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Objective: Painless visible haematuria (VH) necessitates a computed tomography (CT) usually consisting of one unenhanced and two to three contrast enhanced acquisitions to detect urinary tract stones and malignancy. Recently, we demonstrated that a single nephrographic phase (NP) CT sufficed in detecting malignancy in patients with painless VH. Now, we aim to evaluate the diagnostic performance of single NP CT in stone detection and size measurements in the same cohort., Material and Methods: "A Prospective Trial for Examining Haematuria using Computed Tomography" (PROTEHCT) was a single-center prospective diagnostic study in patients with painless VH between September 2019 and June 2021. All underwent four-phase CT (reference standard) from which a single NP CT (experimental) was extracted. Two randomised readers independently assessed the experimental CT for urinary stones and size. Statistical analysis included diagnostic accuracies and inter-reader agreement (kappa) of experimental CT, and size correlation (Spearman's ρ) between experimental CT and reference standard., Results: In 308 included patients (median age: 68 years, 250 males), urinary stones (median size 5 mm) were diagnosed in 21%. The per-patient experimental CT sensitivity was 86% (97% for stones ≥ 5 mm), specificity was 98% and accuracy was 96%. The experimental CT sensitivity for detecting kidney stones was 78% (89% for stones ≥ 5 mm), and 100% for bladder and ureteral stones. No missed stone required active treatment. The inter-reader agreement was almost perfect (96%, k = 0.85). The correlation in stone size was very strong (ρ = 0.91).  Conclusions: A single NP CT is sufficient in detecting and measuring urinary stones in patients with painless VH.

Purpose: This study aimed to establish a CT imaging grading system and explore its value in evaluating upper urinary tract calculi associated with kidney infections., Methods: CT images of 126 patients with kidney infections caused by upper urinary tract calculi were retrospectively analyzed. The CT grading system was developed based on CT images. CT images were classified into 4 grades. General information, symptoms, and clinical findings of patients in different CT grades were analyzed. With the occurrence of systemic inflammatory response syndrome (SIRS) as the endpoint, univariate and multivariate analysis was conducted to analyze the risk factors of SIRS., Results: Patients with fever or diabetes had higher CT grades, and the following examination data revealed significant differences across the various CT grades ( P < 0.05): the white blood cell count, urine leucocytes count, CT1, CT2, maximum body temperature, duration of disease, the proportion of blood neutrophils, the size of stones, and levels of the C-reactive protein and procalcitonin. Only CT grading was statistically significant after multivariate analysis. According to the values of the partial regression coefficient (B), the higher the CT grade, the greater the risk of SIRS. The risk of SIRS was 4.472 times higher with each increment of the CT grade., Conclusions: The CT grade is directly associated with clinical symptoms and the risk of SIRS.

Objectives: To perform a systematic review on artificial intelligence (AI) performances to detect urinary stones. Methods: A PROSPERO-registered (CRD473152) systematic search of Scopus, Web of Science, Embase, and PubMed databases was performed to identify original research articles pertaining to AI stone detection or measurement, using search terms ("automatic" OR "machine learning" OR "convolutional neural network" OR "artificial intelligence" OR "detection" AND "stone volume"). Risk-of-bias (RoB) assessment was performed according to the Cochrane RoB tool, the Joanna Briggs Institute Checklist for nonrandomized studies, and the Checklist for Artificial Intelligence in Medical Imaging (CLAIM). Results: Twelve studies were selected for the final review, including three multicenter and nine single-center retrospective studies. Eleven studies completed at least 50% of the CLAIM checkpoints and only one presented a high RoB. All included studies aimed to detect kidney (5/12, 42%), ureter (2/12, 16%), or urinary (5/12, 42%) stones on noncontrast computed tomography (NCCT), but 42% intended to automate measurement. Stone distinction from vascular calcification interested two studies. All studies used AI machine learning network training and internal validation, but a single one provided an external validation. Trained networks achieved stone detection, with sensitivity, specificity, and accuracy rates ranging from 58.7% to 100%, 68.5% to 100%, and 63% to 99.95%, respectively. Detection Dice score ranged from 83% to 97%. A high correlation between manual and automated stone volume ( r  = 0.95) was noted. Differentiate distal ureteral stones and phleboliths seemed feasible. Conclusions: AI processes can achieve automated urinary stone detection from NCCT. Further studies should provide urinary stone detection coupled with phlebolith distinction and an external validation, and include anatomical abnormalities and urologic foreign bodies (ureteral stent and nephrostomy tubes) cases.

Rationale: Extracorporeal shock wave lithotripsy (ESWL) is widely considered the primary approach for managing urinary tract stones. This study aimed to assess the predictive factors associated with non-contrast computed tomography (NCCT)-based parameters of upper urinary stones in relation to the outcomes of ESWL., Materials and Methods: A systematic search was conducted in PubMed, ScienceDirect, Web of Science, and Cochrane Library to identify all relevant studies published up to June 3, 2023. Several NCCT-based parameters to predict ESWL outcomes, comprised of mean stone density (MSD), skin-to-stone distance (SSD), and stone size, were extracted and analyzed using Review Manager software., Results: Out of 979 publications screened, a total of 39 publications, involving 7869 patients, were enrolled in the analysis. The pooled estimate demonstrated significant differences between MSD, and stone size between successful and failure of stone fragmentation groups, in which lower values of these parameters are associated with successful ESWL outcomes., Conclusion: The results from the current study suggested that lower NCCT parameters, notably MSD, SSD, and stone size, are significantly associated with successful ESWL outcome. However, additional large-scale prospective studies are required to utilize these parameters effectively, and the optimal cutoff value should be determined., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Background Contrast-unenhanced abdominal CT is the imaging standard for urinary calculi detection; however, studies comparing photon-counting detector (PCD) CT and energy-integrating detector (EID) CT dose-reduction potentials are lacking. Purpose To compare the radiation dose and image quality of optimized EID CT with those of an experimental PCD CT scan protocol including tin prefiltration in patients suspected of having urinary calculi. Materials and Methods This retrospective single-center study included patients who underwent unenhanced abdominal PCD CT or EID CT for suspected urinary caliculi between February 2022 and March 2023. Signal and noise measurements were performed at three anatomic levels (kidney, psoas, and obturator muscle). Nephrolithiasis and/or urolithiasis presence was independently assessed by three radiologists, and diagnostic confidence was recorded on a five-point scale (1, little to no confidence; 5, complete confidence). Reader agreement was determined by calculating Krippendorff α. Results A total of 507 patients (mean age, 51.7 years ± 17.4 [SD]; 317 male patients) were included (PCD CT group, 229 patients; EID CT group, 278 patients). Readers 1, 2, and 3 detected nephrolithiasis in 129, 127, and 129 patients and 94, 94, and 94 patients, whereas the readers detected urolithiasis in 113, 114, and 114 patients and 152, 153, and 152 patients in the PCD CT and EID CT groups, respectively. Regardless of protocol (PCD CT or EID CT) or calculus localization, near perfect interreader agreement was found (α ≥ 0.99; 95% CI: 0.99, 1). There was no evidence of a difference in reader confidence between PCD CT and EID CT (median confidence, 5; IQR, 5-5; P ≥ .57). The effective doses were 0.79 mSv (IQR, 0.63-0.99 mSv) and 1.39 mSv (IQR, 1.01-1.87 mSv) for PCD CT and EID CT, respectively. Despite the lower radiation exposure, the signal-to-noise ratios at the kidney, psoas, and obturator levels were 30%, 23%, and 17% higher, respectively, in the PCD CT group ( P < .001). Conclusion Submillisievert abdominal PCD CT provided high-quality images for the diagnosis of urinary calculi; radiation exposure was reduced by 44% with a higher signal-to-noise ratio than with EID CT and with no evidence of a difference in reader confidence. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Nezami and Malayeri in this issue.

