Machine Learning-Assisted Preoperative Diagnosis of Infection Stones in Urolithiasis Patients.

The decision-making of how to treat urinary infection stones was complicated by the difficulty in preoperative diagnosis of these stones. Hence, we developed machine learning (ML) models that can be leveraged to discriminate between infection and noninfection stones in urolithiasis patients before treatment. We enrolled 462 patients with urinary stones and randomly stratified them into training (80%) and testing sets (20%).

MicroRNA expression profiles in muscle-invasive bladder cancer: identification of a four-microRNA signature associated with patient survival.

Bladder cancer ranks the second most common genitourinary tract cancer, and muscle-invasive bladder cancer (MIBC) accounts for approximately 25 % of all bladder cancer cases with high mortality. In the current study, with a total of 202 treatment-naïve primary MIBC patients identified from The Cancer Genome Atlas dataset, we comprehensively analyzed the genome-wide microRNA (miRNA) expression profiles in MIBC, with the aim to investigate the relationship of miRNA expression with the progression and prognosis of MIBC, and generate a miRNA signature of prognostic capabilities. In the progression-related miRNA profiles, a total of 47, 16, 3, and 84 miRNAs were selected for pathologic T, N, M, and histologic grade, respectively.