Urinary stone differentiation in patients with large body size using dual-energy dual-source computed tomography

The purpose of this phantom study was to determine the utility of a third-generation dual-source CT scanner with increased dual-energy spectral separation in differentiating urinary stone composition. Eighty-seven urinary stones from humans were scanned in 35-, 40-, 45-, and 50-cm wide anthropomorphic phantoms with a third-generation dual-source scanner (system A) with a high-energy beam of 150 kV plus 0.6-mm tin filtration (Sn). The low-energy data were acquired at 70, 80, 90, and 100 kV. A second-generation dual-source scanner (system B) was used to acquire data at 140 kV plus 0.4-mm Sn for the high-energy and 80 or 100 kV for the low-energy images. Volume CT dose index was matched for a given phantom size. CT number ratios were calculated and used to differentiate uric acid from non-uric acid stones and oxalate from apatite stones in an ROC analysis. The area under the curve (AUC) of the ROC curve for uric acid versus non-uric acid stones increased for large phantoms. For example, for imaging of the 45-cm wide phantom with system A at the 100- and 150-kV Sn low- and high-energy combination, the AUC was 0.99, whereas for system B at the 100- and 140-kV Sn combination, the AUC was 0.86. At each phantom size and for all energy combinations, the AUC values for oxalate versus apatite stones were higher for system A than they were for any energy combination for system B. Compared with use of second-generation dual-source CT, use of third-generation dual-source CT at the energy combination of 100 and 150 kV Sn improved classification of urinary stones across a wide range of phantom sizes and increased the ability to differentiate oxalate from apatite stones.

The purpose of this study was to assess the feasibility of using dual-energy CT to accurately quantify uric acid and non-uric acid components in urinary stones of mixed composition. A total of 24 urinary stones were analyzed with micro CT to serve as the reference standard for uric acid and non-uric acid composition. These stones were placed in water phantoms to simulate body attenuation of slim to obese adults and scanned with a third-generation dual-source CT scanner by use of dual-energy modes adaptively selected on the basis of phantom size. CT number ratio, which is distinct for different materials, was calculated for each pixel of the stones. Each pixel was then classified as uric acid and non-uric acid by comparison of the CT number ratio with preset thresholds ranging from 1.10 to 1.70. Minimal, maximal, and root-mean-square errors were calculated by comparing composition with the reference standard, and the threshold with the minimal root-mean-square error was determined. A paired t test was performed to compare the stone composition determined with dual-energy CT with the reference standard obtained with micro CT. The optimal CT number ratio threshold ranged from 1.27 to 1.55, dependent on phantom size. The root-mean-square error ranged from 9.60% to 12.87% across all phantom sizes. Minimal absolute error ranged from 0.04% to 1.24% and maximal absolute error from 22.05% to 35.46%. Dual-energy CT and the reference micro CT did not differ significantly on uric acid and non-uric acid composition (paired t test, p = 0.20-0.96). Accurate quantification of uric acid and non-uric acid composition in mixed stones is possible with dual-energy CT.