Purpose: Urolithiasis is a chronic condition that leads to repeated CT scans throughout the patient's life. The goal was to assess the diagnostic performance and image quality of submillisievert abdominopelvic computed tomography (CT) using deep learning-based image reconstruction (DLIR) in urolithiasis., Methods: 57 patients with suspected urolithiasis underwent both non-contrast low-dose (LD) and ULD abdominopelvic CT. Raw image data of ULD CT were reconstructed using hybrid iterative reconstruction (ASIR-V 70%) and high-strength-level DLIR (DLIR-H). The performance of ULD CT for the detection of urinary stones was assessed by two readers and compared with LD CT with ASIR-V 70% as a reference standard. Image quality was assessed subjectively and objectively., Results: 266 stones were detected in 38 patients. Mean effective dose was 0.59 mSv for ULD CT and 1.96 mSv for LD CT. For diagnostic performance, sensitivity and specificity were 89% and 94%, respectively, for ULDCT with DLIR-H. There was an almost perfect intra-observer concordance on ULD CT with DLIR-H versus LDCT with ASIR-V 70% (ICC = 0.90 and 0.90 for the two readers). Image noise was significantly lower and signal-to-noise ratio significantly higher with DLIR-H compared to ASIR-V 70%. Subjective image quality was also significantly better with ULDCT with DLIR-H., Conclusion: ULD CT with Deep Learning Image Reconstruction maintains a good diagnostic performance in urolithiasis, with better image quality than hybrid iterative reconstruction and a significant radiation dose reduction., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

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 ., (Copyright © 2024 The Chinese Medical Association, produced by Wolters Kluwer, Inc. under the CC-BY-NC-ND license.)

Purpose: Oral chemolysis is an effective and non-invasive treatment for uric acid urinary stones. This study aimed to classify urinary stones into either pure uric acid (pUA) or other composition (Others) using non-contrast-enhanced computed tomography scans (NCCTs)., Methods: Instances managed at our institution from 2019 to 2021 were screened. They were labeled as either pUA or Others based upon composition analyses, and randomly split into training or testing data set. Several instances contained multiple NCCTs which were all collected. In each of NCCTs, individual urinary stone was treated as individual sample. From manually drawn volumes of interest, we extracted original and wavelet radiomics features for each sample. The most important features were then selected via the Least Absolute Shrinkage and Selection Operator for building the final model on a Support Vector Machine. Performance on the testing set was evaluated via accuracy, sensitivity, specificity, and area under the precision-recall curve (AUPRC)., Results: There were 302 instances, of which 118 had pUA urinary stones, generating 576 samples in total. From 851 original and wavelet radiomics features extracted for each sample, 10 most important features were ultimately selected. On the testing data set, accuracy, sensitivity, specificity, and AUPRC were 93.9%, 97.9%, 92.2%, and 0.958, respectively, for per-sample prediction, and 90.8%, 100%, 87.5%, and 0.902, respectively, for per-instance prediction., Conclusion: The machine learning algorithm trained with radiomics features from NCCTs can accurately predict pUA urinary stones. Our work suggests a potential assisting tool for stone disease treatment selection., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Objective: The aim of the study was to characterize artificial stones used for research in endourology in terms of radiological properties and hardness, based on stone fragmentation, and to compare them with real stones. Materials and Methods: We built artificial stones using BegoStone Plus™ powder (BEGO, Lincoln, RI), with powder (g)-water (mL) ratios ranging from 15:03 to 15:12. The CT Gemstone Spectral Imaging Software ® (GE Medical Systems, LLC, Waukesha, WI) was used to evaluate the radiological density in HU and spectral properties. Stone fragmentation was assessed in an in vitro experimental setting. These properties of artificial stones were compared with real urinary calculi. Results: Regarding radiological density in terms of HUs, 15:03 artificial calculi showed similar results when compared with real stones comprising calcium oxalate and calcium phosphate. The 15:03 and 15:04 artificial stones showed similar spectral property results to calcium pyrophosphate stones. The 15:11 artificial stones showed similar stone fragmentation results to real stones comprising uric acid, and 15:03 artificial calculi showed similar results to apatite and cystine stones. Conclusions: Artificial stones are useful for research in endourology. Stones with a powder (g)-water (mL) ratio of 15:03 proved to mimic real hard stones in terms of HUs, atomic number, and stone fragmentation in our study and could be used as artificial hard stones, and 15:11 stones showed similar stone fragmentation to uric acid stones. Our study might suggest that standard Bego stones are useful to investigate different areas in endourology, but not radiological properties because radiological homogeneity is not ensured unless more sophisticated mixing methods are used.

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 (Z eff ), 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 Z eff images to determine stone composition (reference standard = crystallography) and image quality. The objective analysis included image noise and Z eff 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 Z eff 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 Z eff cutoff of 7.66 accurately distinguished uric acid from non-uric acid stones (AUC = 1.00). Z eff 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 Z eff images allows for better tailored treatment strategies, helping to choose appropriate interventions and prevent potential complications related to urinary stones in patient care., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

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&#95;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.

Objective: Dual-energy computed tomography (DECT) imaging technology opens a new idea and method for analyzing stone composition, which can obtain several quantitative parameters reflecting tissue-related information and energy images different from traditional images. However, the application of DECT in diagnosing urinary calculi remains unknown. This study aims to evaluate the value of DECT in diagnosing urinary calculi by meta-analysis., Methods: PubMed, EMBASE, Web of Science, and the Cochrane Library were searched to articles published from the establishment of the databases to April 18, 2023. We reviewed the articles on the diagnosis of urinary calculi detected by DECT, established standards, screened the articles, and extracted data. Two researchers carried out data extraction and the Cohen's unweighted kappa was estimated for inter-investigator reliability. The quality of the literature was evaluated by the diagnostic test accuracy quality evaluation tool (QUADAS-2). The heterogeneity and threshold effects were analyzed by Meta-Disc 1.4 software, and the combined sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic ratio were calculated. The combined receiver-operating characteristic (ROC) curve was drawn, and the value of DECT in the diagnosis of urinary calculi was evaluated by the area under the curve (AUC). The meta-analysis was registered at PROSPERO (CRD42023418204)., Results: One thousand and twenty-seven stones were detected in 1,223 samples from 10 diagnostic tests. The analyzed kappa alternated between 0.78-0.85 for the document's retrieval and detection procedure. The sensitivity of DECT in the diagnosis of urinary calculi was 0.94 (95% CI [0.92-0.96]). The positive likelihood ratio (PLR) of DECT in the diagnosis of urinary stones was 0.91 (95% CI [0.88-0.94]), and the negative likelihood ratio (NLR) was 0.08 (95% CI [0.05-0.11]). The specificity of DECT for detecting urinary calculi was 0.91 (95% CI [0.88-0.94]). The area under the curve of the summary receiver operator characteristic (SROC) was 0.9875. The sensitivity of dual-energy CT in the diagnosis of urinary calculi diameter <3 mm was 0.94 (95% CI [0.91-0.96]). The PLR of DECT in the diagnosis of urinary stones diameter <3 mm was 10.79 (95% CI [5.25 to 22.17]), and the NLR was 0.08 (95% CI [0.05-0.13]). The specificity of DECT for detecting urinary calculi <3 mm was 0.91 (95% CI [0.87-0.94]). The SROC was 0.9772., Conclusion: The DECT has noble application value in detecting urinary calculi., Competing Interests: The authors declare there are no competing interests., (©2023 Feng et al.)

