Diagnostic accuracy of dual-energy computed tomography (DECT) to differentiate uric acid from non-uric acid calculi: systematic review and meta-analysis

Development of a radiomics model to discriminate ammonium urate stones from uric acid stones in vivo : A remedy for the diagnostic pitfall of dual-energy computed tomography

BACKGROUND

Dual-energy computed tomography (DECT) is purported to accurately distinguish uric acid stones from non-uric acid stones. However, whether DECT can accurately discriminate ammonium urate stones from uric acid stones remains unknown. Therefore, we aimed to explore whether they can be accurately identified by DECT and to develop a radiomics model to assist in distinguishing them.

METHODS

This research included two steps. For the first purpose to evaluate the accuracy of DECT in the diagnosis of uric acid stones, 178 urolithiasis patients who underwent preoperative DECT between September 2016 and December 2019 were enrolled. For model construction, 93, 40, and 109 eligible urolithiasis patients treated between February 2013 and October 2022 were assigned to the training, internal validation, and external validation sets, respectively. Radiomics features were extracted from non-contrast CT images, and the least absolute shrinkage and selection operator (LASSO) algorithm was used to develop a radiomics signature. Then, a radiomics model incorporating the radiomics signature and clinical predictors was constructed. The performance of the model (discrimination, calibration, and clinical usefulness) was evaluated.

RESULTS

When patients with ammonium urate stones were included in the analysis, the accuracy of DECT in the diagnosis of uric acid stones was significantly decreased. Sixty-two percent of ammonium urate stones were mistakenly diagnosed as uric acid stones by DECT. A radiomics model incorporating the radiomics signature, urine pH value, and urine white blood cell count was constructed. The model achieved good calibration and discrimination {area under the receiver operating characteristic curve (AUC; 95% confidence interval [CI]), 0.944 (0.899-0.989)}, which was internally and externally validated with AUCs of 0.895 (95% CI, 0.796-0.995) and 0.870 (95% CI, 0.769-0.972), respectively. Decision curve analysis revealed the clinical usefulness of the model.

CONCLUSIONS

DECT cannot accurately differentiate ammonium urate stones from uric acid stones. Our proposed radiomics model can serve as a complementary diagnostic tool for distinguishing them in vivo .

Advancements in Uric Acid Stone Detection: Integrating Deep Learning with CT Imaging and Clinical Assessments in the Upper Urinary Tract

INTRODUCTION

Among upper urinary tract stones, a significant proportion comprises uric acid stones. The aim of this study was to use machine learning techniques to analyze CT scans and blood and urine test data, with the aim of establishing multiple predictive models that can accurately identify uric acid stones.

METHODS

We divided 276 patients with upper urinary tract stones into two groups: 48 with uric acid stones and 228 with other types, identified using Fourier-transform infrared spectroscopy. To distinguish the stone types, we created three types of deep learning models and extensively compared their classification performance.

RESULTS

Among the three major types of models, considering accuracy, sensitivity, and recall, CLNC-LR, IMG-support vector machine (SVM), and FUS-SVM perform the best. The accuracy and F1 score for the three models were as follows: CLNC-LR (82.14%, 0.7813), IMG-SVM (89.29%, 0.89), and FUS-SVM (29.29%, 0.8818). The area under the curves for classes CLNC-LR, IMG-SVM, and FUS-SVM were 0.97, 0.96, and 0.99, respectively.

CONCLUSION

This study shows the feasibility of utilizing deep learning to assess whether urinary tract stones are uric acid stones through CT scans, blood, and urine tests. It can serve as a supplementary tool for traditional stone composition analysis, offering decision support for urologists and enhancing the effectiveness of diagnosis and treatment.

Diagnostic performance and feasibility of dual-layer detector dual-energy CT for characterization of urinary stones in patients of different sizes

BACKGROUND

Urinary stones are frequently encountered in urology and are typically identified using non-contrast CT scans. Dual-energy CT (DECT) is a valuable imaging technique that produces material-specific images and allows for precise assessment of stone composition by estimating the effective atomic number (Zeff), a capability not achievable with the conventional single-energy CT's attenuation measurement method.

