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Prediction of Kidney Stone Fragility Using Radiomics-Based Regression Models. | LitMetric

AI Article Synopsis

  • The study aims to create a linear regression model using CT-based radiomic markers to predict how long it takes to break down kidney stones during surgery, considering their fragility.
  • Patients eligible for a specific kidney stone removal procedure, percutaneous nephrolithotomy, were enrolled, with stone disintegration rates measured during surgery.
  • The results showed that a refined model that includes stone volume and other features significantly improved prediction accuracy and could help surgeons choose the best approach for stone removal based on fragility levels.

Article Abstract

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 with two ultrasonic lithotrites. 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 . The model with the least root mean squared error (RMSE) on comminution times and the fewest misclassification of fragility was finally selected. Twenty-eight patients with 31 stones in total were included in this study. Stones in the cohort averaged 1557 (±2472) mm 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. CT metrics-based fragility models may provide information to surgeons regarding kidney stone fragility and facilitate the selection of stone removal procedures.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10066766PMC
http://dx.doi.org/10.1089/end.2022.0483DOI Listing

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