LAUSR

Preoperative prediction of infection stones from CT images could provide additional information for treatment planning. We developed a radiomics algorithm that could apply data from non-contrast-enhanced CT images to distinguish infection stones from non-infection stones. This retrospective study included 98 patients with clinically confirmed infection kidney stones and 59 patients with non-infection kidney stones. Fifty-four radiomics features extracted from CT images were reduced to 27 key features by the LASSO algorithm, for which a radiomics signature was built with ensemble learning based on bagged trees. Multivariable logistic regression analysis was then used to develop a radiomics nomogram incorporating the radiomics signature and independent clinical factors. The radiomics signature, which consisted of morphological features and textural features, was significantly associated with infection kidney stones. Ensemble learning based on bagged trees could differentiate infection kidney stones from non-infection kidney stones with 90.7% accuracy, 85.81% sensitivity, 93.96% specificity, a 91% positive predictive value and a 91% negative predictive value. Predictors incorporated into the individualized prediction nomogram included the radiomics signature, white blood cell count and urine culture. Decision curve analysis demonstrated that the radiomics nomogram had potential clinical application for infection stone prediction.