Prediction of pathological stage in patients with prostate cancer: a neuro-fuzzy model

The prediction of cancer staging in prostate cancer is a process for estimating the likelihood that the cancer has spread before treatment is given to the patient. Although important for determining the most suitable treatment and optimal management strategy for patients, staging continues to present significant challenges to clinicians. Clinical test results such as the pre-treatment Prostate-Specific Antigen (PSA) level, the biopsy most common tumor pattern (Primary Gleason pattern) and the second most common tumor pattern (Secondary Gleason pattern) in tissue biopsies, and the clinical T stage can be used by clinicians to predict the pathological stage of cancer. However, not every patient will return abnormal results in all tests. This significantly influences the capacity to effectively predict the stage of prostate cancer. Herein we have developed a neuro-fuzzy computational intelligence model for classifying and predicting the likelihood of a patient having Organ-Confined Disease (OCD) or Extra-Prostatic Disease (ED) using a prostate cancer patient dataset obtained from The Cancer Genome Atlas (TCGA) Research Network. The system input consisted of the following variables: Primary and Secondary Gleason biopsy patterns, PSA levels, age at diagnosis, and clinical T stage. The performance of the neuro-fuzzy system was compared to other computational intelligence based approaches, namely the Artificial Neural Network, Fuzzy C-Means, Support Vector Machine, the Naive Bayes classifiers, and also the AJCC pTNM Staging Nomogram which is commonly used by clinicians. A comparison of the optimal Receiver Operating Characteristic (ROC) points that were identified using these approaches, revealed that the neuro-fuzzy system, at its optimal point, returns the largest Area Under the ROC Curve (AUC), with a low number of false positives (FPR = 0.274, TPR = 0.789, AUC = 0.812). The proposed approach is also an improvement over the AJCC pTNM Staging Nomogram (FPR = 0.032, TPR = 0.197, AUC = 0.582)

Long-term carbon sink in Borneo's forests halted by drought and vulnerable to edge effects

Less than half of anthropogenic carbon dioxide emissions remain in the atmosphere. While carbon balance models imply large carbon uptake in tropical forests, direct on-the-ground observations are still lacking in Southeast Asia. Here, using long-term plot monitoring records of up to half a century, we find that intact forests in Borneo gained 0.43 Mg C ha‾¹ per year (95% CI 0.14—0.72, mean period 1988-2010) above-ground live biomass. These results closely match those from African and Amazonian plot networks, suggesting that the world's remaining intact tropical forests are now en masse out-of-equilibrium. Although both pan-tropical and long-term, the sink in remaining intact forests appears vulnerable to climate and land use changes. Across Borneo the 1997-1998 El Niño drought temporarily halted the carbon sink by increasing tree mortality, while fragmentation persistently offset the sink and turned many edge-affected forests into a carbon source to the atmosphere

Fuzzy receiver operating characteristic curve: An option to evaluate diagnostic tests

Traditional receiver operating characteristic (ROC) analysis is widely utilized to evaluate diagnostic tests but it is restricted to dichotomous results. The aim of this study is to develop the "fuzzy receiver operating characteristic" methodology combining the fuzzy sets theory and the traditional ROC methodology, and to utilize this new tool to evaluate a diagnostic test. We review traditional ROC analysis in mathematical language that utilizes crisp sets and rewrites it based on fuzzy sets. Fuzzy ROC analysis is used to evaluate a fuzzy- rule-based system (FRBS) developed to predict the pathological stage of a prostate cancer in its ability to discriminate between two states: organ-confined and non-confined. Traditional ROC analysis is insufficient to evaluate this system because the result is given in possibilistic terms. The methodology developed in this work is a generalization of the dichotomous ROC analysis, and appears to better represent the performance of diagnostic tests that include a degree of uncertainty similar to the one presented here.11324425

Fuzzy subset approach in coupled population dynamics of blowflies

This paper is a study on the population dynamics of blowflies employing a density-dependent. non-linear mathematical model and a coupled population formalism. In this Study, we investigated the coupled population dynamics applying fuzzy subsets to model the Population trajectory. analyzing demographic parameters such as fecundity, Survival, and migration. The main results suggest different possibilities in terms of dynamic behavior produced by migration in coupled Populations between distinct environments and the rescue effect generated by the connection between populations. It was possible to conclude that environmental heterogeneity can play an important role in blowfly metapopulation systems. The implications of these results for population dynamics of blowflies are discussed.39234135

Fuzzy expert system for predicting pathological stage of prostate cancer

Prostate cancer is the second most common cancer among men, responsible for the loss of half a million lives each year worldwide, according to the World Health Organization. In prostate cancer, definitive therapy such as radical prostatectomy, is more effective when the cancer is organ-confined. The aim of this study is to investigate the performance of some fuzzy expert systems in the classification of patients with confined or non-confined cancer. To deal with the intrinsic uncertainty about the variables utilized to predict cancer stage, the developed approach is based on Fuzzy Set Theory. A fuzzy expert system was developed with the fuzzy rules and membership functions tuned by a genetic algorithm. As a result, the utilized approach reached better precision taking into account some correlated studies. (C) 2012 Elsevier Ltd. All rights reserved.40246647