Neural networks is a tool of pattern analysis, prog-nostication and adaptive learning with capabilities to answer questions like – ‘what if’. ‘Neural net-work’ simulates architecture of biological neural networks. The simplest one is single layer. Gener-ally an artificial network consists of many process-ing elements fused together in layers. It has input component, processing component and the output component. Endpoints like survival, toxicity or, prob-ability of nodal involvement constitute output points while weighed variables form the input. Process-ing units are organized into groups called layers and, as such, a typical network consists of a sequence of layers successively connected by full or random connectors. The uniqueness of the neural networks is their capability to learn. The learning process in-cludes associative mapping like, auto association, hetro association, nearest neighbour recall and in-terpolative recall. One another way of learning is by regularity detection. The neural networks are of two types fixed & adaptive. The adaptive networks are dept in both supervised and unsupervised learn-ing. The behaviour of the network can be linear, threshold or sigmoid. ANN have regained their prominence after a phase of obscurity. Hence, the above mentioned book is a timely addition. The book has a collection of articles written by various re-searchers. The book assumes the readers to be fa-miliar with ANN. It would have enticed the first time readers if the introductory chapters had elaborated on the essential concepts of ANN, may be with his-torical background. The book should definitely in-terest an intelligent clinician as it covers areas of application of ANN in lung, prostate, urological can-cers as well as oral cancer. Most of these chapters are generally comprehensible to clinicians though a chapter on probabilistic framework for classifica-tion, which includes Baysion decision theory is full of mathematics, thus a little inaccessible.

Copyright 2005 - Journal of Cancer Research and Therapeutics