PURPOSE

To investigate the diagnostic performance and image quality of dual-layer detector DECT (dlDECT) in characterizing urinary stones in patients of different sizes.

METHODS

All consecutive dlDECT examinations with stone protocol and presence of urinary stones between July 2018 and November 2019 were retrospectively evaluated. Two radiologists independently reviewed 120 kVp and color-overlay Zeff images to determine stone composition (reference standard = crystallography) and image quality. The objective analysis included image noise and Zeff values measurement.

RESULTS

A total of 739 urinary stones (median size 3.7 mm, range 1-35 mm) were identified on 177 CT examinations from 155 adults (mean age, 57 ± 15 years, 80 men, median weight 82.6 kg, range 42.6-186.9 kg). Using color-overlay Zeff images, the radiologists could subjectively interpret the composition in all stones ≥ 3 mm (n = 491). For stones with available reference standards (n = 74), dlDECT yielded a sensitivity of 80% (95%CI 44-98%) and a specificity of 98% (95%CI 92-100%) in visually discriminating uric acid from non-uric acid stones. Patients weighing > 90 kg and ≤ 90 kg had similar stone characterizability (p = 0.20), with 86% of stones characterized in the > 90 kg group and 87% in the ≤ 90 kg group. All examinations throughout various patients' weights revealed acceptable image quality. A Zeff cutoff of 7.66 accurately distinguished uric acid from non-uric acid stones (AUC = 1.00). Zeff analysis revealed AUCs of 0.78 and 0.91 for differentiating calcium-based stones from other non-uric stones and all stone types, respectively.

CONCLUSION

dlDECT allowed accurate differentiation of uric acid and non-uric acid stones among patients with different body sizes with acceptable image quality.

CLINICAL IMPACT

The ability to accurately differentiate uric acid stones from non-uric acid stones using color-overlay Zeff images allows for better tailored treatment strategies, helping to choose appropriate interventions and prevent potential complications related to urinary stones in patient care.

Detection and characterization of urinary stones using material-specific images derived from contrast-enhanced dual-energy CT urography

OBJECTIVE

To determine the accuracy of material-specific images derived from contrast-enhanced dual-energy CT urography (DECTU) in detecting and measuring urinary stones in comparison with that of unenhanced images and its utility in calcified stone differentiation.

METHODS

105 patients with 202 urinary stones (121 had confirmed composition by infrared spectroscopy) underwent triphasic (unenhanced, portal venous (VP) and excretory phase (EP)) DECTU. Material-specific images were derived in VP and EP with calcium-water, calcium-iodine and CaOxalate_Dihydrate (COD)-Hydroxyapatite (HAP) as basis material pairs. Stone number and size were recorded on unenhanced images and VP and EP material-specific images, where stone densities were also measured. Material densities of calcified stones (pure calcium oxalate [pCaO, n = 34], mixed calcium oxalate [mCaO, n = 14], mixed carbonate phosphate [mCaP, n = 70]) were compared and thresholds for differentiating these stones were determined using receiver operating characteristic analysis.

RESULTS

All 202 urinary stones were detected on the unenhanced, calcium (water) and calcium (iodine) images in VP. While the detection rate was significantly decreased to 58 and 64% using calcium (water) and calcium (iodine) images in EP, respectively (all p < 0.001). Stone sizes measured on calcium (iodine) images in VP was similar to that of unenhanced images (10.6 vs 10.7 mm, p > 0.05). Significant differences in material densities were found among pCaO, mCaO and mCaP on COD(HAP) images with AUC of 0.72-0.74 for differentiating these stones.

CONCLUSION

Material-specific images in VP derived from DECTU allow reliably detecting and measuring urinary tract stones in comparison with unenhanced images and can identify calcified stones with moderate diagnostic performance to provide potential 33% dose reduction.

