Atypical Cell'' Parameter in Automated Urine Analysis for the Diagnosis of Bladder Cancer: A Retrospective Pilot Study - FİLİNTE, DENİZ; ÇAM, HAYDAR KAMİL; TİNAY, İLKER; AKSU, MEHMET BURAK; SARAÇOĞLU, SERTAÇ; İLKİ, ZEYNEP ARZU (GALENOS YAYINCILIK, 2020)
Objective: To evaluate the application of an automated urine analyzer (AUA) for the diagnosis of bladder cancer (BC) Materials and Methods: A retrospective data analysis of 2365 urine specimens from the department of urology has been performed and matched with those patients, who have undergone cystoscopic evaluation or surgical treatment for different urological pathologies during 2018. After matching, clinical records of the patients has been further evaluated in order to select patients with recent or previous BC diagnosis. To assess the diagnostic efficacy of AUA, patients were divided into five groups according to the patient history of BC and healthy controls. Results: A total of 106 patients are included in this study and the majority (69.8%) of the patients are follow-up patients with previous diagnosis and treatment of non-muscle invasive BC (NMIBC). For patients with low-risk NMIBC (n=27), the sensitivity and specificity were calculated as 75% and 100%. For patients with high-risk NMIBC (n=47), who were previously treated with intravesical BCG, the sensitivity and specificity were calculated as 54.5% and 83.3%. All patients in radical cystectomy group (n=7) with muscle invasive BC had positive urine analyses results for atypical cells. And none of the patients in the control group (n=8) had positive AUA results and cystoscopic evaluation also did not show any bladder mass suspicious for BC. Conclusion: The results of this retrospective pilot study showed acceptable sensitivity and specificity rates of the fluorescence flow cytometry based AUA and the results of the low-risk group are especially valuable regarding its potential use to decide on performing a follow-up cystoscopy or not. A prospective study is currently on progress to validate the findings of the current study.
Keywords: Bladder cancer; automated urine analysis; diagnosis