Uric acid versus non-uric acid renal stones: in vivo differentiation with spectral CT

Imaging in stone diagnosis and surgical planning

PURPOSE OF REVIEW

Radiological imaging techniques and applications are constantly advancing. This review will examine modern imaging techniques in the diagnosis of urolithiasis and applications for surgical planning.

RECENT FINDINGS

The diagnosis of urolithiasis may be done via plain film X-ray, ultrasound (US), or contrast tomography (CT) scan. US should be applied in the workup of flank pain in emergency rooms and may reduce unnecessary radiation exposure. Low dose and ultra-low-dose CT remain the diagnostic standard for most populations but remain underutilized. Single and dual-energy CT provide three-dimensional imaging that can predict stone-specific parameters that help clinicians predict stone passage likelihood, identify ideal management techniques, and possibly reduce complications. Machine learning has been increasingly applied to 3-D imaging to support clinicians in these prognostications and treatment selection.

SUMMARY

The diagnosis and management of urolithiasis are increasingly personalized. Patient and stone characteristics will support clinicians in treatment decision, surgical planning, and counseling.

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

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

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

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

Evaluation of split-filter dual-energy CT for characterization of urinary stones

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.

Spectral CT of the abdomen: Where are we now?

Spectral CT adds a new dimension to radiological evaluation, beyond assessment of anatomical abnormalities. Spectral data allows for detection of specific materials, improves image quality while at the same time reducing radiation doses and contrast media doses, and decreases the need for follow up evaluation of indeterminate lesions. We review the different acquisition techniques of spectral images, mainly dual-source, rapid kV switching and dual-layer detector, and discuss the main spectral results available. We also discuss the use of spectral imaging in abdominal pathologies, emphasizing the strengths and pitfalls of the technique and its main applications in general and in specific organs.

Can Dual Energy CT with Fast kV-Switching Determine Renal Stone Composition Accurately?

RATIONALE AND OBJECTIVES

To determine whether a single source computed tomography (CT) system utilizing fast kV switching and low dose settings can characterize (diameter and chemical composition) renal stones accurately when compared infrared spectroscopy.

MATERIALS AND METHODS

The chemical composition of 15 renal stones was determined using Fourier transform infrared spectroscopy. The stones were inserted into a porcine kidney and placed within a water tank for CT scanning using both fast kV switching dual energy and standard protocols. Effective atomic number of each stone was measured using scanner software. Stone diameter measurements were repeated twice to determine intra-rater variation and compared to actual stone diameter as measured by micro CT.

RESULTS

The chemical composition of three stones (one calcium phosphate and two carbonite apatite) could not be determined using the scanner software. The composition of 10/12 remaining stones was correctly identified using dual energy computed tomography (83% absolute agreement; k = 0.69). No statistical difference (p = 0.051) was noted in the mean stone diameter as measured by clinical CT and micro CT.

CONCLUSION

Dual energy computed tomography using fast kV switching may potentially be developed as a low dose clinical tool for identifying and classifying renal stones in vivo supporting clinical decision-making.

Dual-Energy CT for Urinary Stone Evaluation

PURPOSE OF REVIEW

Conventional CT imaging is an excellent tool for the diagnosis of nephrolithiasis however is limited in its ability to detect stone composition. Dual-energy CT (DECT) scans have demonstrated promise in overcoming this limitation. We review the current utility of DECT in nephrolithiasis.

RECENT FINDINGS

DECT is superior to conventional CT in differentiating uric acid stones from non-uric acid stones, with numerous studies reporting sensitivities and specificities approaching > 95%. Dose reduction protocols incorporating low-dose CT scans are commonly used, providing significantly lower effective radiation doses compared to conventional CT. DECT remains an effective diagnostic tool in patients with large body habitus. DECT can accurately detect uric acid stones, which can help guide which stones may be suitable to medical dissolution. Further studies evaluating the effectiveness of DECT in guiding management of patients with nephrolithiasis can help to promote its widespread use.

Efficacy of single-source rapid kV-switching dual-energy CT for characterization of non-uric acid renal stones: a prospective ex vivo study using anthropomorphic phantom

PURPOSE

To investigate the accuracy of rapid kV-switching single-source dual-energy computed tomography (rsDECT) for prediction of classes of non-uric-acid stones.

MATERIALS AND METHODS

Non-uric-acid renal stones retrieved via percutaneous nephrolithotomy were prospectively collected between January 2017 and February 2018 in a single institution. Only stones ≥ 5 mm and with pure composition (i.e., ≥ 80% composed of one component) were included. Stone composition was determined using Fourier Transform Infrared Spectroscopy. The stones were scanned in 32-cm-wide anthropomorphic whole-body phantom using rsDECT. The effective atomic number (Zeff), the attenuation at 40 keV (HU40), 70 keV (HU70), and 140 keV (HU140) virtual monochromatic sets of images as well as the ratios between the attenuations were calculated. Values of stone classes were compared using ANOVA and Mann-Whitney U test. Receiver operating curves and area under curve (AUC) were calculated. A p value < 0.05 was considered statistically significant.

