Correlation of urinary catheterization with histologic grading of eosinophilic cystitis: a single institutional review of 27 cases.

Eosinophilic cystitis (EC) is an uncommon diagnosis, mimicking urothelial carcinoma. Multiple etiologies including iatrogenic, infectious, and neoplastic have been suggested, effecting both adults and pediatric population. A retrospective clinicopathologic review of patients with EC in our institution between 2003 and 2021 was conducted. Age, gender, presenting symptoms, cystoscopic findings, and history of urinary bladder instrumentation were recorded. Histologically, urothelial and stromal changes were noted, and mucosal eosinophilic infiltration was graded as mild (scattered eosinophils in the lamina propria), moderate (visible small clusters of eosinophils without brisk reactive changes), or severe (dense eosinophilic infiltrate with ulcer formation and/or muscularis propria infiltration). Twenty-seven patients (male to female ratio = 18/9, median age 58 [12-85 years]), of whom two were in the pediatric age group were identified. Leading presenting symptoms were hematuria (9/27, 33%), neurogenic bladder (8/27, 30%), and lower urinary tract symptoms (5/27, 18%). Four of 27 (15%) patients had history of urothelial carcinoma of urinary bladder. Cystoscopy commonly revealed erythematous mucosa (21/27, 78%) and/or urinary bladder mass (6/27, 22%). Seventeen of 27 (63%) of patients had history of long-term/frequent catheterization. Mild, moderate, and severe eosinophilic infiltrates were seen in 4/27 (15%), 9/27 (33%), and 14/27 (52%) of cases. Proliferative cystitis (19/27, 70%) and granulation tissue (15/27, 56%) were additional common findings. All cases of long-term/frequent instrumentation cases had moderate or severe eosinophilic infiltrate. EC should be in the differential diagnosis; particularly in patients with long term/frequent catheterization.

Artificial intelligence in prostate cancer: Definitions, current research, and future directions.

Multiple novel modalities tasking artificial intelligence based computational pathology applications and integrating other variables, such as risk factors, tumor microenvironment, genomic testing data, laboratory findings, clinical history, and radiology findings, will improve diagnostic consistency and generate a synergistic diagnostic workflow. In this article, we present the concise and contemporary review on the utilization of artificial intelligence in prostate cancer and identify areas for possible future applications.