Validation of an Automated CT Image Analysis in the Prevention of Urinary Stones with Hydration Trial.
Authors
Affiliations (13)
Affiliations (13)
- Division of Urology, Department of Surgery, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
- Charles and Jane Pak Center for Mineral Metabolism and Clinical Research, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
- Division of Endocrinology, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
- Department of Urology, University of Washington, Seattle, Washington, USA.
- Glickman Urological and Kidney Institute, Cleveland Clinic, Cleveland, Ohio, USA.
- Department of Biostatistics and Health Informatics, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
- Department of Biostatistics and Bioinformatics, Duke Clinical Research Institute, Duke University School of Medicine, Durham, North Carolina, USA.
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, Minnesota, USA.
- Division of Urologic Surgery, Departments of Surgery and Anesthesiology, Washington University School of Medicine, St. Louis, Missouri, USA.
- National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, Maryland, USA.
- Departments of Urology and Population Health Sciences, Duke Clinical Research Institute, Duke University School of Medicine, Durham, North Carolina, USA.
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Abstract
<b><i>Introduction and Objective:</i></b> Kidney stone growth and new stone formation are common clinical trial endpoints and are associated with future symptomatic events. To date, a manual review of CT scans has been required to assess stone growth and new stone formation, which is laborious. We validated the performance of a software algorithm that automatically identified, registered, and measured stones over longitudinal CT studies. <b><i>Methods:</i></b> We validated the performance of a pretrained machine learning algorithm to classify stone outcomes on longitudinal CT scan images at baseline and at the end of the 2-year follow-up period for 62 participants aged >18 years in the Prevention of Urinary Stones with Hydration (PUSH) randomized controlled trial. Stones were defined as an area of voxels with a minimum linear dimension of 2 mm that was higher in density than the mean plus 4 standard deviations of all nonnegative HU values within the kidney. The four outcomes assessed were: (1) growth of at least one existing stone by ≥2 mm, (2) formation of at least one new ≥2 mm stone, (3) no stone growth or new stone formation, and (4) loss of at least one stone. The accuracy of the algorithm was determined by comparing its outcomes to the gold standard of independent review of the CT images by at least two expert clinicians. <b><i>Results:</i></b> The algorithm correctly classified outcomes for 61 paired scans (98.4%). One pair that the algorithm incorrectly classified as stone growth was a new renal artery calcification on end-of-study CT. <b><i>Conclusions:</i></b> An automated image analysis method validated for the prospective PUSH trial was highly accurate for determining clinical outcomes of new stone formation, stone growth, stable stone size, and stone loss on longitudinal CT images. This method has the potential to improve the accuracy and efficiency of clinical care and endpoint determination for future clinical trials.