RESULTS

The final study sample included 31 stones from 31 patients consisting of 25 (81%) calcium-based, 4 (13%) cystine, and 2 (6%) struvite pure stones. The mean size of the stones was 9.9 ± 2.4 mm. The mean Zeff of the stones was 12.01 ± 0.54 for calcium-based, 11.10 ± 0.68 for struvite, and 10.23 ± 0.75 for cystine stones (p < 0.001). Zeff had the best efficacy to separate different classes of stones. The calculated AUC was 0.947 for Zeff; 0.833 for HU40; 0.880 for HU70; and 0.893 for HU140.

CONCLUSION

Zeff derived from rsDECT has superior performance to HU and attenuation ratios for separation of different classes of non-uric-acid stones.

Modern imaging techniques in urinary stone disease

PURPOSE OF REVIEW

Radiological imaging techniques are a fast developing field in medicine. Therefore, the purpose of this review was to identify and discuss the latest changes of modern imaging techniques in the management of urinary stone disease.

RECENT FINDINGS

The introduction of iterative image reconstruction enables low-dose and ultra-low-dose (ULD) protocols. Although current guidelines recommend their utilization in nonobese patients recent studies indicate that low-dose imaging may be feasible in obese (<30 kg/m) but not in bariatric patients. Use of dual energy computed tomography (CT) technologies should balance between additional information and radiation dose aspects. If available on a dose neutral basis, dual energy imaging and analysis should be performed. Current guidelines recommend measuring the largest diameter for clinical decision making; however, recent studies suggest a benefit from measuring the volume based on multiplanar reformation. Quantitative imaging is still an experimental approach.

SUMMARY

The use of low-dose and even ULD CT protocols should be diagnostic standard, even in obese patients. If dual energy imaging is available, it should be limited to specific clinical questions. The stone volume should be reported in addition to the largest diameter for treatment decision and a more valid comparability of upcoming studies.

Dose independent characterization of renal stones by means of dual energy computed tomography and machine learning: an ex-vivo study

OBJECTIVES

To predict the main component of pure and mixed kidney stones using dual-energy computed tomography and machine learning.

METHODS

200 kidney stones with a known composition as determined by infrared spectroscopy were examined using a non-anthropomorphic phantom on a spectral detector computed tomography scanner. Stones were of either pure (monocrystalline, n = 116) or compound (dicrystalline, n = 84) composition. Image acquisition was repeated twice using both, normal and low-dose protocols, respectively (ND/LD). Conventional images and low and high keV virtual monoenergetic images were reconstructed. Stones were semi-automatically segmented. A shallow neural network was trained using data from ND1 acquisition split into training (70%), testing (15%) and validation-datasets (15%). Performance for ND2 and both LD acquisitions was tested. Accuracy on a per-voxel and a per-stone basis was calculated.

RESULTS

Main components were: Whewellite (n = 80), weddellite (n = 21), Ca-phosphate (n = 39), cysteine (n = 20), struvite (n = 13), uric acid (n = 18) and xanthine stones (n = 9). Stone size ranged from 3 to 18 mm. Overall accuracy for predicting the main component on a per-voxel basis attained by ND testing dataset was 91.1%. On independently tested acquisitions, accuracy was 87.1-90.4%.

CONCLUSIONS

Even in compound stones, the main component can be reliably determined using dual energy CT and machine learning, irrespective of dose protocol.

KEY POINTS

• Spectral Detector Dual Energy CT and Machine Learning allow for an accurate prediction of stone composition. • Ex-vivo study demonstrates the dose independent assessment of pure and compound stones. • Lowest accuracy is reported for compound stones with struvite as main component.

Aktualisierung der S2k-Leitlinie zur Diagnostik, Therapie und Metaphylaxe der Urolithiasis (AWMF Registernummer 043-025)

Die Zunahme des medizinischen Wissens, technische Neuerungen gemeinsam mit demographischem Wandel stellen eine Herausforderung an die Neukonzeption von Leitlinien und klinischen Studien dar. Die vorliegende S2k-Leitlinie, die sich ausschließlich mit Nieren- und Harnleitersteinen beschäftigt, soll die Behandlung von Harnsteinpatienten in Klinik und Praxis unterstützen, aber auch Patienteninformationen zur Urolithiasis geben. Die zunehmende interdisziplinäre Zusammenarbeit in der Steintherapie zeigt sich auch an der Anzahl beteiligter Fachgruppen und Arbeitsgemeinschaften in der Erstellung des neuen Leitlinienupdates. Die vorliegende, aus einem interdisziplinären Konsensusprozess hervorgegangene S2k-Leitlinie stellt die aktuellen Empfehlungen praxisnah dar und gibt Entscheidungshilfen für Diagnostik‑, Therapie- und Metaphylaxemaßnahmen auf Basis von Expertenmeinungen und verfügbaren Evidenzgrundlagen aus der Literatur.

