kappa value was 0.83; for the detection of oxalate were 100%,53.7%,and 78.8%, Kappa value was 0.56. Dual energy software correctly characterized 15 UA stones, 9 mixed UA stones, 17 cystine stones, and 22 CaOx stones.4 struvite stones were considered containing cystine. 2 cystine stone, 1 mixed UA stone, 12 struvite stones,17 CaP stones and 47 mixed calcium stones were considered containing oxalate. (3) DSCT detected 64 patients with urinary stone disease. In 55 patients, stones were sampled. Dual energy software correctly characterized 4 UA stones, 1 mixed UA stone, 2 cystine stone, and 42 calcium stones. 4 struvite stones, 1 mixed ammonium urate and calcium stone, 1 mixed UA stone were classified as calcium stones. Statistical differences in HU difference (18±12), (214±21), (329±35), (360±49), (458±97) and (497±110) HU respectively, HU ratio (1.04±0.02, 1.36±0.02, 1.49±0.04, 1.50±0.08, 1.52±0.05 and 1.53±0.04 respectively), and DEI (0.006±0.004, 0.062±0.002, 0.089±0.006, 0.095±0.013, 0.107±0.011 and 0.112±0.012 respectively) among UA stones, cystine stone, struvite stones, CaP stones, CaOx stones and mix calcium stones (P<0.001). There were statistical differences in HU difference, HU ratio and DEI between UA stones and the other groups, in HU ratio and DEI between cystine stones and the other groups, in HU difference and DEI between struvite stones and CaOx or mix calcium stones, in HU difference between cystine stones and CaOx or mix calcium stones, in DEI between CaP stones and mix calcium stones (P<0.05).Conclusion(1) Dual-energy CT can improve the accuracy for the differentiation of urinary stone composition compare to single-energy CT in vitro, especially beneficial to identify UA stones from other stones, struvite stones from cystine, mixed UA stones.(2) Dual-source dual-energy CT has the ability to differentiate UA stones, cystine stones, mixed UA stones and calcium stones in vitro, but it could not distinguish struvite stones and subtypes of calcium stones.(3) Dual-source dual-energy CT has the ability to differentiate UA stones, cystine stones, mixed UA stones and calcium stones in vivo. |
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