BACKGROUND AND AIMS The risk of urinary tract stone formation in patients with autosomal dominant polycystic kidney disease (ADPKD) is 20%, twice as high as in the normal population. The most important reason for this increased tendency to stone disease is thought to be anatomic changes in the kidney. To investigate metabolic factors that predispose to urinary system stone formation in individuals with ADPKD. METHOD A total of 180 volunteers who applied to our outpatient clinic between December 2019 and May 2021 were included in the study. Four groups were formed: patients with ADPKD and urinary system stones (ADPKD + STONE) (n = 42), patients with ADPKD but no stones (ADPKD + STONE FREE) (n = 43), stone patients without ADPKD (STONE) (n = 47) and healthy controls (HEALTHY) (n = 48) groups. Spot urine pH, 24-h urine volume, calcium, uric acid, oxalate, citrate, sodium and magnesium parameters were measured. STONE group data were obtained retrospectively. Along with these parameters, serum electrolyte, parathormone (PTH), ferritin, albumin, creatinine, urea and uric acid values were also compared. A P value of <.05 was considered significant for statistical analysis. RESULTS There was no statistically significant difference between the four groups in terms of age and gender (P > .05). The citrate value in 24-h urine was significantly lower in the ADPKD + STONE and ADPKD + STONE-FREE groups than the STONE and HEALTHY groups (P < .05). The citrate value in the 24-h urine was significantly lower in the STONE group than in the HEALTHY group (P < .05). The citrate value in 24-h urine did not differ significantly between the ADPKD + STONE and ADPKD + STONE FREE groups (P > .05). In the ADPKD + STONE and ADPKD + STONE-FREE groups, the 24-h urinary calcium value was significantly lower than the STONE and HEALTHY groups (P < .05). In the STONE group, the 24-h urinary calcium value was significantly higher than the HEALTHY group (P < .05). There was no significant difference in the 24-h urinary calcium value between the ADPKD + STONE and ADPKD + STONE FREE groups (P > .05). The uric acid level in 24-h urine was significantly lower in the ADPKD + STONE and ADPKD + STONE-FREE groups than in the HEALTHY group (P < .05). The uric acid value in the 24-h urine did not differ significantly between the ODPBH + STONE, ADPKD + STONE-FREE and STONE groups (P > .05). The uric acid value in 24-h urine did not differ significantly between the STONE and HEALTHY groups (P > .05). Oxalate, sodium and magnesium values in 24-h urine and 24-h urine volume did not differ significantly between the groups (P > .05). Urine pH value was significantly lower in the ADPKD + STONE, ADPKD + STONE-FREE and STONE groups than in the HEALTHY group (P < .05). The pH value did not differ significantly between ADPKD + STONE, ADPKD + STONE-FREE and STONE groups (P > .05). Serum PTH values were significantly higher in the ADPKD + STONE and ADPKD + STONE-FREE groups than in the HEALTHY group (P < .05). The PTH values did not differ significantly between the STONE and HEALTHY groups (P > .05). PTH values did not differ significantly between ODPBH + STONE, ADPKD + STONE-FREE and STONE groups (P > .05). There was no significant difference between the groups in terms of serum ferritin, sodium, potassium, calcium, phosphorus, magnesium and uric acid values (P > .05). CONCLUSION In our study, 24-h urine citrate level was found to be significantly lower in both ADPKD + STONE and ADPKD + STONE-FREE patients compared to the volunteers in the STONE and HEALTHY groups (P < .05). In conclusion, patients with ADPKD should also be evaluated for hypocitraturia in the outpatient clinic.

BackgroundUrolithiasis has emerged as a global affliction, recognized as one of the most excruciating medical issues. The elemental composition of stones provides crucial information, aiding in understanding the causes, mechanisms, and individual variations in stone formation. By understanding the interactions between elements in various types of stones and exploring the key role of elements in stone formation, insights are provided for the prevention and treatment of urinary stone disease.MethodsThis study collected urinary stone samples from 80 patients in Beijing. The chemical compositions of urinary stones were identified using an infrared spectrometer. The concentrations of major and trace elements in the urinary stones were determined using Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES) and Inductively Coupled Plasma Mass Spectrometry (ICP-MS), respectively. The data were processed using correlation analysis and Principal Component Analysis (PCA) methods.ResultsUrinary stones are categorized into five types: the calcium oxalate (CO) stone, carbonate apatite (CA) stone, uric acid (UA) stone, mixed CO and CA stone, and mixed CO and UA stone. Ca is the predominant element, with an average content ranging from 2.64 to 27.68% across the five stone groups. Based on geochemical analysis, the high-content elements follow this order: Ca > Mg > Na > K > Zn > Sr. Correlation analysis and PCA suggested significant variations in the interactions between elements for different types of urinary stones. Trace elements with charges and ionic structures similar to Ca may substitute for Ca during the process of stone formation, such as Sr and Pb affecting the Ca in most stone types except mixed stone types. Moreover, the Mg, Zn and Ba can substitute for Ca in the mixed stone types, showing element behavior dependents on the stone types.ConclusionThis study primarily reveals distinct elemental features associated with five types of urinary stones. Additionally, the analysis of these elements indicates that substitutions of trace elements with charges and ion structures similar to Ca (such as Sr and Pb) impact most stone types. This suggests a dependence of stone composition on elemental behavior. The findings of this study will enhance our ability to address the challenges posed by urinary stones to global health and improve the precision of interventions for individuals with different stone compositions.