Optimized management of urolithiasis by coloured stent-stone contrast using dual-energy computed tomography (DECT)

BACKGROUND

We analysed in vitro the appearance of commonly used ureteral stents with dual-energy computed tomography (DECT) and we used these characteristics to optimize the differentiation between stents and adjacent stone.

METHODS

We analysed in vitro a selection of 36 different stents from 7 manufacturers. They were placed in a self-build phantom model and measured using the SOMATOM® Force Dual Source CT-Scanner (Siemens, Forchheim, Germany). Each sample was scanned at various tube potentials of 80 and 150 peak kilovoltage (kVp), 90 and 150 kVp and 100 and 150 kVp. The syngo Post-Processing Suite software program (Siemens, Forchheim, Germany) was used for differentiation based on a 3-material decomposition algorithm (UA, calcium, urine) according to our standard stone protocol.

RESULTS

Stents composed of polyurethane appeared blue and silicon-based stents were red on the image. The determined appearances were constant for various peak kilovoltage (kVp) values. The coloured stent-stone-contrast displayed on DECT improves monitoring, especially of small calculi adjacent to indwelling ureteral stents.

CONCLUSION

Both urinary calculi and ureteral stents can be accurately differentiated by a distinct appearance on DECT. For the management of urolithiasis patients can be monitored more easily and accurately using DECT if the stent shows a different colour than the adjacent stone.

New Imaging Techniques in the Management of Stone Disease

Recent advances in computed tomography, X-ray-based imaging, and ultrasonography have improved the accuracy of urinary stone detection and differentiation of stone composition while minimizing radiation exposure. Dual-energy computed tomography and digital tomosynthesis show promise in predicting mineral composition to optimize medical and surgical therapy. Electromagnetic tracking may enhance the use of ultrasonography to achieve percutaneous renal access for nephrolithotomy. This article reviews innovations in imaging technology in the contemporary management of urinary stone disease.

Disk injury in patients with vertebral fractures-a prospective diagnostic accuracy study using dual-energy computed tomography

OBJECTIVES

Using magnetic resonance imaging (MRI) as the standard of reference, we aimed to evaluate the diagnostic accuracy of dual-energy computed tomography (DECT) in assessing disk injuries in patients aged more than 50 years with vertebral fractures.

METHODS

This prospective study was approved by the local ethics committee (EA1/372/14), and all patients gave written informed consent. Patients with suspected fractures underwent spinal DECTs and MRIs. Three readers scored DECT collagen maps for the presence or absence of disk injuries and also scored MR images according to the Sander classification (0-3). Only disks at risk (target disks) were included in the analysis. Sensitivity and specificity were calculated. Fleiss's κ was used to evaluate interrater agreement. Attenuation, in Hounsfield units, was compared between affected and unaffected disks in DECT.

RESULTS

Analyzing 295 disks in 67 patients, DECT was both sensitive (0.85) and specific (0.75). Sensitivity varied with the severity of disk damage, as assessed using the Sander scale (grade 1, 0.80; 2, 0.85; and 3, 0.98). Fleiss's κ was 0.41 for MRI and 0.51 for DECT. In the DECT collagen maps, attenuation was lower in injured disks compared to that in normal disks (80.3 ± 35.2 vs. 97.9 ± 41.0, p < 0.001).

CONCLUSIONS

Compared to conventional CT, DECT collagen maps can yield more diagnostic information, allowing identification of disk injuries in elderly patients with vertebral fractures.

KEY POINTS

• Dual-energy computed tomography allows vertebral disk injuries to be detected in elderly patients with vertebral fractures. • Dual-energy computed tomography yields more diagnostic information about vertebral disks compared to conventional CT. • Dual-energy computed tomography can be used as an alternative imaging modality for patients unwilling or unable to undergo MRI.

Dual-layer spectral detector CT: non-inferiority assessment compared to dual-source dual-energy CT in discriminating uric acid from non-uric acid renal stones ex vivo

PURPOSE

To assess the non-inferiority of dual-layer spectral detector CT (SDCT) compared to dual-source dual-energy CT (dsDECT) in discriminating uric acid (UA) from non-UA stones.

