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Artificial Neural Networks in Cancer Diagnosis, Prognosis, and Patient Management : Biomedical Engineering Series - R. N. G. Naguib

Artificial Neural Networks in Cancer Diagnosis, Prognosis, and Patient Management

Biomedical Engineering Series

By: R. N. G. Naguib (Editor), G. V. Sherbet (Editor)

Hardcover Published: 22nd June 2001
ISBN: 9780849396922
Number Of Pages: 216

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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.

Industry Reviews

"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 Neural Networks and Their Use in Cancer Diagnosis, Prognosis, and Patient Managementp. 1
Preamblep. 1
Artificial Neural Networksp. 2
Discussionp. 5
Referencesp. 6
Analysis of Molecular Prognostic Factors in Breast Cancer by Artificial Neural Networksp. 9
Introductionp. 9
Prognostic Factors in Breast Cancerp. 10
Established Prognostic Factors in Clinical Use: Stage, Grade, and Sizep. 10
Hormone Receptors and Oestrogen Regulated Proteinsp. 11
Markers of Cellular Proliferation and Cell Cycle Regulatorsp. 12
p53 and Related Proteinsp. 12
Type 1 Growth Factor Receptorsp. 12
Prediction of Nodal Metastasis by Neural Analysisp. 13
Proteins Associated with Metastatic Potentialp. 14
h-mts1 and not nm23 Expression Correlates with Nodal Sread of Cancerp. 15
h-mts1 Expression and ER/PgR Statusp. 16
Anaylsis of h-mts1 and nm23 Expression by Artificial Neural Networksp. 16
Concluding Remarksp. 17
Referencesp. 17
Artificial Neural Approach to Analysing the Prognostic Significance of DNA Ploidy and Cell Cycle Distribution of Breast Cancer Aspirate Cellsp. 23
Introductionp. 23
Breast Cancer Fine-Needle Aspiratesp. 23
Analysis of Image Cytometric Data Using Artificial Neural Networksp. 24
Conclusionsp. 25
Referencesp. 26
Neural Networks for the Estimation of Prognosis in Lung Cancerp. 29
Introductionp. 29
Carcinoma of the Lungp. 29
Medical Applications of Artificial Neural Networksp. 30
Artificial Neural Networksp. 31
Architecturep. 31
Training Artificial Neural Network Systemsp. 31
Estimating the Reliability of Artificial Neural Networksp. 32
Practical Application of Neural Networks in the Prognosis of Lung Cancer Patientsp. 32
Tumour Typep. 33
Ploidy Levelsp. 33
Immunohistochemical Proliferation Markersp. 33
Neural Processingp. 34
Conclusionsp. 35
Referencesp. 35
The Use of a Genetic Algorithm Neural Network (GANN) for Prognosis in Surgically Treated Nonsmall Cell Lung Cancer (NSCLC)p. 39
Introductionp. 39
Methodp. 40
The NSCLC Datap. 40
Mathematical Method: The Genetic Algorithm Neural Networkp. 40
Selection of Predictors by Genetic Algorithmp. 40
Classificationp. 41
Choosing the Optimal Genome for Use with a Neural Networkp. 41
Updating the Optimal Information Genomep. 41
Defining the Neural Network Architecturep. 41
Training the Neural Networkp. 41
Stopping Rule for the Training Processp. 41
Classification Solutionp. 42
Application of Bayes' Theoremp. 42
Computational Methodp. 42
Statistical Methodsp. 42
Predictive Statisticsp. 42
Comparison of Predictive Statisticsp. 42
Survivalp. 43
Survival by Logistic Regressionp. 44
Survival Classification by GANNp. 44
Comparison of Logistic Regression and GANN for Classification of Survivalp. 45
Discussionp. 48
Interpretation of Clinical Findingsp. 48
Methodological Considerationsp. 49
Classification of Survival Outcomep. 49
Losses and Gains in Informationp. 50
Adaptability and Retrainingp. 51
Variable Selection by the GAp. 52
Performance of Genetic Algorithm Neural Network Compared with Logistic Regressionp. 52
Adaptations in the Genetic Algorithm Neural Network Method: Simulating Preprocessing in Basic Neural Systemsp. 52
Components of Methodological Performancep. 52
Conclusionsp. 53
Referencesp. 53
The Use of Machine Learning in Screening for Oral Cancerp. 55
Epidemiologyp. 55
Clinical Features and Diagnosisp. 55
Screening for Oral Cancerp. 56
Selection of High-Risk Groups Using a Neural Networkp. 57
