Citations

Introduction: The surgical technique for urinary stone removal is partly influenced by its fragility, as prognosticated by the clinician. This feasibility study aims to develop a linear regression model from CT-based radiomic markers to predict kidney stone comminution time in vivo with two ultrasonic lithotrites. Materials and Methods: Patients identified by urologists at our institution as eligible candidates for percutaneous nephrolithotomy were prospectively enrolled. The active engagement time of the lithotrite in breaking the stone during surgery denoted the comminution time of each stone. The comminution rate was computed as the stone volume disintegrated per minute. Stones were grouped into three fragility classes (fragile, moderate, hard), based on inverse of the comminution rates with respect to the mean. Multivariable linear regression models were trained with radiomic features extracted from clinical CT images to predict comminution times in vivo . The model with the least root mean squared error (RMSE) on comminution times and the fewest misclassification of fragility was finally selected. Results: Twenty-eight patients with 31 stones in total were included in this study. Stones in the cohort averaged 1557 (±2472) mm 3 in volume and 5.3 (±7.4) minutes in comminution time. Ten stones had nonmoderate fragility. Linear regression of stone volume alone predicted comminution time with an RMSE of 6.8 minutes and missed all 10 stones with nonmoderate fragility. A fragility model that included stone volume, internal morphology, shape-based radiomics, and device type improved RMSE to below 3.3 minutes and correctly classified 20/21 moderate and 6/10 nonmoderate stones. Conclusions: CT metrics-based fragility models may provide information to surgeons regarding kidney stone fragility and facilitate the selection of stone removal procedures.

Abstract Objective. To develop a convolutional neural network (CNN) noise reduction technique for ultra-high-resolution photon-counting detector computed tomography (UHR-PCD-CT) that can be efficiently implemented using only clinically available reconstructed images. The developed technique was demonstrated for skeletal survey, lung screening, and head angiography (CTA). Approach . There were 39 participants enrolled in this study, each received a UHR-PCD and an energy integrating detector (EID) CT scan. The developed CNN noise reduction technique uses image-based noise insertion and UHR-PCD-CT images to train a U-Net via supervised learning. For each application, 13 patient scans were reconstructed using filtered back projection (FBP) and iterative reconstruction (IR) and allocated into training, validation, and testing datasets (9:1:3). The subtraction of FBP and IR images resulted in approximately noise-only images. The 5-slice average of IR produced a thick reference image. The CNN training input consisted of thick reference images with reinsertion of spatially decoupled noise-only images. The training target consisted of the corresponding thick reference images without noise insertion. Performance was evaluated based on difference images, line profiles, noise measurements, nonlinear perturbation assessment, and radiologist visual assessment. UHR-PCD-CT images were compared with EID images (clinical standard). Main results. Up to 89% noise reduction was achieved using the proposed CNN. Nonlinear perturbation assessment indicated reasonable retention of 1 mm radius and 1000 HU contrast signals (>80% for skeletal survey and head CTA, >50% for lung screening). A contour plot indicated reduced retention for small-radius and low contrast perturbations. Radiologists preferred CNN over IR for UHR-PCD-CT noise reduction. Additionally, UHR-PCD-CT with CNN was preferred over standard resolution EID-CT images. Significance. CT images reconstructed with very sharp kernels and/or thin sections suffer from increased image noise. Deep learning noise reduction can be used to offset noise level and increase utility of UHR-PCD-CT images.

Introduction: Currently, there are multiple intracorporeal lithotripters available for use in percutaneous nephrolithotomy (PCNL). This study aimed to evaluate the efficiency of two novel lithotripters: Trilogy and ShockPulse-SE. Materials and Methods: This is a prospective multi-institutional randomized trial comparing outcomes of PCNL using two novel lithotripters between February 2019 and June 2020. The study assessed objective measures of stone clearance time, stone clearance rate, device malfunction, stone-free rates, and complications. Device assessment was provided through immediate postoperative survey by primary surgeons. Results: There were 100 standard PCNLs completed using either a Trilogy or ShockPulse-SE lithotrite. Using quantitative Stone Analysis Software to estimate stone volume, the mean stone volume was calculated at 4.18 ± 4.79 and 3.86 ± 3.43 cm 3 for the Trilogy and ShockPulse-SE groups, respectively. Stone clearance rates were found to be 1.22 ± 1.67 and 0.77 ± 0.68 cm 3 /min for Trilogy vs ShockPulse-SE ( p = 0.0542). When comparing Trilogy to ShockPulse-SE in a multivariate analysis, total operative room time (104.4 ± 48.2 minutes vs 121.1 ± 59.2 minutes p = 0.126), rates of secondary procedures (17.65% vs 40.81%, p = 0.005), and device malfunctions (1.96% vs 34.69%, p < 0.001) were less, respectively. There was no difference in final stone-free rates between devices. Conclusion: Both the Trilogy and ShockPulse-SE lithotripters are highly efficient at removing large renal stones. In this study, we noted differences between the two devices including fewer device malfunctions when Trilogy device was utilized. Clinical Trial ID number: NCT03959683