METHODS

Fifty-seven extracted urinary calculi were placed in a cylindrical phantom in a water bath and scanned on a SDCT scanner (IQon, Philips Healthcare) and second- and third-generation dsDECT scanners (Somatom Flash and Force, Siemens Healthcare) under matched scan parameters. For SDCT data, conventional images and virtual monoenergetic reconstructions were created. A customized 3D growing region segmentation tool was used to segment each stone on a pixel-by-pixel basis for statistical analysis. Median virtual monoenergetic ratios (VMRs) of 40/200, 62/92, and 62/100 for each stone were recorded. For dsDECT data, dual-energy ratio (DER) for each stone was recorded from vendor-specific postprocessing software (Syngo Via) using the Kidney Stones Application. The clinical reference standard of X-ray diffraction analysis was used to assess non-inferiority. Area under the receiver-operating characteristic curve (AUC) was used to assess diagnostic performance of detecting UA stones.

RESULTS

Six pure UA, 47 pure calcium-based, 1 pure cystine, and 3 mixed struvite stones were scanned. All pure UA stones were correctly separated from non-UA stones using SDCT and dsDECT (AUC = 1). For UA stones, median VMR was 0.95-0.99 and DER 1.00-1.02. For non-UA stones, median VMR was 1.4-4.1 and DER 1.39-1.69.

CONCLUSION

SDCT spectral reconstructions demonstrate similar performance to those of dsDECT in discriminating UA from non-UA stones in a phantom model.

Uric acid versus non-uric acid urinary stones: differentiation with single energy CT texture analysis

AIM

To evaluate the accuracy of computed tomography (CT) texture analysis (TA) to differentiate uric acid (UA) stones from non-UA stones on unenhanced CT in patients with urinary calculi with ex vivo Fourier transform infrared spectroscopy (FTIR) as the reference standard.

MATERIALS AND METHODS

Fourteen patients with 18 UA stones and 31 patients with 32 non-UA stones were included. All the patients had preoperative CT evaluation and subsequent surgical removal of the stones. CTTA was performed on CT images using commercially available research software. Each texture feature was evaluated using the non-parametric Mann-Whitney test. Receiver operating characteristic (ROC) curves were created and the area under the ROC curve (AUC) was calculated for texture parameters that were significantly different. The features were used to train support vector machine (SVM) classifiers. Diagnostic accuracy was evaluated.

RESULTS

Compared to non-UA stones, UA stones had significantly lower mean, standard deviation and mean of positive pixels but higher kurtosis (p<0.001) on both unfiltered and filtered texture scales. There were no significant differences in entropy or skewness between UA and non-UA stones. The average SVM accuracy of texture features for differentiating UA from non-UA stones ranged from 88% to 92% (after 10-fold cross validation). A model incorporating standard deviation, skewness, and kurtosis from unfiltered texture scale images resulted in an AUC of 0.965±00.029 with a sensitivity of 94.4% and specificity of 93.7%.

CONCLUSION

CTTA can be used to accurately differentiate UA stones from non-UA stones in vivo using unenhanced CT images.

Accurately Diagnosing Uric Acid Stones from Conventional Computerized Tomography Imaging: Development and Preliminary Assessment of a Pixel Mapping Software

PURPOSE

Preoperative determination of uric acid stones from computerized tomography imaging would be of tremendous clinical use. We sought to design a software algorithm that could apply data from noncontrast computerized tomography to predict the presence of uric acid stones.

MATERIALS AND METHODS

Patients with pure uric acid and calcium oxalate stones were identified from our stone registry. Only stones greater than 4 mm which were clearly traceable from initial computerized tomography to final composition were included in analysis. A semiautomated computer algorithm was used to process image data. Average and maximum HU, eccentricity (deviation from a circle) and kurtosis (peakedness vs flatness) were automatically generated. These parameters were examined in several mathematical models to predict the presence of uric acid stones.

RESULTS

A total of 100 patients, of whom 52 had calcium oxalate and 48 had uric acid stones, were included in the final analysis. Uric acid stones were significantly larger (12.2 vs 9.0 mm, p = 0.03) but calcium oxalate stones had higher mean attenuation (457 vs 315 HU, p = 0.001) and maximum attenuation (918 vs 553 HU, p <0.001). Kurtosis was significantly higher in each axis for calcium oxalate stones (each p <0.001). A composite algorithm using attenuation distribution pattern, average attenuation and stone size had overall 89% sensitivity, 91% specificity, 91% positive predictive value and 89% negative predictive value to predict uric acid stones.

CONCLUSIONS

A combination of stone size, attenuation intensity and attenuation pattern from conventional computerized tomography can distinguish uric acid stones from calcium oxalate stones with high sensitivity and specificity.