Dual-energy computed tomography: A reliable and established tool for In vivo differentiation of uric acid from nonuric acid renal Stones

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Dual-Energy Multidetector Computed Tomography: A Highly Accurate Non-Invasive Tool for in Vivo Determination of Chemical Composition of Renal Calculi

Introduction. Computed tomography is more accurate than excretory urography in evaluation of renal stones due to its high sensitivity and temporal resolution; it permits sub-millimetric evaluation of the size and site of calculi but cannot evaluate their chemical composition. Dual-energy computed tomography allows evaluating the chemical composition of urinary calculi using simultaneous image acquisition at two different energy levels. The objective of the research was to determine renal stone composition using dual-energy multidetector computed tomography, and its correlation with post-extraction chemical analysis of stones. Materials and Methods. This prospective study was conducted in the Department of Radiodiagnosis and Imaging from September 2017 to March 2019. A total of 50 patients with urolithiasis at the age of 18-70 years were included in the study. Dual-energy computed tomography ratios of various stones were noted, and preoperative composition of calculi was given based on their colour and dual-energy computed tomography ratio. These results were compared with the post-extraction chemical analysis of stones (using Fourier infrared transform spectroscopy as the standard comparative method.) Results. The most common type of calculi in our study population was calcium oxalate stones (78%) followed by uric acid stones (12%), cystine stones (6%) and hydroxyapatite stones (4%). The dual-energy ratio of calcium oxalate, uric acid, cystine and hydroxyapatite stones ranged from 1.38-1.59, 0.94-1.08, and 1.20-1.28 and 1.52-1.57, respectively, with the mean dual-energy ratio of 1.43, 1.01, 1.25 and 1.55, respectively. Dual-energy computed tomography was found to be 100% sensitive and specific for differentiating uric acid stones from non‑uric acid stones. The sensitivity and specificity in differentiating calcium oxalate calculus from non‑calcium oxalate calculus was 97.5% and 90.9%, respectively, with 96% accuracy and kappa value of 0.883 suggesting strong agreement. Conclusions. Dual-energy computed tomography is highly sensitive and accurate in distinguishing between various types of renal calculi. It has vital role in management as uric acid calculi are amenable to drug treatment, while most of non-uric acid calculi require surgical intervention.

Value of artificial intelligence model based on unenhanced computed tomography of urinary tract for preoperative prediction of calcium oxalate monohydrate stones in vivo

Background

Urolithiasis is a global disease with a high incidence and recurrence rate, and stone composition is closely related to the choice of treatment and preventive measures. Calcium oxalate monohydrate (COM) is the most common in clinical practice, which is hard and difficult to fragment. Preoperative identification of its components and selection of effective surgical methods can reduce the risk of patients having a second operation. Methods that can be used for stone composition analysis include infrared spectroscopy, X-ray diffraction, and polarized light microscopy, but they are all performed on stone specimens in vitro after surgery. This study aimed to design and develop an artificial intelligence (AI) model based on unenhanced computed tomography (CT) images of the urinary tract, and to investigate the predictive ability of the model for COM stones in vivo preoperatively, so as to provide surgeons with more accurate diagnostic information.

Methods

Preoperative unenhanced CT images of patients with urinary calculi whose components were determined by infrared spectroscopy in a single center were retrospectively analyzed, including 337 cases of COM stones and 170 of non-COM stones. All images were manually segmented and the image features were extracted, and randomly divided into the training and testing sets in a ratio of 7:3. The least absolute shrinkage and selection operation algorithm (LASSO) was used to construct the AI model, and classification of the training and testing sets was carried out.

Results

A total of 1,218 radiomics imaging features were extracted, and 8 features with non-zero coefficients were finally obtained. The sensitivity, specificity and accuracy of the AI model were 90.5%, 84.3% and 88.5% for the training set, and 90.1%, 84.3% and 88.3% for the testing set. The area under the curve was 0.935 for the training set and 0.933 for the testing set.