ADVANCES IN KNOWLEDGE

Material-specific images, especially the calcium (iodine) images in VP allow for reliable detection of urinary stones.Stone size measurement should be performed on the calcium (iodine) images in VP.Material density measurements on COD-HAP (VP) material decomposition images can be used to differentiate among pure calcium oxalate, mixed calcium oxalate and mixed carbonate phosphate stones with AUC of 0.72-0.74.

Expanding Role of Dual-Energy CT for Genitourinary Tract Assessment in the Emergency Department, From the AJR Special Series on Emergency Radiology

Among explored applications of dual-energy CT (DECT) in the abdomen and pelvis, the genitourinary (GU) tract represents an area where accumulated evidence has established the role of DECT to provide useful information that may change management. This review discusses established applications of DECT for GU tract assessment in the emergency department (ED) setting, including characterization of renal stones, evaluation of traumatic injuries and hemorrhage, and characterization of incidental renal and adrenal findings. Use of DECT for such applications can reduce the need for additional multiphase CT or MRI examinations and reduce follow-up imaging recommendations. Emerging applications are also highlighted, including use of low-energy virtual monoenergetic images (VMIs) to improve image quality and potentially reduce contrast media doses and use of high-energy VMIs to mitigate renal mass pseudoenhancement. Finally, implementation of DECT into busy ED radiology practices is presented, weighing the trade-off of additional image acquisition, processing time, and interpretation time against potential additional useful clinical information. Automatic generation of DECT-derived images with direct PACS transfer can facilitate radiologists' adoption of DECT in busy ED environments and minimize impact on interpretation times. Using the described approaches, radiologists can apply DECT technology to improve the quality and efficiency of care in the ED.

2022 Recommendations of the AFU Lithiasis Committee: Diagnosis

The choice of imaging modality is guided by the clinical presentation and the context (acute or not). Although ultrasound is safe (no radiation) and easily available, non-contrast-enhanced CT has become the gold standard in the diagnostic strategy for patients with acute flank pain because of its sensitivity (93.1%) and specificity (96.6%). It also allows determining the stone size, volume and density, visualizing their internal structure, and assessing their distance from the skin and the adjacent anatomy. All these parameters can influence the stone management and the choice of intervention modality. METHODOLOGY: These recommendations were developed using two methods: the Clinical Practice Recommendations method (CPR) and the ADAPTE method, depending on whether the issue was considered in the EAU recommendations (https://uroweb.org/guidelines/urolithiasis [EAU Guidelines on urolithiasis. 2022]) and their adaptability to the French context.

Development and external validation of a machine learning-based model to classify uric acid stones in patients with kidney stones of Hounsfield units < 800

The correct diagnosis of uric acid (UA) stones has important clinical implications since patients with a high risk of perioperative morbidity may be spared surgical intervention and be offered alkalization therapy. We developed and validated a machine learning (ML)-based model to identify stones on computed tomography (CT) images and simultaneously classify UA stones from non-UA stones. An international, multicenter study was performed on 202 patients who received percutaneous nephrolithotomy for kidney stones with HU < 800. Data from 156 (77.2%) patients were used for model development, while data from 46 (22.8%) patients from a multinational institution were used for external validation. A total of 21,074 kidney and stone contour-annotated CT images were trained with the ResNet-18 Mask R-convolutional neural network algorithm. Finally, this model was concatenated with demographic and clinical data as a fully connected layer for stone classification. Our model was 100% sensitive in detecting kidney stones in each patient, and the delineation of kidney and stone contours was precise within clinically acceptable ranges. The development model provided an accuracy of 99.9%, with 100.0% sensitivity and 98.9% specificity, in distinguishing UA from non-UA stones. On external validation, the model performed with an accuracy of 97.1%, with 89.4% sensitivity and 98.6% specificity. SHAP plots revealed stone density, diabetes mellitus, and urinary pH as the most important features for classification. Our ML-based model accurately identified and delineated kidney stones and classified UA stones from non-UA stones with the highest predictive accuracy reported to date. Our model can be reliably used to select candidates for an earlier-directed alkalization therapy.

