Accurate prediction of pure uric acid urinary stones in clinical context via a combination of radiomics and machine learning.

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.

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.

Radiological Density, Atomic Numbers, and Stone Fragmentation of Bego Stones Used for Research in Endourology: Comparison to Real Urinary Stones.

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.

The value of dual-energy computed tomography (DECT) in the diagnosis of urinary calculi: a systematic review and meta-analysis of retrospective studies.

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.

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.

Correlation of the Degree of Hydronephrosis and Computed Tomography Value of Calculi with Efficacy of Ureteroscopic Lithotripsy in Patients with Upper Urinary Tract Infectious Calculi.

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.

The most common types of uroliths larger than 1 mm are readily visible and accurately measured in an in vitro setting mimicking the canine abdomen using digital radiography.

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.

Pure uric acid stone prediction model using the variant coefficient of stone density measured by thresholding 3D segmentation-based methods: A multicenter study.

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.

Effect of surface-partial-volume correction and adaptive threshold on segmentation of uroliths in computed tomography.

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.

Optimal dual-energy computed tomography scan parameters to detect small-sized urinary stones and their composition.

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.

Use of dual energy CT urography in evaluation of urinary stone and complex cyst.

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.

Deep-Learning Segmentation of Urinary Stones in Noncontrast Computed Tomography.

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 R2 > 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.

Machine learning based multi-label classification of single or mixed-composition urinary stones in in vivo spectral CT.

BACKGROUND: Urinary stones comprise both single and mixed compositions. Knowledge of the stone composition helps the urologists choose appropriate medical interventions for patients. The parameters from the spectral computerized tomography (CT) analysis have potential values for identification of the urinary stone compositions. PURPOSE: The present study aims to identify the compositions of urinary stones in vivo using parameters from spectral CT and machine learning, based on multi-label classification modeling. METHODS: This retrospective study collected 252 urinary stone samples with single/mixed compositions (including carbapatite [CP], calcium oxalate monohydrate [COM], calcium oxalate dehydrate [COD], uric acid [UA], and struvite [STR]), which were confirmed by ex vivo infrared spectroscopy. Parameters were extracted from an energy spectrum analysis (ESA) of the spectral CT, including the effective atomic number (Zeff ), Zeff histogram, CT values at a given x-ray energy level, and material densities. These ESA parameters were utilized for composition analysis via a multi-label classification fusion framework, where 250 multi-label models were built and the classification decisions from the top performance models were integrated by a multi-criterion weighted fusion (MCWF) approach in order to reach a consensus prediction. An example-based metric A c c e x a m $Ac{c_{exam}}$ and label-based metric A c c l a b e l $Ac{c_{label}}$ were used for global and label-wise accuracy evaluations, respectively. The top-ranked parameters associated with discriminating the stone composition were also identified. RESULTS: The multi-label classification fusion framework achieved an overall A c c e x a m $Ac{c_{exam}}$ of 81.2%, with A c c l a b e l $Ac{c_{label}}$ of 86.7% (CP), 90.6% (COM), 80.6% (COD), 95.0% (UA), and 94.4% (STR) for each composition on the independent testing cohort 1, and A c c e x a m $Ac{c_{exam}}$ of 76.4% with A c c l a b e l $Ac{c_{label}}$ of 80.5% (CP), 88.7% (COM), 74.9% (COD), 94.4% (UA), and 98.5% (STR) on the independent testing cohort 2. CONCLUSION: The parameters extracted from the ESA on spectral CT can be utilized to characterize single or mixed stone compositions via multi-label classification modeling. The generalization capability of the proposed methodology still requires further verification.

Ultrasound-guided mini-percutaneous nephrolithotomy in the treatment of upper urinary tract stones in children: A single-center evaluation.

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.

Tin-filtered 100 kV Ultra-low-dose Abdominal CT for Calculi Detection in the Urinary Tract: A Comparative Study of 510 Cases.

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/m2). The "ultra-low-dose group" with Sn100 kVp consisted of 220 patients (84 women; age 47 ± 17 years; BMI 26.82 ± 5.62 kg/m2). 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.

Thermoluminescence Dosimetry in Abdominal CT for Urinary Stone Detection: Effective Radiation Dose Reduction With Tin Prefiltration at 100 kVp.

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.

The characterization of in vivo urinary phospate stones by spectral CT.

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 (Zeff) peak, its high and low points, average Zeff 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 Zeff peak, its low and high points, average Zeff value, and iodine-based value. Three compositions had similar water-based and average CT values. The 36 mixed-composition stones all had two Zeff peaks, which included 23 of carbapatite, 25 of struvite, and 24 of brushite. When 95% confidence intervals of Zeff 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 Zeff and average CT values. Zeff peaks in vivo spectral CT revealed some characteristics of various phospate stones.

Computed Tomography Radiation Exposure among Urinary Tract Stone Patients at Tikur Anbessa Specialized Hospital: A Retrospective Study.

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.

National Imaging Trends for Suspected Urinary Stone Disease in the Emergency Department.

This cohort study examines the use of an ultrasonography-first strategy for urinary stone disease.