Introduction Stress urinary incontinence (SUI) is a common complication following radical prostatectomy, affecting up to 60.0% of men. The artificial urinary sphincter (AUS) has been the gold standard for treating severe SUI since its introduction in 1973. Despite its efficacy, long-term complications such
as device failure and recurrent incontinence are relatively common, often necessitating revision surgeries. This review focuses on cuff downsizing as a revision strategy for non-mechanical AUS failure.
Material and methods A literature review was conducted using PubMed/Medline, covering studies published between January 2000 and December 2023. Key words included: “artificial urinary sphincter”, “cuff downsizing”, “urethral atrophy”, “non-mechanical failure” and "male urinary incontinence revision”.
Inclusion criteria were studies addressing cuff downsizing as a primary revision for non-mechanical failures. Only English-language studies were reviewed. We analyzed the timing of revisions, follow-up duration, and outcomes such as continence rates, complication rates, and device survival.
Results Six retrospective studies involving 206 patients were included in the present review. Cuff downsizing was performed as the sole intervention in 3 studies and in combination with other approaches in the remaining 3 studies. The median cuff size decreased from 4.5 cm preoperatively to 4.0 cm postoperatively, with 8.0–12.0% of patients receiving a cuff downsized by more than 1.0 cm. Across all studies, continence rates after revision surgery ranged from 52.0% to 90.0% based on patientreported outcome measures (PROMs). Device survival rates varied from 64.0% to 95.0%, with infection and urethral erosion being the leading causes of device explantation.
Conclusions Cuff downsizing is a reasonable revision strategy for non-mechanical AUS failure, offering similar continence outcomes and complication rates compared to alternative techniques.

Background Renal cell carcinoma (RCC) is a prevalent malignancy with highly variable outcomes. MicroRNA-15a
(miR-15a) has emerged as a promising prognostic biomarker in RCC, linked to angiogenesis, apoptosis, and proliferation.
Radiogenomics integrates radiological features with molecular data to non-invasively predict biomarkers, offering
valuable insights for precision medicine. This study aimed to develop a machine learning-assisted radiogenomic
model to predict miR-15a expression in RCC.
Methods A retrospective analysis was conducted on 64 RCC patients who underwent preoperative multiphase
contrast-enhanced CT or MRI. Radiological features, including tumor size, necrosis, and nodular enhancement, were
evaluated. MiR-15a expression was quantified using real-time qPCR from archived tissue samples. Polynomial regression
and Random Forest models were employed for prediction, and hierarchical clustering with K-means analysis
was used for phenotypic stratification. Statistical significance was assessed using non-parametric tests and machine
learning performance metrics.
Results Tumor size was the strongest radiological predictor of miR-15a expression (adjusted R2 = 0.8281, p < 0.001).
High miR-15a levels correlated with aggressive features, including necrosis and nodular enhancement (p < 0.05),
while lower levels were associated with cystic components and macroscopic fat. The Random Forest regression
model explained 65.8% of the variance in miR-15a expression ( R2 = 0.658). For classification, the Random Forest classifier
demonstrated exceptional performance, achieving an AUC of 1.0, a precision of 1.0, a recall of 0.9, and an F1-score
of 0.95. Hierarchical clustering effectively segregated tumors into aggressive and indolent phenotypes, consistent
with clinical expectations.
Conclusions Radiogenomic analysis using machine learning provides a robust, non-invasive approach to predicting
miR-15a expression, enabling enhanced tumor stratification and personalized RCC management. These findings
underscore the clinical utility of integrating radiological and molecular data, paving the way for broader adoption
of precision medicine in oncology.