Conclusions

The AI model based on unenhanced CT images of the urinary tract can predict COM and non-COM stones in vivo preoperatively, and the model has high sensitivity, specificity and accuracy.

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

OBJECTIVES

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

METHODS

Between September 2018 and September 2019, 116 study participants were prospectively included in the study if they had at least one 3-20-mm urinary stone on an initial urinary tract SE-CT scan. An additional dual-energy CT (DE-CT) scan was performed, limited to the stone of interest. Additionally, to include a sufficient number of UA stones, eight participants with confirmed UA stone on DE-CT were retrospectively included. The SE-CT stone features used in the prediction models were (1) maximum attenuation (maxHU) and (2) the peak point Laplacian (ppLapl) calculated at the position in the stone with maxHU. Two prediction models were previously published methods (ppLapl-maxHU and maxHU) and the third was derived from the previous results based on the k-nearest neighbors (kNN) algorithm (kNN-ppLapl-maxHU). The three methods were evaluated on this new independent stone dataset. The reference standard was the CT vendor's DE-CT application for kidney stones.

RESULTS

Altogether 124 participants (59 ± 14 years, 91 men) with 106 non-UA and 37 UA stones were evaluated. For classification of UA and non-UA stones, the sensitivity, specificity, and accuracy were 100% (37/37), 97% (103/106), and 98% (140/143), respectively, for kNN-ppLapl-maxHU; 95% (35/37), 98% (104/106), and 97% (139/143) for ppLapl-maxHU; and 92% (34/37), 94% (100/106), and 94% (134/143) for maxHU.

CONCLUSION

A quantitative SE-CT method (kNN-ppLapl-maxHU) can classify UA stones with accuracy comparable to DE-CT.

KEY POINTS

• Single-energy CT is the first-line diagnostic tool for suspected renal colic. • A single-energy CT method based on the internal urinary stone attenuation distribution can classify urinary stones into uric acid and non-uric acid stones with high accuracy. • This immensely increases the availability of in vivo stone analysis.

Role of Dual-Energy Computed Tomography in Characterization of Ureteric Calculi and Urinary Obstruction

Objective The present study was carried out to assess the accuracy of dual-energy computed tomography (DECT) in the morphological and chemical characterization of ureteric calculi along with the prediction of the grade of urinary obstruction. Methods This was a prospective observational study that included 100 cases with ultrasonography (USG)-diagnosed ureteric calculi that underwent surgery or had spontaneous expulsion of ureteric calculi. At enrolment, DECT was performed for an in vivo evaluation of volume, chemical composition, and grade of obstruction by subjective assessment of the perinephric edema. After surgical intervention, in vitro evaluation of volume was done by fluid displacement followed by infrared spectroscopy (IRS) for chemical composition. DECT findings were compared with the biochemical analysis and degree of obstruction was validated against excretory CT urograms. Sensitivity, specificity, and the positive predictive and negative predictive values of DECT were assessed. Results No significant difference was observed between the mean volume of stones by fluid displacement (65.1±77.61 mm³) and DECT assessment (66.09±81.78 mm³). IRS revealed the composition of stones as hydroxyapatite, uric acid, cysteine, oxalic acid, and mixed type in 48, 23, 15, five, and nine cases. The sensitivity and specificity of DECT for hydroxyapatite, uric acid, cysteine, oxalic acid, and mixed types were 89.6% and 88.5%, 82.6% and 97.5%, 86.7% and 96.5%, 80% and 98.9%, and 88.9% and 98.9%, respectively. On CT urography, a total of 35 had a high-grade and 65 had a low-grade obstruction, whereas DECT revealed high- and low-grade obstructions in 42 and 58 cases. The sensitivity and specificity of DECT for a high-grade obstruction were 94.3% and 86.2%. Conclusions The findings of the study showed that DECT provides comprehensive information regarding the morphological, chemical, and anatomical characterization of ureteric 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.