Introduction: Non-muscle-invasive bladder cancer (NMIBC) is usually effectively treated with transurethral resection (TUR), most often followed by intravesical instillation of bacillus Calmette-Guérin (BCG) or intravesical chemotherapy. Although the precise mechanism of BCG immunotherapy is still unclear, a local immune response is presumed. However, a number of severe side effects and complications are related to intravesical immunotherapy. AIM: Aim of this report is to present rare case of the renal granulomatous disease in a patient previously treated with intravesical instillation of BCG immunotherapy, following TURBT. In addition, we performed review of previously reported cases of renal granulomas following intravesical BCG immunotherapy. Case report: A 79-year-old man was presented to Urology Clinic due to clinically verified tumor of the urinary bladder. After transurethral resection of bladder tumor, histopathological analysis revealed the diagnosis of papillary urothelial highgrade pT1 carcinoma. Intravesical BCG immunotherapy was initiated, according to protocol currently used in our institution. Upon completion of therapy with BCG, we re-examined the patient and, using ultrasound, found a change in the right kidney, resembling moth bites not seen on CT scan before TURBT. Additionally, CT-guided core-needle biopsy of the affected kidney was performed, and the specimen was sent for histopathological analysis, which revealed chronic necrotizing granulomatous inflammation. Antituberculotic therapy was initiated for 6 months. Upon completion of antituberculotic therapy, control CT-scan was performed at follow-up, indicating regression of changes on the right kidney. Conclusion: This case report emphasizes the importance of consistent implementation of follow-up protocol and the identification of lesions during the asymptomatic period and enables the proper treatment of the disease. To reduce the incidence of adverse effects of BCG treatment for bladder tumors, an individualized approach is needed.

OBJECTIVES: Online patient discussions in health-related social media forums represent a rich and increasingly important source for helping to determine the patient perspective concerning all aspects of their medical condition. Yet there is a common perception in the pharmaceutical industry that use of such data will lead to an overwhelming number of reportable adverse events (AEs). This perception can potentially hold back the use of health-related social media, and thereby hold back an understanding of the patient perspective. In this study, we set out to determine the frequency of reportable AEs in a large sample of patient posts. METHODS: We used a combination of regular expressions and machine learning techniques on a collection of 10,000 posts obtained from cancer discussion forums, to detect posts that met all four of the required criteria for reporting of AEs. We first quantified the performance of the machine learning algorithm with a set of simulated testing posts that were randomly generated by inserting combinations of randomly generated postal addresses, email addresses, zip codes and telephone numbers together with AEs and treatment names. Potentially reportable posts were then manually reviewed. RESULTS: Under testing conditions with simulated posts, the machine learning algorithm identified reportable posts with an AUC of 0.928 and an overall accuracy of 88%. On the collection of 10,000 real posts from cancer forums the algorithm identified 505 potentially reportable posts for manual review. After manual review only two posts met all four criteria for reporting AEs. CONCLUSIONS: Whilst there is a concern that studies involving use of healthrelated social media discussions will lead to a large number of AEs being detected, this study found that posts meeting all four criteria for reporting are very rare, with only 0.1% of posts meeting the criteria for reporting.