Urinary stones are common urological diseases with increasing prevalence and incidence worldwide. Among the various types of stones, uric acid stones can be dissolved by oral chemolysis without any surgical procedure. Therefore, our study demonstrates that variant coefficient of stone density measured by thresholding a three-dimensional segmentation-based method from noncontrast computed tomography images can be used to identify pure uric acid stones from non-pure uric acid stones. This study provides a preoperative pure uric acid stone prediction model that could reduce invasive procedural treatments. The pure uric acid stone prediction model may offer optimized clinical decision-making for patients with urinary stones., Background and Objectives: While most urinary stones are managed with interventional therapy, uric acid (UA) stones can be dissolved by oral chemolysis without invasive procedures. This study aimed to develop and validate a pure UA (pUA) stone prediction model using a variant coefficient of stone density (VCSD) measured by thresholding a three-dimensional (3D) segmentation-based method., Methods: Patients with urolithiasis treated at Keimyung University Dongsan Hospital between January 2017 and December 2020 were divided into training and internal validation sets, and patients from Kyungpook National University Hospital between January 2017 and December 2018 were used as an external validation set. Each stone was segmented by a thresholding 3D segmentation-based method using an attenuation threshold of 130 Hounsfield units. VCSD was calculated as the stone heterogeneity index divided by the mean stone density., Results: A total of 1175 urinary stone cases in 1023 patients were enrolled in this study. Of these, 224 (19.1%) were pUA stone cases. Among the potential predictors, thresholding 3D segmentation-based VCSD, age, sex, radio-opacity, hypertension, diabetes, and urine pH were identified as independent pUA stone predictors, and VCSD was the most powerful indicator. The pUA stone prediction model showed good discrimination, yielding area under the receiver operating characteristic curve of 0.960 (95% confidence interval (CI): 0.940-0.979, P < 0.001), 0.931 (95% CI: 0.875-0.987, P < 0.001), and 0.938 (95% CI: 0.912-0.965, P < 0.001) in the training, internal validation, and external validation sets, respectively., Conclusions: VCSD measured using 3D segmentation was a decisive independent predictive factor for pUA stones. Furthermore, the established prediction model with VCSD can serve as a noninvasive preoperative tool to identify pUA stones., Competing Interests: Declaration of Competing Interest All the authors declare no competing interests., (Copyright © 2023. Published by Elsevier B.V.)

Abdominal radiography is an important diagnostic to detect uroliths. Cystine and urate uroliths were historically characterized as nonmineral opaque on survey radiographs. However, recent research and clinical observations indicate that pure urate and cystine uroliths may be detected with digital radiography. The primary purpose of this prospective, in vitro, diagnostic accuracy study was to determine the sensitivity of digital radiography in detecting cystoliths of varying size and composition. Forty canine uroliths of pure composition (10 each of calcium oxalate, struvite, cystine, and urate), acquired from Minnesota Urolith Center and ranging from 1 to 10 mm, were placed in phantoms of three various sizes and radiographed. The radiographs, including three sets of each urolith separately, were evaluated by three blinded radiologists on two separate occasions. Evaluation included presence or absence of urolith, number of uroliths, and maximum diameter of the urolith(s). For all four types of uroliths and all readers, the specificity and PPV were 100% with an associated very high sensitivity (94.4%-98.9%) and NPV (94.8%-98.9%). Calcium oxalate uroliths were the most accurately measured and struvite were the least accurately measured when compared with the gross measurement. Smaller uroliths were more accurately measured than larger uroliths. Uroliths placed in smaller bladder phantoms were more accurately measured than in larger bladder phantoms. Though accurate measurement of uroliths is complicated by and dependent on numerous variables, our results reveal that urate and cystine uroliths are visualized on digital radiography making them a relevant differential diagnosis when seen clinically., (© 2023 American College of Veterinary Radiology.)

Objective: The correlation of the degree of hydronephrosis and computed tomography (CT) value of calculi with the efficacy of ureteroscopic lithotripsy (URSL) in patients with upper urinary tract infectious calculi was explored., Methods: The clinical data of 152 patients with upper urinary tract infectious calculi and on URSL in Shanghai Baoshan District Wusong Central Hospital from November 2019 to November 2021 were collected for retrospective analysis. All patients received CT examination before surgery. According to the therapeutic effect of URSL, all patients were divided into the non-calculi group (NCG, n = 101) and residual calculi group (RCG, n = 51), which were compared in terms of the degree of hydronephrosis and CT value of calculi. Then, the correlation of the degree of hydronephrosis and CT value of calculi with the efficacy of URSL in patients was analysed., Results: No significant difference in clinical data was found between the groups ( p > 0.05). Patients in the NCG group had lower degree of hydronephrosis than those in the RCG group ( p < 0.05), and the NCG had lower CT value of calculi ( p < 0.001). Spearman rank correlation analysis showed that the degree of hydronephrosis in patients with upper urinary tract infectious calculi was negatively correlated with the efficacy of URSL (r = -0.676, p < 0.001), and the CT value of calculi in such patients was negatively correlated with the efficacy of URSL (r = -0.795, p < 0.001)., Conclusions: The degree of hydronephrosis and CT value of calculi were negatively correlated with the efficacy of URSL. Both can be used to predict clinical efficacy and have clinical guiding value for the formulation of treatment plans in patients with urinary tract infectious calculi., Competing Interests: The authors declare no conflict of interest., (© 2023 The Author(s).)

Computed tomography (CT) is used to diagnose urolithiasis, a prevalent condition. In order to establish the strongest foundation for the quantifiability of urolithiasis, this study aims to develop semi-automated urolithiasis segmentation methods for CT images that differ in terms of surface-partial-volume correction and adaptive thresholding. It also examines the diagnostic accuracy of these methods in terms of volume and maximum stone diameter. One hundred and one uroliths were positioned in an anthropomorphic phantom and prospectively examined in CT. Four different segmentation methods were developed and used to segment the uroliths semi-automatically based on CT images. Volume and maximum diameter were calculated from the segmentations. Volume and maximum diameter of the uroliths were measured independently by three urologists by means of electronic calipers. The average value of the urologists´ measurements was used as a reference standard. Statistical analysis was performed with multivariate Bartlett's test. Volume and maximum diameter were in very good agreement with the reference measurements (r>0.99) and the diagnostic accuracy of all segmentation methods used was very high. Regarding the diagnostic accuracy no difference could be detected between the different segmentation methods tested (p>0.55). All four segmentation methods allow for accurate characterization of urolithiasis in CT with respect to volume and maximum diameter of uroliths. Thus, a simple thresholding approach with an absolute value may suffice for robust determination of volume and maximum diameter in urolithiasis., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Neubauer et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Objectives: For detection of urinary calculi, unenhanced low-dose computed tomography is the method of choice, outperforming radiography and ultrasound. This retrospective monocentric study aims to compare a clinically established, dedicated low-dose imaging protocol for detection of urinary calculi with an ultra-low-dose protocol employing tin prefiltration at a standardized tube voltage of 100 kVp., Methods: Two study arms included a total of 510 cases. The "low-dose group" was comprised of 290 individuals (96 women; age 49 ± 16 years; BMI 27.23 ± 5.60 kg/m 2 ). The "ultra-low-dose group" with Sn100 kVp consisted of 220 patients (84 women; age 47 ± 17 years; BMI 26.82 ± 5.62 kg/m 2 ). No significant difference was ascertained for comparison of age (p = 0.132) and BMI (p = 0.207) between cohorts. For quantitative assessment of image quality, image noise was assessed., Results: No significant difference regarding frequency of calculi detection was found between groups (p = 0.596). Compared to the low-dose protocol (3.08 mSv; IQR 2.22-4.02 mSv), effective dose was reduced by 62.35% with the ultra-low-dose protocol employing spectral shaping (1.16 mSv; IQR 0.89-1.54 mSv). Image noise was calculated at 18.90 (IQR 17.39-21.20) for the low-dose protocol and at 18.69 (IQR 17.30-21.62) for the ultra-low-dose spectral shaping protocol. No significant difference was ascertained for comparison between groups (p = 0.793)., Conclusion: For urinary calculi detection, ultra-low-dose scans utilizing spectral shaping by means of tin prefiltration at 100 kVp allow for considerable dose reduction of up to 62% over conventional low-dose CT without compromising image quality., (Copyright © 2022 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Background: Noncontrast CT (NCCT) relies on labor-intensive examinations of CT slices to identify urolithiasis in the urinary tract, and, despite the use of deep-learning algorithms, false positives remain. Materials and Methods: A total of 410 NCCT axial scans from patients undergoing surgical treatment for urolithiasis were used for model development. The deep learning model was customized to combine a urolithiasis segmentation with per-slice classification for screening. Prediction models of the axial, coronal, and sagittal views were trained, and an additive model with an intersection of the coronal and sagittal predictions added to the axial outcome was introduced. Automated quantification of clinical metrics was evaluated in three-dimensional models of urinary stones. Results: The axial model detected 88.92% of urinary stones and produced a dice similarity coefficient of 87.56% in the urolithiasis segmentation. For urolithiasis (>5 mm), the sensitivity of the axial model reached 95.10%. False positives were reduced to 0.34 per patient using an ensemble of individual models. The additive model improved the sensitivity to 90.97% by detecting more small urolithiasis (<5 mm). All clinical metrics of size, long-axis diameter, volume, mean stone density, stone heterogeneity index, and skin-to-stone distance showed a strong correlation of R 2  > 0.964. Conclusions: The proposed system could reduce the burden on the physician for imaging diagnosis and help determine treatment strategies for urinary stones through automated quantification of clinical metrics with high accuracy and reproducibility.