Dual-energy CT kidney stone characterization-can diagnostic accuracy be achieved at low radiation dose?

OBJECTIVES

To assess the accuracy of low-dose dual-energy computed tomography (DECT) to differentiate uric acid from non-uric acid kidney stones in two generations of dual-source DECT with stone composition analysis as the reference standard.

METHODS

Patients who received a low-dose unenhanced DECT for the detection or follow-up of urolithiasis and stone extraction with stone composition analysis between January 2020 and January 2022 were retrospectively included. Collected stones were characterized using X-ray diffraction. Size, volume, CT attenuation, and stone characterization were assessed using DECT post-processing software. Characterization as uric acid or non-uric acid stones was compared to stone composition analysis as the reference standard. Sensitivity, specificity, and accuracy of stone classification were computed. Dose length product (DLP) and effective dose served as radiation dose estimates.

RESULTS

A total of 227 stones in 203 patients were analyzed. Stone composition analysis identified 15 uric acid and 212 non-uric acid stones. Mean size and volume were 4.7 mm × 2.8 mm and 114 mm3, respectively. CT attenuation of uric acid stones was significantly lower as compared to non-uric acid stones (p < 0.001). Two hundred twenty-five of 227 kidney stones were correctly classified by DECT. Pooled sensitivity, specificity, and accuracy were 1.0 (95%CI: 0.97, 1.00), 0.93 (95%CI: 0.68, 1.00), and 0.99 (95%CI: 0.97, 1.00), respectively. Eighty-two of 84 stones with a diameter of  ≤ 3 mm were correctly classified. Mean DLP was 162 ± 57 mGy*cm and effective dose was 2.43 ± 0.86 mSv.

CONCLUSIONS

Low-dose dual-source DECT demonstrated high accuracy to discriminate uric acid from non-uric acid stones even at small stone sizes.

KEY POINTS

• Two hundred twenty-five of 227 stones were correctly classified as uric acid vs. non-uric acid stones by low-dose dual-energy CT with stone composition analysis as the reference standard. • Pooled sensitivity, specificity, and accuracy for stone characterization were 1.0, 0.93, and 0.99, respectively. • Low-dose dual-energy CT for stone characterization was feasible in the majority of small stones  < 3 mm.

A combined model based on CT radiomics and clinical variables to predict uric acid calculi which have a good accuracy

The aim of this study was to develop a CT-based radiomics and clinical variable diagnostic model for the preoperative prediction of uric acid calculi. In this retrospective study, 370 patients with urolithiasis who underwent preoperative urinary CT scans were enrolled. The CT images of each patient were manually segmented, and radiomics features were extracted. Sixteen radiomics features were selected by one-way analysis of variance (ANOVA) and least absolute shrinkage and selection operator (LASSO). Logistic regression (LR), random forest (RF) and support vector machine (SVM) were used to model the selected features, and the model with the best performance was selected. Multivariate logistic regression was used to screen out significant clinical variables, and the radiomics features and clinical variables were combined to construct a nomogram model. The area under the receiver operating characteristic (ROC) curve (AUC), etc., were used to evaluate the diagnostic performance of the model. Among the three machine learning models, the LR model had the best performance and good robustness of the dataset. Therefore, the LR model was used to construct the nomogram. The AUCs of the nomogram model in the training set and validation set were 0.878 and 0.867, respectively, which were significantly higher than those of the radiomics model and the clinical feature model. The CT-based radiomics model based has good performance in distinguishing uric acid stones from nonuric acid stones, and the nomogram model has the best diagnostic performance among the three models. This model can provide an effective reference for clinical decision-making.

