A novel composite two-stage urethroplasty for complex penile strictures: A multicenter experience.

INTRODUCTION: Complex penile strictures are usually repaired using a two-stage urethroplasty. Buccal mucosal graft (BMG) placed in the first stage can have a significant contraction rate, which may require a subsequent revision surgery. We describe a composite two-stage penile urethroplasty using BMG for patients of complex penile strictures who have some salvageable urethral plate. METHODS: Within a multi-institutional cohort, 82 patients underwent a two-stage urethroplasty for complex stricture of the penile urethra. Of these 42 patients who underwent our composite two-stage penile urethroplasty using BMG implanted at the second-stage were included. Patients with genital lichen sclerosus or incomplete clinical records were excluded from this study. The primary outcome of the study was to evaluate stricture-free success rate. RESULTS: Of total 42, 4 patients were lost to follow-up. 42% of stricture etiology was failed hypospadias repair. Mean stricture length was 4.5 cm (range 3-8 cm). Seventeen (44.7%) patients had undergone the previous urethroplasty. At a median follow-up of 44 months, of 38 patients, 34 (89.5%) were successful, and 4 (10.5%) had a recurrence. No patient required revision surgery before the second-stage and required redo buccal graft harvesting for subsequent urethroplasty. CONCLUSIONS: The composite two-stage technique in repairing complex penile urethral strictures is a valid and reproducible surgical treatment for complex penile stricture and it may reduce the rate of contraction of the transplanted BMG.

Kohonen's Map Approach for the Belief Mass Modeling.

In the framework of the evidence theory, several approaches for estimating belief functions are proposed. However, they generally suffer from the problem of masses attribution in the case of compound hypotheses that lose much conceptual contribution of the theory. In this paper, an original method for estimating mass functions using Kohonen's map derived from the initial feature space and an initial classifier is proposed. Our approach allows a smart mass belief assignment, not only for simple hypotheses but also for disjunctions and conjunctions of hypotheses. Thus, it can model at the same time ignorance, imprecision, and paradox. The proposed method for a basic belief assignment (BBA) is of interest for solving estimation mass functions problems where a large quantity of multivariate data is available. Indeed, the use of Kohonen's map simplifies the process of assigning mass functions. The proposed method has been compared with the state-of-the-art BBA technique on benchmark database and applied on remote sensing data for image classification purpose. Experimentation shows that our approach gives similar or better results than other methods presented in the literature so far, with an ability to handle a large amount of data.