To investigate the optimal scanning parameters of dual-energy computed tomography (DECT), which can accurately determine sensitivity (the detectability of urinary stones) and accuracy (the composition matching of urinary stones), and to apply them to clinical trials. Fifteen urinary stones were chemically analyzed, and their chemical compositions were considered a reference standard with which we compared the uric acid (UA) and non-UA compositions determined using DECT. The urinary stones were placed inside a bolus and scanned with a dual-source CT scanner under various selected dual-energy conditions (A to X) using various solid water phantom thicknesses. These datasets were analyzed using the Siemens syngo.via software tool (integrated into the CT system) for matching the sensitivity and accuracy assessments. This study showed that 80% of the highest sensitivity (detection of urinary stones) and 92% of the highest accuracy (composition matching of urinary stones) were achieved under condition A (a collimation beam width setting of 2 × 32 × 0.6 mm, an automatic exposure control setting of 80/sn140 peak kilovoltage, and a slice thickness of 0.5/0.5 mm) (P < 0.05). Application of the DECT energy parameters presented in the study will help identify the sensitivity and accuracy of UA and non-UA stone analysis, even in patients with small-sized urinary stones and in conditions difficult for analysis., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Objectives: Spectral shaping via tin prefiltration has gained recognition for dose saving in high-contrast imaging tasks. The aim of this phantom dosimetry study was to investigate whether the use of tin filters can also reduce the effective radiation dose in 100 kVp abdominal computed tomography (CT) compared with standard low-dose scans for suspected urolithiasis., Methods: Using a third-generation dual-source CT scanner, 4 scan protocols each were used on a standard (P1-P4) and a modified obese Alderson-Rando phantom (P5-P8), in which 11 urinary stones of different compositions were placed. Hereby 1 scan protocol represented standard low-dose settings (P1/P5: 110 kVp/120 kVp), whereas 3 experimental protocols used low-kilovoltage spectral shaping (P2/P3/P4 and P6/P7/P8: 100 kVp with tin prefiltration). Radiation dose was recorded by thermoluminescent dosimeters at 24 measurement sites. For objective assessment of image quality, dose-weighted contrast-to-noise ratios were calculated and compared between scan protocols. Additional subjective image quality analysis was performed by 2 radiologists using equidistant 5-point scales for estimation image noise, artifacts, kidney stone detectability, and delineation of bone and soft tissue., Results: Both conventional low-dose protocols without tin prefiltration were associated with the highest individual equivalent doses and the highest effective radiation dose in the experimental setup (P1: 0.29-6.43 mGy, 1.45-1.83 mSv; P5: 0.50-9.35 mGy, 2.33-2.79 mSv). With no false-positive diagnoses, both readers correctly detected each of the 11 urinary calculi irrespective of scan protocol and phantom configuration. Protocols using spectral shaping via tin prefiltration allowed for effective radiation dose reduction of up to 38% on the standard phantom and 18% on the modified obese phantom, while maintaining overall diagnostic image quality. Effective dose was approximately 10% lower in a male versus female anatomy and could be reduced by another 10% if gonadal protection was used ( P < 0.001)., Conclusions: Spectral shaping via tin prefiltration at 100 kVp is a suitable means to reduce the effective radiation dose in abdominal CT imaging of patients with suspected urolithiasis. The dose reduction potential is slightly less pronounced in a modified phantom emulating an obese body composition compared with a standard phantom., Competing Interests: Conflicts of interest and sources of funding: This study was supported by the Interdisciplinary Center of Clinical Research Würzburg, Germany., (Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)

Background: Dual-energy computed tomography scans can provide significant benefits to the urinary system. The aim of this study is to determine the limitations and benefits of using dual energy CT urography in patients with urinary system stones and cysts., Methods: In the analysis of the images, the virtual noncontrasted images obtained from the combined nephrogenicexcretory phase and the true noncontrasted images were evaluated. The true noncontrast images were accepted as the gold standard for stone detection., Results: Eighty-three different stones were detected in 26 of the 115 patients included in the study. Sensibilities of virtual noncontrast images in detecting urinary system stones were 66.7% and 65.4% according to the first and second radiologists, respectively. In this study, 32 hyperdense cysts were detected. According to iodine map images, there was no enhancement in 26 of 32 cysts; only 5 cysts showed minimal contrast enhancement. One patient could not decide on contrast enhancement., Discussion: As a result, if CT urography is performed with dual energy, it can provide additional information in patients with urinary system disorder.

To evaluate the safety and efficacy of ultrasound-guided mini-percutaneous nephrolithotomy (mini-perc) for the treatment of upper urinary tract stones in children. We reviewed the records of 70 children with upper urinary tract stones who were treated with a mini-perc technique between July 2015 and April 2020. All puncture site selections and tract dilations were determined by Doppler ultrasonography. Patient age, height, weight, stone size, operation time, stone-free rate (SFR), postoperative complications, tubeless rate, and length of hospital stay (LOS) were recorded. Stone components were analysed using infrared spectroscopy. A total of 47 boys and 23 girls were included. Median weight and height of the patients were 18.5 kg and 110 cm, respectively. Median stone diameter was 2.0 cm. Median time to establish access was 4.0 min and median operation time was 25 min. Patients had median preoperative and postoperative haemoglobin levels of 121 and 113.5 g/L, median haemoglobin levels dropped to 8.0 g/L the day after surgery. No patient needed a blood transfusion. Eight children (11.4%) developed significant complications, including four cases with Clavien Grade I, one with Clavien Grade II, and three with Clavien Grade Ⅲ complications. One-day and 1-month follow-ups revealed a complete SFR of 95.7% (67/70) and 97.1% (68/70), respectively. Fifty-six patients (80.0%) did not require catheters of any type (total tubeless). Median LOS was 2.0 days. Ultrasound-guided mini-perc is safe and effective. The mini-perc technique is a feasible alternative for paediatric stone disease that does not result in major complications., Competing Interests: Declaration of competing interest The authors declare no conflict of interest., (Copyright © 2022 Asian Surgical Association and Taiwan Robotic Surgery Association. Published by Elsevier B.V. All rights reserved.)