DECT Numbers in Upper Abdominal Organs for Differential Diagnosis: A Feasibility Study

Evaluating the similarity between two entities such as primary and suspected metastatic lesions using quantitative dual-energy computed tomography (DECT) numbers may be useful. However, the criteria for the similarity between two entities based on DECT numbers remain unclear. We therefore considered the possibility that a similarity in DECT numbers within the same organ could provide suitable standards. Thus, we assumed that the variation in DECT numbers within a single organ is sufficiently minimal to be considered clinically equivalent. Therefore, the purpose of this preliminary study is to investigate the differences in DECT numbers within upper abdominal organs. This retrospective study included 30 patients with data from hepatic protocol DECT scans. DECT numbers of the following parameters were collected: (a, b) 70 and 40 keV CT values, (c) slope, (d) effective Z, and (e, f) iodine and water concentration. The agreement of DECT numbers obtained from two regions of interest in the same organ (liver, spleen, and kidney) were assessed using Bland-Altman analysis. The diagnostic ability of each DECT parameter to distinguish between the same or different organs was also assessed using receiver operating characteristic analysis. The 95% limits of agreement within the same organ exhibited the narrowest value range on delayed phase (DP) CT [(c) -11.2-8.3%, (d) -2.0-1.5%, (e) -11.3-8.4%, and (f) -0.59-0.62%]. The diagnostic ability was notably high when using differences in DECT numbers on portal venous (PVP) and DP images (the area under the curve of DP: 0.987-0.999 in (c)-(f)). Using the variability in DECT numbers in the same organ as a criterion for defining similarity may be helpful in making a differential diagnosis by comparing the DECT numbers of two entities.

Dual-energy CT: Technical considerations and clinical applications

Although dual-energy CT was initially described by Hounsfield in 1973, it remains underused in clinical practice. It is therefore important to emphasize the clinical benefits and limitations of this technique. Iodine mapping makes it possible to quantify the uptake of iodine, which is very important in characterizing tumors, lung perfusion, pulmonary nodules, and the tumor response to new treatments. Dual-energy CT also makes it possible to obtain virtual single-energy images and virtual images without iodinated contrast or without calcium, as well as to separate materials such as uric acid or fat and to elaborate hepatic iron overload maps. In this article, we review some of the clinical benefits and technical limitations to improve understanding of dual-energy CT and expand its use in clinical practice.

Comparison of Four Dual-Energy CT Scanner Technologies for Determining Renal Stone Composition: A Phantom Approach

Background Studies have investigated the value of various dual-energy CT (DECT) technologies for determining renal stone composition. However, sparse multivendor comparison data exist. Purpose To compare the performance of four DECT technologies in determining renal stone composition at standard- and low-dose acquisitions. Materials and Methods This was an in vitro phantom study. Seventy-one urinary stones (size: 2.7-14.1 mm) of known chemical composition (51 calcium, four struvite, four cystine, and 12 urate) were placed in a custom-made cylindrical phantom. Consecutive scans with manufacturer-recommended protocols and dose-optimized institutional protocols (up to 80% reduction in volumetric CT dose index) were obtained with rapid kilovolt peak switching DECT (rsDECT) (n = 2), dual-source DECT (n = 2), twin-beam DECT (tbDECT) (n = 1), and dual-layer detector-based CT (dlDECT) (n = 1) scanners. The image data sets were analyzed using effective atomic number and dual-energy ratio indexes of maximally available and comparable spectra. The performance of each combination of scanner technology, method, and acquisition was assessed. Logistic regression models were used to calculate the area under the receiver operating characteristic curve (AUC). Results After image analysis, all scanners except tbDECT had an AUC greater than 0.95 in at least one acquisition in distinguishing urate from other stones. All DECT techniques were able to help differentiate calcium oxalate monohydrate stones with moderate accuracy (AUC: 0.70-0.83), and brushite was differentiated from urate with AUC greater than 0.99. There was no correlation between performance and acquisition with dose-optimized and/or vendor-recommended settings. Conclusion All four dual-energy CT (DECT) technologies enabled accurate determination of stone composition at standard- and low-dose acquisitions; however, performance varied based on the scanner parameters, DECT technique, and stone type. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Ringl and Apfaltrer in this issue.

