The Significance of Prostate Specific Antigen Persistence in Prostate Cancer Risk Groups on Long-Term Oncological Outcomes.

Objective: To assess the significance of prostate-specific antigen (PSA) persistence at the first measurement after radical prostatectomy (RP) on long-term outcomes in different prostate cancer risk groups.

Methods: Persistent PSA was defined as ≥0.1 ng/mL at 4-8 weeks after RP.

Impact of Grade Groups on Prostate Cancer-Specific and Other-Cause Mortality: Competing Risk Analysis from a Large Single Institution Series.

To assess the risk of cancer-specific mortality (CSM) and other-cause mortality (OCM) using post-operative International Society of Urological Pathology Grade Group (GG) model in patients after radical prostatectomy (RP). Overall 1921 consecutive men who underwent RP during 2001 to 2017 in a single tertiary center were included in the study. Multivariate competing risk regression analysis was used to identify significant predictors and quantify cumulative incidence of CSM and OCM.

Application of automatic statistical post-processing method for analysis of ultrasonic and digital dermatoscopy images.

Ultrasonic and digital dermatoscopy diagnostic methods are used in order to estimate the changes of structure, as well as to non-invasively measure the changes of parameters of lesions of human tissue. These days, it is very actual to perform the quantitative analysis of medical data, which allows to achieve the reliable early-stage diagnosis of lesions and help to save more lives. The proposed automatic statistical post-processing method based on integration of ultrasonic and digital dermatoscopy measurements is intended to estimate the parameters of malignant tumours, measure spatial dimensions (e.

Time-series-based hybrid mathematical modelling method adapted to forecast automotive and medical waste generation: Case study of Lithuania.

The aim of the study was to create a hybrid forecasting method that could produce higher accuracy forecasts than previously used 'pure' time series methods. Mentioned methods were already tested with total automotive waste, hazardous automotive waste, and total medical waste generation, but demonstrated at least a 6% error rate in different cases and efforts were made to decrease it even more. Newly developed hybrid models used a random start generation method to incorporate different time-series advantages and it helped to increase the accuracy of forecasts by 3%-4% in hazardous automotive waste and total medical waste generation cases; the new model did not increase the accuracy of total automotive waste generation forecasts.

Forecasting medical waste generation using short and extra short datasets: Case study of Lithuania.

The aim of the study is to evaluate the performance of various mathematical modelling methods, while forecasting medical waste generation using Lithuania's annual medical waste data. Only recently has a hazardous waste collection system that includes medical waste been created and therefore the study access to gain large sets of relevant data for its research has been somewhat limited. According to data that was managed to be obtained, it was decided to develop three short and extra short datasets with 20, 10 and 6 observations.

Application and evaluation of forecasting methods for municipal solid waste generation in an Eastern-European city.

Forecasting of generation of municipal solid waste (MSW) in developing countries is often a challenging task due to the lack of data and selection of suitable forecasting method. This article aimed to select and evaluate several methods for MSW forecasting in a medium-scaled Eastern European city (Kaunas, Lithuania) with rapidly developing economics, with respect to affluence-related and seasonal impacts. The MSW generation was forecast with respect to the economic activity of the city (regression modelling) and using time series analysis.