To analyze characteristics of urinary phospate stones using spectral CT (rapid kV switching dual-energy CT) in vivo. This study included 55 single-composition and 36 mixed-composition infection stones, which were derived from 38 patients (15 males and 23 females), with median age of 49 years old (range 10-71). The effective nuclear number (Z eff ) peak, its high and low points, average Z eff value, water- and iodine-based values, and average CT values were detected. Post-surgical infrared spectroscopy results were compared with CT findings. In single-composition stones, 21 were of carbapatite, 20 of struvite, and 14 of brushite. These stones differed significantly in Z eff peak, its low and high points, average Z eff value, and iodine-based value. Three compositions had similar water-based and average CT values. The 36 mixed-composition stones all had two Z eff peaks, which included 23 of carbapatite, 25 of struvite, and 24 of brushite. When 95% confidence intervals of Z eff peaks in single-composition stones were as standard, this standard overlapped 82.6% of carbapatite, 88.0% of struvite, and 87.5% of brushite in mixed-composition stones. But many low and high points in three mixed-composition groups were outside the 95% confidence interval (overlap < 70%). The significance differences were not observed in water/iodine-based, average Z eff and average CT values. Z eff peaks in vivo spectral CT revealed some characteristics of various phospate stones., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Objective: To determine the sensitivity of dual-energy (DE) virtual non-contrast computed tomography (vNCT), generated from the excretory phase of a CT urogram, compared to true non-contrast CT (tNCT) for the detection of urinary calculi., Methods: A search of multiple medical literature databases was performed using predetermined search terms. Inclusion and exclusion criteria were applied, and bias risk was assessed by two independent reviewers using the quality assessment of diagnostic accuracy studies (QUADAS) tool. Collated estimates of sensitivity were generated, and sources of heterogeneity were identified and reviewed., Results: Thirteen studies (1760 patients; 1740 urinary calculi) were included for sensitivity assessment. Pooled sensitivity for urinary calculi on vNCT was 78.1% (95% CI: 70.2 to 85.0%); however, heterogeneity between studies was very high (I 2 = 92.0%). Sources of heterogeneity between studies were explored through subgroup analysis by categorising studies according to slice thickness (≥ 2 mm and < 2 mm), use of oral hydration, and use of intravenous furosemide. Pooled sensitivity for detection of urinary calculi on vNCT for studies that used oral hydration and < 2 mm slice thickness was 92.2% (95% CI: 89.5 to 94.5%). Pooled specificity was not performed as true negatives were not reported in most studies. Potential sources of bias were identified in included studies., Conclusion: vNCT demonstrated a moderate pooled sensitivity compared to tNCT for the detection of urinary calculi in split bolus CT urogram protocols. However, subgroup analysis suggests higher sensitivity when employing oral hydration and < 2 mm slice thickness or increment., Key Points: • vNCT demonstrated moderate pooled sensitivity for the detection of urinary calculi in split bolus CT urogram protocols. • Subgroup analysis suggested higher sensitivity with oral hydration and < 2 mm slice thickness or increment., (© 2022. Crown.)

Background: National and multinational surveys indicate large variability of Computed Tomography urinary tract Stone doses. The wide use of abdominopelvic Computed Tomography in the diagnosis, raised the issue of radiation exposure. Hence, this study was conducted to assess Computed Tomography radiation exposure of urinary tract Stone Patients there by, to compare the results from established reference values and other published studies., Methods: A retrospective cross-sectional was done on 100 urinary tract Stone patients who have at least one computed tomography scan as part of their follow-up or for diagnosis purposes from February 1 to May 31, 2021, at Tikur Anbesa Specialized Hospital. Data were collected using a structured questionnaire format that evaluates the number of Computed Tomography they had, scan parameters, dose indicators, and socio-demographic characteristics. Finally, the collected data were analyzed using statistical software SPSS version 22., Results: Out of 100 patients 3.6%of our patients have radiation exposure of more than 4mSv, which is the standard for low-dose Computed Tomography. The median radiation exposure is 1.27mSv per scan. Exposure factors like tube current, tube current products, dose length product, and scan range all have similar values with an almost null interquartile range. All the scans that overpassed the low dose threshold(4mSv) were done outside Tikur Anbesa Specialized Hospital., Conclusion: Our study showed that Tikur Anbesa Specialized Hospital's low-dose CT protocol for patients with urinary tract Stone is well optimized as opposed to non- Tikur Anbesa Specialized Hospital., (© 2022 Seife T.D, et al.)

The purpose of this study was to evaluate and compare positive cystography techniques at 5%, 10%, and 20%, as well as three different double-contrast protocols for detecting radiolucent uroliths with a diameter of less than 3.0 mm in dogs. Six cadavers were used, one was selected at random to represent the negative control, and the others were submitted to urolith implantation in the bladder by urethral catheter. Three radiology professionals blindly accessed ventrodorsal and -lateral projections of each test. Contrast at 20% showed greater diagnostic sensitivity, but with greater difficulty identifying the number and size of the uroliths. Consequently, double-contrast techniques are better and should be used for diagnostic and therapeutic planning. Sensitivity and specificity tests demonstrated that positive 5% cystography and different concentrations of double contrast obtained better results in terms of sensitivity and specificity. However, due to the presence of a greater amount of artifacts in the 5% cystography, it is suggested that double contrast is used for this purpose, especially with the removal of contrast excess (protocol 2)., Competing Interests: The authors have declared that no competing interests exist.

Background: Persistent uroliths after a cystotomy in dogs are a common cause of surgical failure., Objectives: This study examined the following: the success rate of retrograde urohydropropulsion in male dogs using non-enhanced computed tomography (CT), whether the CT mean beam attenuation values in Hounsfield Units (mHU) measured in vivo could predict the urolithiasis composition and whether the selected reconstruction kernel may influence the measured mHU., Methods: All dogs and cats that presented with lower urinary tract uroliths and had a non-enhanced CT preceding surgery were included. In male dogs, CT was performed after retrograde urohydropropulsion to detect the remaining urethral calculi. The percentage and location of persistent calculi were recorded. The images were reconstructed using three kernels, from smooth to ultrasharp, and the calculi mHU were measured., Results: Sixty-five patients were included in the study. The success rate of retrograde urohydropropulsion in the 45 male dogs was 55.6% and 86.7% at the first and second attempts, respectively. The predominant components of the calculi were cystine (20), struvite (15), calcium oxalate (8), and urate (7). The convolution kernel influenced the mHU values (p < 0.05). The difference in mHU regarding the calculus composition was better assessed using the smoother kernel. A mHU greater than 1,000 HU was predictive of calcium oxalate calculi., Conclusions: Non-enhanced CT is useful for controlling the success of retrograde urohydropropulsion. The mHU could allow a prediction of the calculus composition, particularly for calcium oxalate, which may help determine the therapeutic strategy., Competing Interests: The authors declare no conflicts of interest., (© 2022 The Korean Society of Veterinary Science.)