Prediction of the Uric Acid Component in Nephrolithiasis Using Simple Clinical Information about Metabolic Disorder and Obesity: A Machine Learning-Based Model

There is a great need for a diagnostic tool using simple clinical information collected from patients to diagnose uric acid (UA) stones in nephrolithiasis. We built a predictive model making use of machine learning (ML) methodologies entering simple parameters easily obtained at the initial clinical visit. Socio-demographic, health, and clinical data from two cohorts (A and B), both diagnosed with nephrolithiasis, one between 2012 and 2016 and the other between June and December 2020, were collected before nephrolithiasis treatment. A ML-based model for predicting UA stones in nephrolithiasis was developed using eight simple parameters-sex, age, gout, diabetes mellitus, body mass index, estimated glomerular filtration rate, bacteriuria, and urine pH. Data from Cohort A were used for model training and validation (ratio 3:2), while data from Cohort B were used only for validation. One hundred and forty-six (13.3%) out of 1098 patients in Cohort A and 3 (4.23%) out of 71 patients in Cohort B had pure UA stones. For Cohort A, our model achieved a validation AUC (area under ROC curve) of 0.842, with 0.8475 sensitivity and 0.748 specificity. For Cohort B, our model achieved 0.936 AUC, with 1.0 sensitivity, and 0.912 specificity. This ML-based model provides a convenient and reliable method for diagnosing urolithiasis. Using only eight readily available clinical parameters, including information about metabolic disorder and obesity, it distinguished pure uric acid stones from other stones before treatment.

Virtual monoenergetic images from dual-energy CT: systematic assessment of task-based image quality performance

Background

To compare task-based image quality (TB-IQ) among virtual monoenergetic images (VMI) and linear-blended images (LBI) from dual-energy CT as a function of contrast task, radiation dose, size, and lesion diameter.

Methods

A TB-IQ phantom (Mercury Phantom 4.0, Sun Nuclear Corporation) was imaged on a third-generation dual-source dual-energy CT with 100/Sn150 kVp at three volume CT dose levels (5, 10, 15 mGy). Three size sections (diameters 16, 26, 36 cm) with subsections for image noise and spatial resolution analysis were used. High-contrast tasks (e.g., calcium-containing stone and vascular lesion) were emulated using bone and iodine inserts. A low-contrast task (e.g., low-contrast lesion or hematoma) was emulated using a polystyrene insert. VMI at 40-190 keV and LBI were reconstructed. Noise power spectrum (NPS) determined the noise magnitude and texture. Spatial resolution was assessed using the task-transfer function (TTF) of the three inserts. The detectability index (d') served as TB-IQ metric.

Results

Noise magnitude increased with increasing phantom size, decreasing dose, and decreasing VMI-energy. Overall, noise magnitude was higher for VMI at 40-60 keV compared to LBI (range of noise increase, 3-124%). Blotchier noise texture was found for low and high VMIs (40-60 keV, 130-190 keV) compared to LBI. No difference in spatial resolution was observed for high contrast tasks. d' increased with increasing dose level or lesion diameter and decreasing size. For high-contrast tasks, d' was higher at 40-80 keV and lower at high VMIs. For the low-contrast task, d' was higher for VMI at 70-90 keV and lower at 40-60 keV.

Conclusions

Task-based image quality differed among VMI-energy and LBI dependent on the contrast task, dose level, phantom size, and lesion diameter. Image quality could be optimized by tailoring VMI-energy to the contrast task. Considering the clinical relevance of iodine, VMIs at 50-60 keV could be proposed as an alternative to LBI.