Urinary stones have four main types of components: calcium, uric acid, ammonium magnesium phosphate, and cystine, and the type of treatment must be selected according to these types. Although X-ray computed tomography (CT) is currently the most promising method for differentiating the component types of urinary stones in vivo, both conventional and dual-energy CTs are subject to limits in this component differentiation. In this paper, we show the usefulness of an energy-resolved CT system for determining urinary stone components. Standard laboratory chemicals were used as substitutes for true urinary stones, and the effective atomic numbers of the chemicals were estimated to see if they could be used to identify the different types of urinary stones.

Large scale patterns in animal assemblages are increasingly being used as a method of understanding how species abundances in such assemblages are determined (see Lawton 1989, 1991, for reviews). In particular, the relationship between species abundance, measured as density or number of individuals, and species body size has received considerable interest (Brown and Maurer 1987, Gaston 1988, Morse et al. 1988, Tokeshi 1990, Basset and Kitching 1991, Nee et al. 1991, Griffiths 1992, Novotny 1992, Blackburn et al. 1993, Currie 1993). Much less attention has been given to other measures of abundance across animal assemblages, such as the distributions of biomass and energy use across species. One notable exception is a study of a bird assemblage by Maurer and Brown (1988). Maurer and Brown showed how biomass and energy use were distributed across 380 species of North American terrestrial birds, using literature data on body size, population density and individual energy use. When biomass and population energy use are plotted against mean body weight for individual species on log-log axes, values for individual species fall within well defined tetrahedra (Figs 3 and 4 in Maurer and Brown 1988, reproduced as Fig. la,b here; see also discussion on p. 1927 of Maurer and Brown 1988). The upper boundaries of these tetrahedra, representing maximum species biomass and energy use for each body size, increase linearly up to a weight of about 60 g, before levelling off. The lower boundaries increase linearly across the whole body size range, with slopes of approximately 1.0 and 0.67 for the biomass and energy use graphs respectively. The left hand boundary of each tetrahedron is determined by the variation in abundances of the very smallest species in the assemblage, but what constrains the upper and lower boundaries is not obvious, and Maurer and Brown (1988: 1930) suggest that defining these constraints should be a focus of future macro-ecological research. We agree wholeheartedly with the aim of using macroecological patterns in assemblages to understand species abundances. We would, however, draw attention to explanations for both the lower and upper boundaries of the two tetrahedra which suggest that the biomass and energy use distributions are artefacts of the abundance data from which the species biomass and energy use values were calculated. Some account of these artefacts will be needed in future considerations of these distributions. The explanations presented are partly extensions of previously published observations on possible artefacts in assemblage patterns (Blackburn et al. 1990), and apply to the patterns in biomass and energy use in identical fashion. We deal with the lower boundary first. Maurer and Brown calculated species biomass, B, for each bird species as B = M-D, where M is the mean body weight of individuals of the species, and D is the species density. The density data were taken from the Breeding Bird Surveys (BBS) conducted by the US and Canadian Fish and Wildlife Services, for which birds are counted over a large number of standard transect routes. They were calculated as the average species density per route on which the species was recorded. Since the minimum density a bird species can have in the BBS data is 1.0 individual/route, the minimum value for B is M, when D = 1.0. Thus for the plot of log species biomass against log body size, species points cannot lie below the line with slope 1.0 and intercept at the origin (when B = M. 1.0, or logB = logM+ logl.0 = logM). Since the bird assemblage used by Maurer and Brown includes species of density 1.0 across a wide range of body sizes (Brown and Maurer 1987), this slope indeed approximates 1.0 (Maurer and Brown 1988: Fig. 3).

