Effect of Clinical Parameters on Risk of Death from Cancer after Radical Prostatectomy in Men with Localized and Locally Advanced Prostate Cancer.

Background: The study aimed to assess predictors and to identify patients at increased risk of prostate-cancer-specific mortality (CSM) after radical prostatectomy (RP). Methods: A total of 2421 men with localized and locally advanced PCa who underwent RP in 2001−2017 were included in the study. CSM predictors were assessed using multivariate competing risk analysis. Death from other causes was considered a competing event. Cumulative CSM and other-cause mortality (OCM) were calculated in various combinations of predictors. Results: During the median 8 years (interquartile range 4.4−11.7) follow-up, 56 (2.3%) of registered deaths were due to PCa. Cumulative 10 years CSM and OCM was 3.6% (95% CI 2.7−4.7) and 15.9% (95% CI 14.2−17.9), respectively. The strongest predictors of CSM were Grade Group 5 (GG5) (hazard ratio (HR) 19.9, p < 0.0001), lymph node invasion (HR 3.4, p = 0.001), stage pT3b-4 (HR 3.1, p = 0.009), and age (HR 1.1, p = 0.0007). In groups created regarding age, stage, and GG, cumulative 10 years CSM ranged from 0.4−84.9%, whereas OCM varied from 0−43.2%. Conclusions: CSM after RP is related to GGs, pathological stage, age, and combinations of these factors, whereas other-cause mortality is only associated with age. Created CSM and OCM plots can help clinicians identify patients with the most aggressive PCa who could benefit from more intensive or novel multimodal treatment strategies.

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. Patients were stratified into low-, intermediate- and high-risk groups, according to the preoperative PSA, pathological stage, grade group and lymph nodes status. The ten-year cumulative incidence of biochemical recurrence (BCR), metastases, cancer-specific mortality (CSM) and overall mortality (OM) were calculated in patients with undetectable and persistent PSA in different PCa-risk groups. Multivariate regression analyses depicted the significance of PSA persistence on each study endpoint. RESULTS: Of all 1225 men, in 246 (20.1%), PSA persistence was detected. These men had an increased risk of BCR (hazard ratio (HR) 4.2, p < 0.0001), metastases (HR: 2.7, p = 0.002), CRM (HR: 5.5, p = 0.002) and OM (HR: 1.8, p = 0.01) compared to the men with undetectable PSA. The same significance of PSA persistence on each study endpoint was found in the high-risk group (HR: 2.5 to 6.2, p = 0.02 to p < 0.0001). In the intermediate-risk group, PSA persistence was found as a predictor of BCR (HR: 3.9, p < 0.0001), while, in the low-risk group, PSA persistence was not detected as a significant predictor of outcomes after RP. CONCLUSIONS: Persistent PSA could be used as an independent predictor of worse long-term outcomes in high-risk PCa patients, while, in intermediate-risk patients, this parameter significantly predicts only biochemical recurrence and has no impact on the outcomes in low-risk PCa patients.

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

Objective: 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). Patients and Methods: 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. Time-depending area under the curve (AUC) depicted the performance of GG model on prediction of CSM. Results: Over a median follow-up of 7.9-year (IQR 4.4-11.7) after RP, 235 (12.2%) deaths were registered, and 52 (2.7%) of them were related to PCa. GG model showed high and stable performance (time-dependent AUC 0.88) on prediction of CSM. Cumulative 10-year CSM in GGs 1 to 5 was 0.9%, 2.3%, 7.6%, 14.7%, and 48.6%, respectively; 10-year OCM in GGs was 15.5%, 16.1%, 12.6%, 17.7% and 6.5%, respectively. The ratio between 10-year CSM/OCM in GGs 1 to 5 was 1:17, 1:7, 1:2, 1:1, and 7:1, respectively. Conclusions: Cancer-specific and other-cause mortality differed widely between GGs. Presented findings could aid in personalized clinical decision making for active treatment.

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.g. thickness) and shape, and perform faster diagnostics by increasing the accuracy of tumours differentiation. It leads to optimization of time-consuming analysis procedures of medical images and could be used as a reliable decision support tool in the field of dermatology.

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. Developed models' abilities to forecast short- and mid-term forecasts were tested using prediction horizon.

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. Spearman's correlation calculation showed that the influence of independent variables, such as visits at hospitals and other medical institutions, number of children in the region, number of beds in hospital and other medical institutions, average life expectancy and doctor's visits in that region are the most consistent and common in all three datasets. Tests on the performance of artificial neural networks, multiple linear regression, partial least squares, support vector machines and four non-parametric regression methods were conducted on the collected datasets. The best and most promising results were demonstrated by generalised additive (R(2) = 0.90455) in the regional data case, smoothing splines models (R(2) = 0.98584) in the long annual data case and multilayer feedforward artificial neural networks in the short annual data case (R(2) = 0.61103).

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. The modelling based on social-economic indicators (regression implemented in LCA-IWM model) showed particular sensitivity (deviation from actual data in the range from 2.2 to 20.6%) to external factors, such as the synergetic effects of affluence parameters or changes in MSW collection system. For the time series analysis, the combination of autoregressive integrated moving average (ARIMA) and seasonal exponential smoothing (SES) techniques were found to be the most accurate (mean absolute percentage error equalled to 6.5). Time series analysis method was very valuable for forecasting the weekly variation of waste generation data (r (2) > 0.87), but the forecast yearly increase should be verified against the data obtained by regression modelling. The methods and findings of this study may assist the experts, decision-makers and scientists performing forecasts of MSW generation, especially in developing countries.