Urate Crystals; Beyond Joints

Gout is the most common inflammatory arthropathy caused by the deposition of monosodium urate (MSU) crystals. The burden of gout is substantial with increasing prevalence of gout globally. The prevalence of Gout in the United States has increased by over 7% in the last two decades. Initially, it was believed that MSU crystal deposits occur only in the joints with the involvement of the periarticular soft tissues, but recent studies have shown the presence of MSU crystal deposition in extra-articular sites as well. Human plasma becomes supersaturated with uric acid at 6.8 mg/dl, a state called hyperuricemia. Beyond this level, uric acid crystals precipitate out of the plasma and deposit in soft tissues, joints, kidneys, etc. If left untreated, hyperuricemia leads to chronic gout characterized by the deposition of tophi in soft tissues such as the joints, tendons, and bursae. With the advent of newer imaging techniques such as DECT, MSU crystals can be visualized in various extra-articular sites. Extra-articular deposition of MSU crystals is believed to be the causative factor for the development of multiple comorbidities in gout patients. Here, we review the literature on extra-articular deposition of urate crystals and the role of dual-energy computed tomography (DECT) in elucidating multi-organ involvement. DECT has emerged as an invaluable alternative for accurate and efficient MSU crystal deposition detection. Future studies using DECT can help determine the clinical consequences of extra-articular deposition of MSU in gout patients.

Low-dose dual-energy CT for stone characterization: a systematic comparison of two generations of split-filter single-source and dual-source dual-energy CT

PURPOSE

To compare noise texture and accuracy to differentiate uric acid from non-uric acid urinary stones among four different single-source and dual-source DECT approaches in an ex vivo phantom study.

METHODS

Thirty-two urinary stones embedded in gelatin were mounted on a Styrofoam disk and placed into a water-filled phantom. The phantom was imaged using four different DECT approaches: (A) dual-source DECT (DS-DE); (B) 1st generation split-filter single-source DECT (SF1-TB); (C) 2nd generation split-filter single-source DECT (SF2-TB) and (D) 2nd generation split-filter single-source DECT using serial acquisitions (SF2-TS). Two different radiation doses (3 mGy and 6 mGy) were used. Noise texture was compared by assessing the average spatial frequency (f_av) of the normalized noise power spectrum (nNPS). ROC curves for stone classification were computed and the accuracy for different dual-energy ratio cutoffs was derived.

RESULTS

NNPS demonstrated comparable noise texture among A, C, and D (f_av-range 0.18-0.19) but finer noise texture for B (f_av = 0.27). Stone classification showed an accuracy of 96.9%, 96.9%, 93.8%, 93.8% for A, B, C, D for low-dose, respectively, and 100%, 96.9%, 96.9%, 100% for routine dose. The vendor-specified cutoff for the dual-energy ratio was optimal except for the low-dose scan in D for which the accuracy was improved from 93.8 to 100% using an optimized cutoff.

CONCLUSION

Accuracy to differentiate uric acid from non-uric acid stones was high among four single-source and dual-source DECT approaches for low- and routine dose DECT scans. Noise texture differed only slightly for the first-generation split-filter approach.

Single-energy CT predicts uric acid stones with accuracy comparable to dual-energy CT-prospective validation of a quantitative method

OBJECTIVES

To prospectively validate three quantitative single-energy CT (SE-CT) methods for classifying uric acid (UA) and non-uric acid (non-UA) stones.

METHODS

Between 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.

RESULTS

Altogether 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.

CONCLUSION

A 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.

Performance of Dual-Source CT in Calculi Component Analysis: A Systematic Review and Meta-Analysis of 2151 Calculi

Objective:

To evaluate the performance of dual-source computed tomography (DSCT) in the component analysis of all types of calculi by doing a systematic review and meta-analysis.

Methods:

We searched MEDLINE, Embase, Scopus, and CNKI up to February 28, 2020, for in vivo studies investigating the performance of DSCT in the component analysis of calculi. We pooled the sensitivity, specificity, and areas under the summary receiver operating characteristic (AUROC) curves using a random-effect model in the meta-analysis. Publication bias was evaluated using Deek’s funnel plot asymmetry test.

