The potential value of artificial neural networks (ANN) as a predictor of malignancy has begun to receive increased recognition. Research and case studies can be found scattered throughout a multitude of journals. Artificial Neural Networks in Cancer Diagnosis, Prognosis, and Patient Management brings together the work of top researchers - primarily clinicians - who present the results of their state-of-the-art work with ANNs as applied to nearly all major areas of cancer for diagnosis, prognosis, and management of the disease.
The book introduces the theory of neural networks and the method of their application in oncology. It is not an exercise in ANN research, but the presentation of a new technique for diagnosing and determining the treatment of cancers. The authors have included almost all cancers for which there exist ANN applications. When the data available is ill-defined and the development of an algorithmic solution difficult, neural networks provide a non-linear approach which helps sift through the maze of information and arrive at a reasonable solution.
Highly interdisciplinary in nature, this book provides comprehensive coverage of the most important materials relating to the applications of ANNs in the cancer field. With contributions from prominent research centers worldwide, it serves as an introduction to how neural networks can be used for accurate prediction or diagnosis and shows why neural networks are more accurate. Artificial Neural Networks in Cancer Diagnosis, Prognosis, and Patient Management gives you an understanding of this new tool, its applications, and when it should be used.
"This book concentrates on the application of ANNs in human cancer research including feature extraction, prognostic studies, survival analyses, and outcome prediction. . . This book presents an exciting alternative to traditional statistical techniques for use in outcome prediction and disease management for cancer patients. . . most readers will be able to use the book immediately."
-Kemi Ladeji-Osias, Morgan State University
Introduction to Artificial Networks and Their Use in Cancer Diagnosis, Prognosis, and Patient Management, R.N.G. Naguib and G.V. Sherbet
Analysis of Molecular Prognostic Factors in Breast Cancer by Artificial Neural Networks, B. Angus, T.W.J. Lennard, R.N.G. Naguib, and G.V. Sherbet
Artificial Neural Approach to Analyzing the Prognostic Significance of DNA Ploidy and Cell Cycle Distribution of Breast Cancer Aspirate Cells, R.N.G. Naguib and G.V. Sherbet
Neural Networks for the Estimation of Prognosis in Lung Cancer, H. Esteva, M. Bellotti, and A.M. Marchevsky
The Use of a Genetic Algorithm Neural Network (GANN) for Prognosis in Surgically Treated Non-Small Cell Lung Cancer (NSCLC), M.F. Jefferson, N. Pendelton, S.B. Lucas, and M.A. Horan
The Use of Machine Learning in Screening for Oral Cancer, P.M. Speight and P. Hammond
Outcome Prediction of Oesophago-Gastric Cancer Using Neural Analysis of Pre- and Post-Operative Parameters, J. Wayman and S.M. Griffin
Artificial Neural Networks in Urologic Oncology, T.H. Douglas and J.W. Moul
Neural Networks in Urologic Oncology, C. Niedernerger and D. Ridout
Comparison of a Neural Network with High Sensitivity and Specificity to Free/Total Serum PSA for Diagnosing Prostate Cancer in Men with PSA less than 4.0 ng/ml, T.A. Stamey, S.D. Barnhill, Z. Zhang, C.M. Yemoto, H. Zhang, and K. Rama Madyastha
Artificial Neural Networks and Prognosis in Prostate Cancer, F.C. Hamdy
Comparison Between Urologists and Artificial Neural Networks in Bladder Cancer Outcome Prediction, K.N. Qureshi and J.K. Mellon
A Probabilistic Neural Network Framework for Detection of Malignant Melanoma, M. Hintz-Madsen, L.K. Hansen, J. Larsen, and K.T. Drzewiecki