Background and Objective: The role of urine cytology during follow-up for low-grade (LG) non-muscle-invasive bladder cancer (NMIBC) is not well established, although cytology has low sensitivity in detecting LG recurrences. Our study aims to evaluate the impact of urine cytology as a complementary method to cystoscopy during follow-up for LG NMIBC. Methods: Patients diagnosed with primary LG TaT1 bladder cancer (BC) between 2010 and 2020 were included. Patients were stratified according to the EAU NMIBC scoring model. Urine cytology was performed during follow-up cystoscopy. The outcomes of the study were BC recurrence and upgrading to high-grade (HG). Cytology utility was established by assessing whether its result led to management change. Results: We included 337 patients with LG TaT1 BC. EAU risk group distribution was low in 262 (77.7%), intermediate in 57 (16.9%), and high-risk in 18 (5.3%) cases. With a median follow-up of 5 years, 166 (49.3%) patients experienced recurrence. Cystoscopy was positive in 154 (92.8%) and suspicious in 12 (7.2%) cases. Urine cytology was positive in 33 (19.9%) cases but only changed management in 3 (0.89%), all with suspicious cystoscopy. Positive cytology at first recurrence was associated with higher risk of upgrading during follow-up (HR 2.781, p = 0.006) and lower upgrading-free survival (p = 0.001). Conclusions: The role of urine cytology to detect first recurrences during follow-up for primary LG TaT1 NMIBC might be limited to patients with non-conclusive lesions in the cystoscopy. A positive cytology at first recurrence is associated with a higher risk of upgrading to HG BC during follow-up.

Prostate cancer (PCa) is the most common malignancy in men worldwide. Multiparametric MRI (mpMRI) improves the detection of clinically significant PCa (csPCa); however, it remains limited by false-positive findings and inter-observer variability. Time-dependent diffusion (TDD) MRI provides microstructural information that may enhance csPCa characterization beyond standard mpMRI. This prospective observational diagnostic accuracy study protocol describes the evaluation of PROS-TD-AI, an in-house developed AI workflow integrating TDD-derived metrics for zone-aware csPCa risk prediction. PROS-TD-AI will be compared with PI-RADS v2.1 in routine clinical imaging using MRI-targeted prostate biopsy as the reference standard.

Prostate cancer (PCa) is the most frequently diagnosed malignancy in men and the eighth leading cause of cancer death worldwide. Multiparametric MRI (mpMRI) has become central to the diagnostic pathway for men at intermediate risk, improving detection of clinically significant PCa (csPCa) while reducing unnecessary biopsies and over-diagnosis. However, mpMRI remains limited by false positives, false negatives, and moderate to substantial interobserver agreement. Time-dependent diffusion (TDD) MRI, a novel sequence that enables tissue microstructure characterization, has shown encouraging preclinical performance in distinguishing clinically significant from insignificant PCa. Combining TDD-derived metrics with machine learning may provide robust, zone-specific risk prediction with less dependence on reader training and improved accuracy compared to current standard-of-care. This study protocol outlines the rationale and describes the prospective evaluation of a home-developed AI-enhanced TDD-MRI software (PROSTDAI) in routine diagnostic care, assessing its added value against PI-RADS v2.1 and validating results against MRI-guided prostate biopsy.