Follow-up strategies after trimodal treatment for muscle-invasive bladder cancer: a systematic review

PURPOSE: Optimal follow-up strategies following trimodal treatment for muscle invasive bladder cancer play a crucial role in detecting and managing relapse and side-effects. This article provides a comprehensive summary of the patterns and risk factors of relapse, functional outcomes, and follow-up protocols. METHODS: A systematic literature search on PubMed and review of current guidelines and institutional follow-up protocols after trimodal therapy were conducted. RESULTS: Out of 200 identified publications, 43 studies (28 retrospective, 15 prospective) were selected, encompassing 7447 patients (study sizes from 24 to 728 patients). Recurrence rates in the urinary bladder varied between 14-52%; 3-16% were muscle-invasive while 11-36% were non-muscle invasive. Nodal recurrence occurred at 13-16% and distant metastases at 15-35%. After 5 and 10 years of follow-up, around 60-85% and 45-75% of patients could preserve their bladder, respectively. Various prognostic risk factors associated with relapse and inferior survival were proposed, including higher disease stage (> c/pT2), presence of extensive/multifocal carcinoma in situ (CIS), hydronephrosis, multifocality, histological subtypes, incomplete transurethral resection of bladder tumor (TURBT) and incomplete response to radio-chemotherapy. The analyzed follow-up guidelines varied slightly in terms of the number, timing, and types of investigations, but overall, the recommendations were similar. CONCLUSION: Randomized prospective studies should focus on evaluating the impact of specific follow-up protocols on oncological and functional outcomes following trimodal treatment for muscle-invasive bladder cancer. It is crucial to evaluate personalized adaption of follow-up protocols based on established risk factors, as there is potential for improved patient outcomes and resource allocation

New Insights Into Smile, Mispricing and Value At Risk: The Hyperbolic Model

We investigate a new basic model for asset pricing, the hyperbolic model, which allows an almost perfect statistical fit of stock return data. After a brief introduction into the theory supported by an appendix we use also secondary market data to compare the hyperbolic model to the classical Black-Scholes model. We study implicit volatilities, the smile effect and the pricing performance. Exploiting the full power of the hyperbolic model, we construct an option value process from a statistical point of view by estimating the implicit risk-neutral density function from option data. Finally we present some new valueat -risk calculations leading to new perspectives to cope with model risk. I Introduction There is little doubt that the Black-Scholes model has become the standard in the finance industry and is applied on a large scale in everyday trading operations. On the other side its deficiencies have become a standard topic in research. Given the vast literature where refinements a..

Modelling Financial Data Using Generalized Hyperbolic Distributions

rameter estimations for German stock and US stock index data and evaluate the goodness of the fits. Especially we look at the tails of the distributions. 1 Generalized hyperbolic distributions The density function of the generalized hyperbolic (GH) distribution is given by gh(x; ; ff; fi; ffi; ¯) = a \Gamma ffi 2 + (x \Gamma ¯) 2 \Delta (\Gamma1=2)=2 K \Gamma1=2 i ff p ffi 2 + (x \Gamma ¯) 2 j exp(fi(x \Gamma ¯)) (1) a = a (ff; fi; ffi) = (ff 2 \Gamma fi 2 )<F20.9

Apparent scaling

A number of authors have reported empirically observed scaling laws of the absolute values of log returns of stocks and exchange rates, with a scaling coefficient in the order of 0.58-0.59. It is suggested here that this phenomenon is largely due to the semi-heavy tailedness of the distributions concerned rather than to real scaling.