Training and Testing the Neural Networkp. 57
Comparison of Neural Networks and Other Machine Learning Techniquesp. 60
Data Visualisation in Clementinep. 60
Inducing Models in Clementinep. 64
Evaluating the Potential Performance of Machine Learning for Detection of High-Risk Individualsp. 68
Summary and Conclusionsp. 70
Referencesp. 70
Outcome Prediction of Oesophago-Gastric Cancer Using Neural Analysis of Pre- and Postoperative Parametersp. 73
Introductionp. 73
Artificial Neural Networks for the Prediction of Outcome of Oesophago-Gastric Cancerp. 74
Referencep. 90
Artifical Neural Networks in Urologic Oncologyp. 93
Introductionp. 93
Kidney Cancerp. 94
Bladder Cancerp. 95
Prostate Cancerp. 95
Testicular Cancerp. 98
Discussionp. 98
Referencesp. 100
Neural Networks in Urologic Oncologyp. 103
Introductionp. 103
Renal Cell Carcinomap. 103
Prostate Cancerp. 107
Bladder Cancerp. 110
Testicular Carcinomap. 111
Conclusionp. 111
Referencesp. 112
Comparison of a Neural Network with High Sensitivity and Specificity of Free/Total Serum PSA for Diagnosing Prostate Cancer in Men with a PSA [ 4.0 ng/mLp. 115
Introductionp. 115
Material and Methodsp. 116
Patient Selectionp. 116
Laboratory Analysisp. 116
Neural Network Inputs and Derivation of the PIp. 116
Resultsp. 116
Commentp. 122
Referencesp. 124
Artificial Neural Networks and Prognosis in Prostate Cancerp. 125
Introductionp. 125
Prostate Cancerp. 126
Patients and Methodsp. 126
Patientsp. 126
Methodsp. 126
Immunohistochemistryp. 126
Artificial Neural Networksp. 127
Statistical Analysisp. 127
Resultsp. 127
Immunohistochemistryp. 127
Neural Network Analysisp. 127
Comparison of ANNs with Statistical Analysisp. 129
Summaryp. 130
Referencesp. 131
Comparison between Urologists and Artificial Neural Networks in Bladder Cancer Outcome Predictionp. 133
Introductionp. 133
Materials and Methodsp. 135
Resultsp. 136
Clinical Outcome: Recurrence, Progression, and Survivalp. 136
Predictions by the ANN and Cliniciansp. 136
Discussionp. 138
Referencesp. 139
A Probabilistic Neural Network Framework for the Detection of Malignant Melanomap. 141
Introductionp. 141
Malignant Melanomap. 141
Evolution of Malignant Melanomap. 142
Image Acquistion Techniquesp. 142
Traditional Imagingp. 142
Dermatoscopic Imagingp. 142
Dermatoscopic Featuresp. 143
Feature Extraction in Dermatoscopic Imagesp. 145
Image Acquisitionp. 145
Image Preprocessingp. 146
Median Filteringp. 147
Karhunen-Loeve Transformp. 148
Image Segmentationp. 149
Optimal Thresholdingp. 149
Dermatoscopic Feature Descriptionp. 152
Asymmetryp. 152
Edge Abruptnessp. 154
Colourp. 156
A Probabalistic Framework for Classificationp. 160
Bayes' Decision Theoryp. 160
Measuring Model Preformancep. 161
Cross-Entropy Error Function for Multiple Classesp. 163
Measuring Generalisation Performancep. 163
Empirical Estimatesp. 164
Algebraic Estimatesp. 165
Controlling Model Complexityp. 165
Weight Decay Regularisationp. 166
Optimal Brain Damage Pruningp. 166
Neural Classifier Modellingp. 167
Multilayer Perception Architecturep. 168
Softmax Normalisationp. 168
Modified Softmax Normalisationp. 169
Estimating Model Parametersp. 170
Gradient Descent Optimisationp. 172
Newton Optimisationp. 172
Overview of Design Algorithmp. 173
Experimentsp. 173
Experimental Setupp. 173
Resultsp. 174
Classifier Resultsp. 174
Dermatoscopic Feature Importancep. 178
Conclusionsp. 180
Dermatoscopic Feature Extractionp. 180
Probabilistic Framework for Classificationp. 180
Neural Classifier Modellingp. 180
The Malignant Melanoma Classification Problemp. 181
Referencesp. 181
Indexp. 185
Table of Contents provided by Syndetics. All Rights Reserved.

ISBN: 9780849396922
ISBN-10: 0849396921
Series: Biomedical Engineering Series
Audience: Professional
Format: Hardcover
Language: English
Number Of Pages: 216
Published: 22nd June 2001
Publisher: CRC PR INC
Country of Publication: US
Dimensions (cm): 25.4 x 18.42  x 1.91
Weight (kg): 0.6
Edition Number: 1

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