Noninvasive Differentiation of Uric Acid versus Non–Uric Acid Kidney Stones Using Dual-Energy CT

The parameters that drive population dynamics typically show a relationship with body size. By contrast, there is no theoretical or empirical support for a body-size dependence of mutual interference, which links foraging rates to consumer density. Here, I develop a model to predict that interference may be positively or negatively related to body size depending on how resource body size scales with consumer body size. Over a wide range of body sizes, however, the model predicts that interference will be body-size independent. This prediction was supported by a new dataset on interference and consumer body size. The stabilizing effect of intermediate interference therefore appears to be roughly constant across size, while the effect of body size on population dynamics is mediated through other parameters.

The use of dual energy computed tomography (DECT) has become widespread in urology, including in the diagnosis of urolithiasis. DECT makes it possible to visualize and differentiate urinary stones of different chemical density and composition from each other. Studies have shown the advantages of DECT not only in the detection, but also in the differentiation of the main groups of urinary stones. At the same time, a number of in vivo studies evaluate DECT as a technique for the high-accuracy differential diagnosis of urate stones. The accuracy of diagnosing urate stones with DECT reaches 92-100%, as confirmed by sensitivity (84.6-98.4%) and specificity (100%).

PurposeTo assess the potential of dual-energy computed tomography (DECT) to identify urinary stone components, particularly uric acid and calcium oxalate monohydrate, which are unsuitable for extracorporeal shock wave lithotripsy (ESWL).Materials and MethodsThis clinical study included 246 patients who underwent removal of urinary stones and an analysis of stone components between November 2009 and August 2013. All patients received preoperative DECT using two energy values (80 kVp and 140 kVp). Hounsfield units (HU) were measured and matched to the stone component.ResultsSignificant differences in HU values were observed between uric acid and nonuric acid stones at the 80 and 140 kVp energy values (p<0.001). All uric acid stones were red on color-coded DECT images, whereas 96.3% of the nonuric acid stones were blue. Patients with calcium oxalate stones were divided into two groups according to the amount of monohydrate (calcium oxalate monohydrate group: monohydrate≥90%, calcium oxalate dihydrate group: monohydrate<90%). Significant differences in HU values were detected between the two groups at both energy values (p<0.001).ConclusionsDECT improved the characterization of urinary stone components and was a useful method for identifying uric acid and calcium oxalate monohydrate stones, which are unsuitable for ESWL.

ObjectivesTo prospectively validate three quantitative single-energy CT (SE-CT) methods for classifying uric acid (UA) and non-uric acid (non-UA) stones.MethodsBetween September 2018 and September 2019, 116 study participants were prospectively included in the study if they had at least one 3–20-mm urinary stone on an initial urinary tract SE-CT scan. An additional dual-energy CT (DE-CT) scan was performed, limited to the stone of interest. Additionally, to include a sufficient number of UA stones, eight participants with confirmed UA stone on DE-CT were retrospectively included. The SE-CT stone features used in the prediction models were (1) maximum attenuation (maxHU) and (2) the peak point Laplacian (ppLapl) calculated at the position in the stone with maxHU. Two prediction models were previously published methods (ppLapl-maxHU and maxHU) and the third was derived from the previous results based on the k-nearest neighbors (kNN) algorithm (kNN-ppLapl-maxHU). The three methods were evaluated on this new independent stone dataset. The reference standard was the CT vendor’s DE-CT application for kidney stones.ResultsAltogether 124 participants (59 ± 14 years, 91 men) with 106 non-UA and 37 UA stones were evaluated. For classification of UA and non-UA stones, the sensitivity, specificity, and accuracy were 100% (37/37), 97% (103/106), and 98% (140/143), respectively, for kNN-ppLapl-maxHU; 95% (35/37), 98% (104/106), and 97% (139/143) for ppLapl-maxHU; and 92% (34/37), 94% (100/106), and 94% (134/143) for maxHU.ConclusionA quantitative SE-CT method (kNN-ppLapl-maxHU) can classify UA stones with accuracy comparable to DE-CT.Key Points• Single-energy CT is the first-line diagnostic tool for suspected renal colic.• A single-energy CT method based on the internal urinary stone attenuation distribution can classify urinary stones into uric acid and non-uric acid stones with high accuracy.• This immensely increases the availability of in vivo stone analysis.