Objective: To evaluate the detection and quantification of urinary stones using virtual unenhanced images (VUE) at different phases and slice thickness in contrast-enhanced dual-energy CT Urography (DECTU) in comparison with true unenhanced images (TUE)., Methods: One hundred and twelve urinary stone patients who required triphasic DECTU were analyzed. Data were reconstructed as the followings: TUE images with 1.25 mm thickness (TUE portal venous phase VUE images with 1.25 and 5-mm thickness (VUE(VP)&#95;1.25 mm and VUE(VP)&#95;5 mm) and excretory phase VUE images with 1.25 and 5-mm thickness (VUE(EP)&#95;1.25mm and VUE(EP)&#95;5mm). The. The stones were divided into large (≥5 mm) and small stones. The detection rate, size and CT value of stones were assessed by 2 radiologists and statistically compared among the above groups., Results: Two hundred and thirty urinary stones (163 large and 67 small stones) were detected on TUE&#95;1.25 mm images. For large stones, the detection rate on VUE(VP)&#95;1.25 mm, VUE(VP)&#95;5 mm, VUE(EP)&#95;1.25 mm and VUE(EP)&#95;5 mm was 100%, 96.9%, 85.9%, and 80.4%; while for small stones, the rate was 77.6%, 37.3%, 46.3%, and 23.9%, respectively. VUE(VP) images significantly improved the stone detection rate compared with VUE(EP) images at both slice thicknesses. In general, VUE images identified stones with smaller sizes and lower Hounsfield units, but thinner slice thickness images reduced the inaccuracy. Inter-reader agreement of the stone detection revealed a k value range from 0.85 to 0.94 for TUE and VUE images., Conclusion: Large stones (≥5 mm) can reliably be detected on thin section VUE(VP) images with 33% radiation dose reduction. However, for small stones TUE remains superior. Stone size is underestimated on VUE images., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Purpose: This study aimed to predict the composition of urolithiasis using deep learning from urinary stone images., Materials and Methods: We classified 1,332 stones into 31 classes according to the stone composition. The top 4 classes with a frequency of 110 or more (class 1: calcium oxalate monohydrate [COM] 100%, class 2: COM 80%+struvite 20%, class 3: COM 60%+calcium oxalate dihydrate [COD] 40%, class 4: uric acid 100%) were selected. With the 965 stone images of the top 4 classes, we used the seven convolutional neural networks (CNN) to classify urinary stones and compared their classification performances., Results: Among the seven models, Xception&#95;Ir0.001 showed the highest accuracy, precision, and recall and was selected as the CNN model to predict the stone composition. The sensitivity and specificity for the 4 classes by Xception&#95;Ir0.001 were as follows: class 1 (94.24%, 91.73%), class 2 (85.42%, 96.14%), class 3 (86.86%, 99.59%), and class 4 (94.96%, 98.82%). The sensitivity and specificity of the individual components of the stones were as follows. COM (98.82%, 94.96%), COD (86.86%, 99.64%), struvite (85.42%, 95.59%), and uric acid (94.96%, 98.82%). The area under the curves for class 1, 2, 3, and 4 were 0.98, 0.97, 1.00, and 1.00, respectively., Conclusions: This study showed the feasibility of deep learning for the diagnostic ability to assess urinary stone composition from images. It can be an alternative tool for conventional stone analysis and provide decision support to urologists, improving the effectiveness of diagnosis and treatment., Competing Interests: The authors have nothing to disclose., (© The Korean Urological Association.)

Silicate stones are extraordinarily rare in human beings, but when present, they are often associated with ingestion of Magnesium Trisilicate, an antacid medication. However, there have been few case reports of patients who developed silicate stones, without ingestion of Magnesium Trisilicate. Hereby, we present the case of a 67-year-old man who developed acute kidney injury due to obstructive uropathy, detected during his scheduled chemotherapy for his relapsing multiple myeloma. Abdominal ultrasound and CT scan imaging demonstrated multiple non-mobile calcifications in the bladder neck/prostate bed. Stone analysis showed a material resembling silica. This case with silicate urinary tract stone highlights this extra-rare urinary stone in a patient without any identified source of silicate.  DOI: 10.52547/ijkd.7044.

Objective: To document the clinical signs, diagnosis, and treatment of urolithiasis in green iguanas (Iguana iguana) and to report on the composition of uroliths from green iguanas submitted to the Minnesota Urolith Center for analysis., Animals: 21 green iguanas with urolithiasis., Procedures: Medical record databases of multiple veterinary teaching hospitals were searched from 1996 through 2020. Emails were sent to all facilities that submitted a urolith from a green iguana to the Minnesota Urolith Center from 1996 through 2020. Signalment; presenting complaint; physical examination findings; hematologic, biochemical, and diagnostic imaging findings; treatment; necropsy results; and survival times were described for each patient., Results: Iguanas most commonly presented with nonspecific clinical signs, but 9 of the 21 iguanas had clinical signs associated with the urogenital tract. Twelve iguanas had a palpable mass in the caudal coelom. All uroliths were visible on radiographs. Surgery was performed on 15 iguanas; 3 died secondary to intra- or postoperative complications. Iguanas that underwent surgery had a median survival time of 39 months. Necropsy was performed on 5 iguanas, and urolithiasis contributed to the decision to euthanize or was the cause of death for 4. Uroliths from 132 iguanas were analyzed, and all were composed of 100% uric acid salts., Clinical Relevance: Green iguanas with urolithiasis may not have clinical signs or physical examination findings associated with the urinary system, and hematologic and biochemical abnormalities are nonspecific. Green iguanas should be routinely examined for uroliths, and surgical treatment should be pursued.

Objective: To assess automatic computer-aided in situ recognition of the morphological features of pure and mixed urinary stones using intra-operative digital endoscopic images acquired in a clinical setting., Materials and Methods: In this single-centre study, a urologist with 20 years' experience intra-operatively and prospectively examined the surface and section of all kidney stones encountered. Calcium oxalate monohydrate (COM) or Ia, calcium oxalate dihydrate (COD) or IIb, and uric acid (UA) or IIIb morphological criteria were collected and classified to generate annotated datasets. A deep convolutional neural network (CNN) was trained to predict the composition of both pure and mixed stones. To explain the predictions of the deep neural network model, coarse localization heat-maps were plotted to pinpoint key areas identified by the network., Results: This study included 347 and 236 observations of stone surface and stone section, respectively; approximately 80% of all stones exhibited only one morphological type and approximately 20% displayed two. A highest sensitivity of 98% was obtained for the type 'pure IIIb/UA' using surface images. The most frequently encountered morphology was that of the type 'pure Ia/COM'; it was correctly predicted in 91% and 94% of cases using surface and section images, respectively. Of the mixed type 'Ia/COM + IIb/COD', Ia/COM was predicted in 84% of cases using surface images, IIb/COD in 70% of cases, and both in 65% of cases. With regard to mixed Ia/COM + IIIb/UA stones, Ia/COM was predicted in 91% of cases using section images, IIIb/UA in 69% of cases, and both in 74% of cases., Conclusions: This preliminary study demonstrates that deep CNNs are a promising method by which to identify kidney stone composition from endoscopic images acquired intra-operatively. Both pure and mixed stone composition could be discriminated. Collected in a clinical setting, surface and section images analysed by a deep CNN provide valuable information about stone morphology for computer-aided diagnosis., (© 2021 The Authors BJU International published by John Wiley & Sons Ltd on behalf of BJU International.)

Objective: To explore the application value of the X-ray digital tomographic fusion technique in the diagnosis of urinary system diseases., Methods: 500 patients with suspected urinary diseases in our hospital were examined by three methods: X-ray digital tomographic fusion imaging (DTS), intravenous pyelography (IVP), and abdominal plain film (KUB), and the image quality before and after tomographic fusion was objectively evaluated. The image quality could be divided into three grades: excellent, good, and poor., Results: The image excellent rate of DTS (88%) was higher than that of IVP (27.5%). The sensitivity of DTS in the diagnosis of renal cyst and space occupying of the bladder was higher than that of IVP ( P < 0.05). The accuracy rate of DTS in the diagnosis of urinary calculi was 93.33%, higher than 63.3% of KUB ( P < 0.001). The accuracy rate of DTS in the diagnosis of ureteral stricture was 90%, higher than 65% of IVP ( P =0.03). The accuracy of DTS in the diagnosis of hydronephrosis was higher than that of IVP and KUB ( P < 0.05)., Conclusion: In the examination of urinary system-related diseases, high-definition images can be obtained by timely using sectional fusion technology. Compared with conventional IVP, space occupying lesions such as the bladder and kidney can be displayed more clearly with the help of the tomographic fusion technique, which is helpful to improve the possibility of finding lesions and is of great significance in clinical application., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Chensi Ouyang et al.)