Results:

This analysis included a total of 37 studies in 1840 patients with 2151 calculi (462 uric acid [UA], 1383 calcium oxalate [CaOx], 55 cystine [Cys], 197 hydroxyapatite [HA], and 54 struvite [SV]). Using DSCT, the pooled accuracy for diagnosing UA (sensitivity, 0.95; specificity, 0.99), CaOx (0.98; 0.93), Cys (0.99; 0.99), HA (0.91; 0.99), and SV (0.42; 0.98) was calculated, respectively. The AUROC value was 0.99, 0.99, 1.00, 0.99, and 0.93, respectively. The P values for publication bias test were .49, .70, .07, .04, and .19, respectively.

Conclusion:

Dual-source computed tomography has high sensitivity and specificity for the component analysis of UA, CaOx, Cys, and HA calculi in vivo. This tool may have the potential to replace the current analysis tool in vitro in diagnosing calculi.

Non-Destructive Imaging and Spectroscopic Techniques for Assessment of Carcass and Meat Quality in Sheep and Goats: A Review

In the last decade, there has been a significant development in rapid, non-destructive and non-invasive techniques to evaluate carcass composition and meat quality of meat species. This article aims to review the recent technological advances of non-destructive and non-invasive techniques to provide objective data to evaluate carcass composition and quality traits of sheep and goat meat. We highlight imaging and spectroscopy techniques and practical aspects, such as accuracy, reliability, cost, portability, speed and ease of use. For the imaging techniques, recent improvements in the use of dual-energy X-ray absorptiometry, computed tomography and magnetic resonance imaging to assess sheep and goat carcass and meat quality will be addressed. Optical technologies are gaining importance for monitoring and evaluating the quality and safety of carcasses and meat and, among them, those that deserve more attention are visible and infrared reflectance spectroscopy, hyperspectral imagery and Raman spectroscopy. In this work, advances in research involving these techniques in their application to sheep and goats are presented and discussed. In recent years, there has been substantial investment and research in fast, non-destructive and easy-to-use technology to raise the standards of quality and food safety in all stages of sheep and goat meat production.

Dual-Energy Computed Tomography for Stone Type Assessment: A Pilot Study of Dual-Energy Computed Tomography with Five Indices

Purpose: To assess the efficacy of dual-energy CT (DECT) in predicting the composition of urinary stones with a single index (dual energy ratio [DER]) and five indices. Methods: Patients undergoing DECT before active urolithiasis treatment were prospectively enrolled in the study. Predictions of stone composition were made based on discriminant analysis with a single index (DER) and five indices (stone density at 80 and 135 kV, Zeff [the effective atomic number of the absorbent material] of the stone, DER, dual-energy index [DEI] and dual-energy difference [DED]). After extraction, stone composition was evaluated by means of physicochemical analyses (X-ray phase analysis, electron microscopy, wet chemistry techniques, and infrared spectroscopy). Results: A total of 91 patients were included. For calcium oxalate monohydrate (COM) stones, the sensitivity, specificity, and overall accuracy of DECT with one index (DER) were 83.3%, 89.8%, and 86.8%, respectively; for calcium oxalate dihydrate (COD) and calcium phosphate stones-88.2%, 92.9%, and 91.2%, respectively; for uric acid stones-0%, 98.8% and 97.8%, respectively; for struvite stones-60%, 95.3%, and 93.4%, respectively. Discriminant analysis with five indices yielded the following sensitivity, specificity, and overall accuracy: 95.2%, 89.8%, and 92.3% for COM stones, 85.3%, 96.4%, and 92.3% for COD stones, and 100% in all three categories for both uric acid and struvite stones. Conclusions: DECT is a promising tool for stone composition assessment. It allowed for evaluation of chemical composition of all stone types with specificity and accuracy ranging from 85% to 100%. Five DECT indices have shown much better diagnostic accuracy compared to a single DECT index.