Existing techniques for estimating the morphometric size at maturity of crabs are based on assumptions that may be unnecessary. Here we demonstrate a new method of detecting changes in relative growth (or allometric) relationships and estimating morphometric size at maturity. This method involves fitting several smoothing splines to relationships between body size and claw size, selecting the "best" spline, and finding this spline's maximum second derivative. The body size where the second derivative of the best spline is maximized estimates the morphometric size at maturity. Monte Carlo simulations suggest that uncertainty and bias in the estimate of morphometric size at maturity can be decreased by measuring a large number of crabs from a wide range of body sizes. Our spline method does not require a priori assumptions about the shape of the relative growth relationship; it can detect multiple changes in the relative growth rate; and it is robust to outliers. The modeling technique may also be used to identify regions of allometric change in other types of relationships. We demonstrate the new technique by estimating the morphometric size at sexual maturity for males of both brachyuran (Chionoecetes tanneri) and anomuran (Paralomis spinosissima and P. formosa) crabs.

Reproductive parasitism among males is prevalent in fishes. Typically, small ripe males parasitize the reproductive effort of large bourgeois males by using various behavioural tactics. We examined the size‐dependent advantages of parasitic behavioural tactics in a shell‐brooding cichlid fish of Lake Tanganyika with three male types (large bourgeois males and medium–dwarf parasitic males). The extremely small ‘dwarf males’ weighing only 2.5% on average of large males, perform a specialized tactic in which they avoid attacks by the resident large males by entering the inner part of the whorl of the shell where a female is spawning. Field observations and a manipulation experiment revealed that the very small size of dwarf males is essential for utilizing this positional advantage. Larger dwarf males and medium males opportunistically adopt darting and sneaking which likely result in very low reproductive outcomes. The size associated advantage and disadvantage of parasitic tactics are the major factors shaping the size distribution of ripe males in this species. The success of parasitic spawning by dwarf males is determined not only by body size of the males, but also by the relative sizes of females and shells in which they spawn. These factors would affect the choice of different tactics among dwarf males. The analyses of body condition among ripe and unripe males across a wide range of body sizes suggested that onset of sexual activities at very small body sizes in dwarf males may be associated with higher condition factors that is a pre‐requisite for maintaining investment in testes and intense reproductive activities.

We assessed the potential of dual-energy computed tomography (CT) for the differentiation between uric acid (UA)-containing and non-UA-containing urinary stones. Forty urinary stones of 16 different compositions in two sizes (<and>or=5 mm) were examined in an ex vivo model. Thirty stones consisted of pure calcium oxalate (whewellite or wheddellite), calcium phosphate (apatite, brushite, or vaterite), ammonium magnesium phosphate (struvite), UA, ammonium acid urate, ammonium phosphate, sodium hydrogen urate, or cystine, and ten stones were of mixed composition (UA-sodium hydrogen urate, whewellite-urate, wheddellite-urate, whewellite-brushite, or whewellite-brushite-struvite). Scans were performed using dual-source CT in a dual-energy mode with the tubes simultaneously operating at 80 and 140 kV. Two readers analysed the data with respect to stone attenuation at each energy level. The stones were classified as UA- or non-UA-containing using manual attenuation measurements and software analysis results. Sensitivity, specificity, PPV, and NPV were calculated using crystallographic stone analysis as the gold standard. Twenty-six out of 40 stones (65%) contained no UA; 14 stones (35%) contained UA. When compared with UA-containing stones, the differences in attenuation values at 80 and 140 kV were significantly (P<0.001) higher in stones containing no UA. The software automatically mapped 39/40 stones (98%). Only one (2%) 2 mm UA-stone was missed. The software correctly classified all detected stones as UA- or non-UA-containing. The attenuation values of the missed stone were manually plotted into the analysis sheet which allowed for the correct classification of the stone (containing UA). Therefore, the sensitivity, specificity, PPV, and NPV for the detection of UA-containing stones was 100%. Ex vivo experience indicates that differentiation between UA- and non-UA-containing stones can be accurately performed using dual-source dual-energy CT.

Interpretation of whole body dual energy X-ray absorptiometry measures in children: comparison with peripheral quantitative computed tomography