PROGNOSTIC VALUE OF HISTOLOGY AND LYMPH NODE STATUS IN BILHARZIASIS-BLADDER CANCER: OUTCOME PREDICTION USING NEURAL NETWORKS

Abstract -In this paper, the evaluation of two features in predicting the outcomes of patients with bilharziasis bladder cancer has been investigated using an RBF neural network. Prior to prediction, the feature subsets were extracted from the whole set of features for the purpose of providing a high performance of the network. Throughout the analysis of the prognostic feature combinations, two features, histological type and lymph node status, have been identified as the important indicators for outcome prediction of this type of cancer. The highest predictive accuracy reached 85.0% in this study

Applications of Machine Learning in Cancer Prediction and Prognosis

Machine learning is a branch of artificial intelligence that employs a variety of statistical, probabilistic and optimization techniques that allows computers to “learn” from past examples and to detect hard-to-discern patterns from large, noisy or complex data sets. This capability is particularly well-suited to medical applications, especially those that depend on complex proteomic and genomic measurements. As a result, machine learning is frequently used in cancer diagnosis and detection. More recently machine learning has been applied to cancer prognosis and prediction. This latter approach is particularly interesting as it is part of a growing trend towards personalized, predictive medicine. In assembling this review we conducted a broad survey of the different types of machine learning methods being used, the types of data being integrated and the performance of these methods in cancer prediction and prognosis. A number of trends are noted, including a growing dependence on protein biomarkers and microarray data, a strong bias towards applications in prostate and breast cancer, and a heavy reliance on “older” technologies such artificial neural networks (ANNs) instead of more recently developed or more easily interpretable machine learning methods. A number of published studies also appear to lack an appropriate level of validation or testing. Among the better designed and validated studies it is clear that machine learning methods can be used to substantially (15–25%) improve the accuracy of predicting cancer susceptibility, recurrence and mortality. At a more fundamental level, it is also evident that machine learning is also helping to improve our basic understanding of cancer development and progression

Bladder Cancer

This book is an invaluable source of knowledge on bladder cancer biology, epidemiology, biomarkers, prognostic factors, and clinical presentation and diagnosis. It is also rich with plenty of up-to-date information, in a well-organized and easy to use format, focusing on the treatment of bladder cancer including surgery, chemotherapy, radiation therapy, immunotherapy, and vaccine therapy. These chapters, written by the experts in their fields, include many interesting, demonstrative and colorful pictures, figures, illustrations and tables. Due to its practicality, this book is recommended reading to anyone interested in bladder cancer

Urology

УЧЕБНО-МЕТОДИЧЕСКИЕ ПОСОБИЯУРОЛОГИЯУРОЛОГИЧЕСКИЕ БОЛЕЗН

Global health - a challenge for interdisciplinary research

Human, animal and plant health is a field of work which offers opportunities for inter- and trans-disciplinary research. The whole topic bridges the natural and social sciences. Today, in a world of global environmental change it is widely recognized that human societies and their wellbeing depend on a sustainable equilibrium of ecosystem services and the possibility of cultural adaptation to global environmental change. The need to identify and quantify health risks related to global environmental change is now one of the most important challenges of humankind. Describing spatial (geographic, intra/inter-population) and temporal differences in health risks is an urgent task to understand societies’ vulnerabilities and priorities for interventions better. The Göttingen International Health Network (GIHN) is a research and teaching network in relation to this cross-cutting topic. The book provides a collection of articles which contribute to this issue of overriding importance and presents an overview of the GIHN launch event

Global health

Human, animal and plant health is a field of work which offers opportunities for inter- and trans-disciplinary research. The whole topic bridges the natural and social sciences. Today, in a world of global environmental change it is widely recognized that human societies and their wellbeing depend on a sustainable equilibrium of ecosystem services and the possibility of cultural adaptation to global environmental change. The need to identify and quantify health risks related to global environmental change is now one of the most important challenges of humankind. Describing spatial (geographic, intra/inter-population) and temporal differences in health risks is an urgent task to understand societies’ vulnerabilities and priorities for interventions better. The Göttingen International Health Network (GIHN) is a research and teaching network in relation to this cross-cutting topic. The book provides a collection of articles which contribute to this issue of overriding importance and presents an overview of the GIHN launch event

Environmental Aspects of Zoonotic Diseases

Environmental Aspects of Zoonotic Diseases provides a definitive description, commentary and research needs of environmental aspects related to zoonotic diseases. There are many interrelated connections between the environment and zoonotic diseases such as: water, soil, air and agriculture. The book presents investigations of these connections, with specific reference to environmental processes such as: deforestation, floods, draughts, irrigation practices, soil transfer and their impact on bacterial, viral, fungal, and parasitological spread. Environmental aspects such as climate (tropical, sub-tropical, temperate, arid and semi-arid), developed and undeveloped countries, animal traffic animal border crossing, commercial animal trade, transportation, as well geography and weather on zoonosis, are also discussed and relevant scientific data is condensed and organized in order to give a better picture of interrelationship between the environment and current spread of zoonotic diseases