Background: Deep learning Computed Tomography (CT) reconstruction (DLR) algorithms promise to improve image quality but the impact on clinical diagnostic performance remains to be demonstrated. We aimed to compare DLR to standard iterative reconstruction for detection of urolithiasis by unenhanced CT in children and young adults., Methods: This was an IRB approved retrospective study involving post-hoc reconstruction of clinically acquired unenhanced abdomen/pelvis CT scans. Images were reconstructed with six different manufacturer-standard DLR algorithms and reformatted in 3 planes (axial, sagittal, and coronal) at 3 mm intervals. De-identified reconstructions were loaded as independent examinations for review by 3 blinded radiologists (R1, R2, R3) tasked with identifying and measuring all stones. Results were compared to the clinical iterative reconstruction images as a reference standard. IntraClass correlation coefficients and kappa (k) statistics were used to quantify agreement., Results: CT data for 14 patients (mean age: 17.3 ± 3.4 years, 5 males and 9 females, weight class: 31-70 kg (n = 6), 71-100 kg (n = 7), > 100 kg (n = 1)) were reconstructed into 84 total exams. 7 patients had urinary tract calculi. Interobserver agreement on the presence of any urinary tract calculus was substantial to almost perfect (k = 0.71-1) for all DLR algorithms. Agreement with the reference standard on number of calculi was excellent (ICC = 0.78-0.96) and agreement on the size of the largest calculus was fair to excellent (ICC = 0.51-0.97) depending on reviewer and DLR algorithm., Conclusion: Deep learning reconstruction of unenhanced CT images allows similar renal stone detectability compared to iterative reconstruction., (© 2021. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Objective: To assess accuracy of dual-energy computed tomography (DECT) to differentiate uric acid from calcium urinary stones in dual-energy split filter vs sequential-spiral vs dual-source acquisition., Methods: Thirty-four urinary stones (volume 89.0 ± 77.4 mm³; 17 calcium stones, 17 uric acid stones) were scanned in a water-filled phantom using a split-filter equipped CT scanner (SOMATOM Definition Edge, Siemens Healthineers, Forchheim, Germany) in split-filter mode at 120 kVp and sequential-spiral mode at 80 and 140 kVp. Additional DE scans were acquired at 80 and 140 kVp (tin filter) with a dual-source CT scanner (SOMATOM Definition FLASH, Siemens Healthineers). Scans were performed with a CTDIvol of 7.3 mGy in all protocols. Urinary stone categorization was based on dual energy ratio (DER) using an automated 3D segmentation. As reference standard, infrared spectroscopy was used to determine urinary stone composition., Results: All three DECT techniques significantly differentiated between uric acid and calcium stones by attenuation values and DERs ( p < 0.001 for all). Split-filter DECT provided higher DERs for uric acid stones, when compared with dual-source and sequential-spiral DECT, and lower DERs for calcified stones when compared with dual-source DECT ( p < 0.001 for both), leading to a decreased accuracy for material differentiation., Conclusion: Split-filter DECT, sequential-spiral DECT and dual-source DECT all allow for the acquisition of DER to classify urinary stones., Advances in Knowledge: Split-filter DECT enables the differentiation between uric acid and calcium stones despite decreased spectral separation when compared with dual-source and dual-spiral DECT.

Purpose: To investigate the potential for decreasing radiation dose when utilizing a third-generation vs second-generation dual-source dual-energy CT (dsDECT) scanner, while maintaining diagnostic image quality and acceptable image noise. Materials and Methods : Retrospective analysis of patients who underwent dsDECT for clinical suspicion of urolithiasis from October 2, 2017, to September 5, 2018. Patient demographics, body mass index, abdominal diameter, scanning parameters, and CT dose index volume (CTDIvol) were recorded. Image quality was assessed by measuring the attenuation and standard deviation (SD) regions of interest in the aorta and in the bladder. Image noise was determined by averaging the SD at both levels. Patients were excluded if they had not undergone both third- and second-generation dual-energy CT (DECT), time between DECT was more than 2 years, or scan parameters were outside the standard protocol. Results: A total of 117 patients met the inclusion criteria. Examinations performed on a third-generation DECT had an average CTDIvol 12.3 mGy, while examinations performed on a second-generation DECT had an average CTDIvol 13.3 mGy ( p  < 0.001). Average image noise was significantly lower for the third-generation DECT (SD = 10.3) compared with the second-generation DECT (SD = 13.9) ( p  < 0.001). Conclusions: The third-generation dsDECT scanners can simultaneously decrease patient radiation dose and decrease image noise compared with second-generation DECT. These reductions in radiation exposure can be particularly important in patients with urinary stone disease who often require repeated imaging to evaluate for stone development and recurrence as well as treatment assessment.

Purpose: To analyze various compositions of urinary stones using revolution spectral CT (rapid kV switching dual-energy CT) in vivo., Methods: 202 patients with urinary stones underwent spectral CT before surgery. Zeff peak, overall scope and CT values were detected. Moreover, water/iodine attenuating material images were obtained. Removed stones were subjected to infrared spectroscopy after surgery. The results of infrared spectroscopy were compared with CT., Results: 28 stones (14.08%) with single composition, 165 stones with two mixed compositions (81.68%), and 9 stones with three mixed compositions (4.46%) were observed. When Zeff peaks of stones with single/mixed compositions were summarized together, 146 peaks of calcium oxalate monohydrate, 119 peaks of calcium oxalate dihydrate, 55 peaks of carbapatite, 38 peaks of urate, 16 peaks of struvite, and 11 peaks of brushite were totally observed. 93.8% of calcium oxalate monohydrate had Zeff peaks between 13.3 and 14.0. 91.6% of calcium oxalate dihydrate had peaks between 12.0 and 13.3. For carbapatite, 90.9% of stones had peaks from 14.0 to 15.0. A total of 94.8% of urate had peaks between 7.0 and 11.0. 93.8% of struvite had peaks between 11.0 and 13.0, and 90.9% of brushite had peaks between 12.0 and 14.0. Moreover, densities of urate, struvite and brushite were low density in iodine-based images and high-density in water-based images., Conclusion: The in-vivo analysis of spectral CT in urinary stone revealed characteristics of different compositions, especially mixed compositions. An in-vivo predictive model may be constructed to distinguish stone compositions., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature.)

Background: Recent increased use of medical images induces further burden of their interpretation for physicians. A plain X-ray is a low-cost examination that has low-dose radiation exposure and high availability, although diagnosing urolithiasis using this method is not always easy. Since the advent of a convolutional neural network via deep learning in the 2000s, computer-aided diagnosis (CAD) has had a great impact on automatic image analysis in the urological field. The objective of our study was to develop a CAD system with deep learning architecture to detect urinary tract stones on a plain X-ray and to evaluate the model's accuracy., Methods: We collected plain X-ray images of 1017 patients with a radio-opaque upper urinary tract stone. X-ray images (n = 827 and 190) were used as the training and test data, respectively. We used a 17-layer Residual Network as a convolutional neural network architecture for patch-wise training. The training data were repeatedly used until the best model accuracy was achieved within 300 runs. The F score, which is a harmonic mean of the sensitivity and positive predictive value (PPV) and represents the balance of the accuracy, was measured to evaluate the model's accuracy., Results: Using deep learning, we developed a CAD model that needed 110 ms to provide an answer for each X-ray image. The best F score was 0.752, and the sensitivity and PPV were 0.872 and 0.662, respectively. When limited to a proximal ureter stone, the sensitivity and PPV were 0.925 and 0.876, respectively, and they were the lowest at mid-ureter., Conclusion: CAD of a plain X-ray may be a promising method to detect radio-opaque urinary tract stones with satisfactory sensitivity although the PPV could still be improved. The CAD model detects urinary tract stones quickly and automatically and has the potential to become a helpful screening modality especially for primary care physicians for diagnosing urolithiasis. Further study using a higher volume of data would improve the diagnostic performance of CAD models to detect urinary tract stones on a plain X-ray., (© 2021. The Author(s).)

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., (© 2021. The Author(s).)

A 238-day-old female infant (122 days of age corrected for prematurity, weight 4,847 g) presented with macrohematuria. She was born at 23 weeks and3 days of pregnancy. Her birth weight was 492 g. Ultrasound revealeda 3×2 mm left ureteral stone and left hydronephrosis of grade I-II in the Society of Fetal Urology (SFU) Classification. She suffered from frequent vomiting and weight loss, and was treated with analgesics and rehydration. Eventually, left hydronephrosis was relieved, and she passed the stone at 271 days of age. The stone was 4×3×2 mm in size, and consisted of 98% calcium oxalate and2% calcium phosphate. No recurrent stone has been found during follow-up.

Purpose: Cystine stones are widely considered hard and difficult to treat. Hounsfield Units (HU) are used in other stone types to estimate 'hardness' and treatments based on that finding. Our objective was to report mean HU of cystine stones in vivo in a large case series of cystinuria patients and assess for differences in genotype., Methods: A prospective case series of cystinuria patients referred to a specialist centre was analysed. CT imaging was assessed by two independent radiologists to determine in vivo attenuation of cystine calculi. Mean HU was compared for both cystinuria genes (SLC3A1 and SLC7A9) using an independent t-test., Results: 164 adult cystinuric patients were identified (55% male), median age 43 years (range 18-80). Median follow up was 31 months (IQR 10-62). Genetic data available for 153/164 (93%) demonstrated 97 SLC3A1 (63%) and 55 (36%) SLC7A9 mutations (39 homozygous, 16 heterozygous) and one heterozygous for both SLC3A1/SLC7A9. 107 patients had CT images available demonstrating calculi. Median HU across the cohort was 633 (5th to 95th centile 328-780). There was no difference in mean HU between SLC3A1 and SLC7A9 genotypes (p = 0.68) or homo and heterozygous SLC7A9 (p = 0.70). Mean HU correlated with stone size (Pearson correlation coefficient = 0.51, p < 0.001)., Conclusion: In this large single centre cystinuria cohort, mean HU was low for stones that are difficult to treat. Calculi of < 800 HU should prompt consideration of a cystinuria diagnosis. Attenuation was not associated with genotype, and distinct 'smooth' and 'rough' stones were not observed. Calculi with HU > 1000 are unlikely pure cystine, and in a known cystinuric would suggest conversion to another stone type., (© 2020. The Author(s).)

Introduction: The aim of this review was to comprehensively assess the effectiveness and safety of fluoroless ureteroscopy (URS) vs. conventional URS for urinary stones., Evidence Acquisition: An exhaustive search on PubMed, EMBASE, Web of Science and Cochrane Library were performed to find eligible research before May 2020. Result parameters including stone-free rate (SFR), operation time, repeat procedure rate and complication rate were assessed using RevMan 5.3 (Cochrane Training; https://training.cochrane.org/online-learning/core-software-cochrane-reviews/revman/revman-5-download)., Evidence Synthesis: Seven studies (5 retrospective studies and 2 prospective randomized controlled trials) involving 1404 individuals were included. Pooled results demonstrated that the operation time in fluoroless URS group was slightly longer than conventional URS group (weighted mean difference [MD]=2.79, P=0.0001), but no statistically significant differences regarding SFR (odds ratio [OR]=1.18, P=0.57), repeat procedure rate (OR=1.32, P=0.52), and total complication rate (OR=0.75, P=0.16) were observed between two techniques., Conclusions: Flourless URS is equally safe and effective to conventional URS procedure with zero radiation exposure. However, it needs to be cautiously conducted in selected patients and fluoroscopy equipment should always be available intraoperatively.

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.

Rationale: Reversible posterior leukoencephalopathy syndrome (RPLS) is a clinicoradiological phenomenon first observed 2 decades ago. Reversibility is the hallmark of this rare clinical phenomenon once the triggering pathology is aptly and adequately treated. Tinnitus preceding bilateral hearing loss as a symptomatology of RPLS has not been reported in the literature. Furthermore, chronic obstructive ureteric calculus with superimposed infections as a cause of RPLS has not been reported in the literature., Patient Concerns: A 57-year-old female was admitted at our facility because of 2 days history of hearing loss in both ears. She experienced tinnitus in both ears 2 weeks prior to the hearing loss. She is a known hypertensive. She has also undergone multiple surgical treatments for urinary calculi., Diagnosis: Computed tomography (CT) scan of the urinary system revealed a calculus at the right ureter. Magnetic resonance imaging (MRI) showed abnormal signals at both temporo-parieto-occipital (TPO) cortices, the subcortical area, as well as the left hippocampus which was consistent with the diagnosis of RPLS., Interventions: While on antibiotics for treatment of infections, the patient went into hypertensive encephalopathy and pneumonia was also established necessitating intensive care., Outcomes: We observed a resolution of the patient's temperature and hypertension when the right ureteric stone finally descended into the bladder. Also, we observed disappearance of the abnormal signals at both TPO cortices, the subcortical area, as well as the left hippocampus. Two years follow-up revealed no recurrence of her symptomatology., Lesions: Patients who present with hypertensive encephalopathy maybe more prone to developing RPLS. Renal insufficiency alone or hypertension alone may not be single predisposing entities to RPLS but rather multiple predisposing factors., Competing Interests: The authors have no funding and conflicts of interest to disclose., (Copyright © 2021 the Author(s). Published by Wolters Kluwer Health, Inc.)

Background Virtual unenhanced (VUE) images obtained by using a dual-energy CT (DECT) multimaterial decomposition algorithm hold promise for diagnostic use in the abdomen in lieu of true unenhanced (TUE) images. Purpose To assess VUE images obtained from a DECT multimaterial decomposition algorithm in patients undergoing renal mass and urinary stone evaluation. Materials and Methods In this retrospective Health Insurance Portability and Accountability Act-compliant study, DECT was performed in patients undergoing evaluation for renal mass or urinary stone. VUE images were compared quantitatively to TUE images and qualitatively assessed by four independent radiologists. Differences in attenuation between VUE and TUE images were summarized by using 95% limits of agreement. Diagnostic performance in urinary stone detection was summarized by using area under the receiver operating characteristic curve, sensitivity, and specificity. Results A total of 221 patients (mean age ± standard deviation, 61 years ± 14; 129 men) with 273 renal masses were evaluated. Differences in renal mass attenuation between VUE and TUE images were within 3 HU for both enhancing masses (95% limits of agreement, -3.1 HU to 2.7 HU) and nonenhancing cysts (95% limits of agreement, -2.9 HU to 2.5 HU). Renal mass classification as enhancing mass versus nonenhancing cyst did not change (reclassification rate of enhancing masses, 0% [0 of 78]; 95% CI: 0, 5; reclassification rate of nonenhancing cysts, 0% [0 of 193]; 95% CI: 0, 2) with use of VUE in lieu of TUE images. Among 166 urinary stones evaluated, diagnostic performance of VUE images for stone detection was lower compared with that of TUE images (area under the receiver operating characteristic curve, 0.79 [95% CI: 0.73, 0.84] vs 0.93 [95% CI: 0.91, 0.95]; P < .001) due to reduced sensitivity of VUE for detection of stones 3 mm in diameter or less compared with those greater than 3 mm (sensitivity, 23% [25 of 108; 95% CI: 12, 40] vs 88% [126 of 144; 95% CI: 77, 94]; P < .001). Conclusion Compared with true unenhanced images, virtual unenhanced (VUE) images were unlikely to change renal mass classification as enhancing mass versus nonenhancing cyst. Diagnostic performance of VUE images remained suboptimal for urinary stone detection due to subtraction of stones 3 mm or less in diameter. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Sosna in this issue.

Complications after renal allograft transplantation are not so uncommon. Most complications are related to graft rejection, immune-suppressive drug toxicity and the operative procedure. Stents are placed after a transplant to prevent urine leak at the site of ureteric reimplantation, to facilitate an early healing in immune-suppressed individuals and to prevent obstruction at the site of ureteral anastomosis. We report a case of a renal allograft recipient with a forgotten ureteral double J stent. where the stent remained in situ for more than 4 years and further complicated by encrustation and stone formation at both the bladder and renal pelvic ends. The stone over the bladder coil was removed by holmium laser cystolithotripsy while the encrusted renal pelvic coil was removed by percutaneous approach. This case is presented for its rarity and also to emphasise on the need for maintenance of a stent register in order to ensure avoidance of such preventable complications., Competing Interests: Competing interests: None declared., (© BMJ Publishing Group Limited 2021. No commercial re-use. See rights and permissions. Published by BMJ.)

Competing Interests: Competing interests: None declared.