Outcome Prediction in Cancer

Informationen zum Autor Azzam Taktak is a Principal Clinical Scientist in the Department of Clinical Engineering, Royal Liverpool University Hospital and an Honorary Lecturer at the University of Liverpool. His main research interests are the application of mathematical models and artificial intelligence to medical applications specifically in cancer. Anthony Fisher is a Consultant Clinical Scientist in the Department of Clinical Engineering, Royal Liverpool University Hospital. Previously he was a Senior Lecturer in Bioengineering at the University of Strathclyde. Glasgow. His principal academic interests are biomedical instrumentation and signal processing. Klappentext This book is organized into 4 sections, each looking at the question of outcome prediction in cancer from a different angle. The first section describes the clinical problem and some of the predicaments that clinicians face in dealing with cancer. Amongst issues discussed in this section are the TNM staging, accepted methods for survival analysis and competing risks. The second section describes the biological and genetic markers and the rôle of bioinformatics. Understanding of the genetic and environmental basis of cancers will help in identifying high-risk populations and developing effective prevention and early detection strategies. The third section provides technical details of mathematical analysis behind survival prediction backed up by examples from various types of cancers. The fourth section describes a number of machine learning methods which have been applied to decision support in cancer. The final section describes how information is shared within the scientific and medical communities and with the general population using information technology and the World Wide Web. Zusammenfassung Organized into 4 sections! each looking at the question of outcome prediction in cancer from a different angle. This work describes the clinical problem and some of the predicaments that clinicians face in dealing with cancer. It discusses a number of machine learning methods which have been applied to decision support in cancer. Inhaltsverzeichnis Section 1 - The Clinical Problem. THE PREDICTIVE VALUE OF DETAILED HISTOLOGICAL STAGING OF SURGICAL RESECTION SPECIMENS IN ORAL CANCER Chapter 1: The predictive value of detailed histological staging of surgical resection specimens in oral cancer. J. Woolgar Liverpool Dental School, UK Chapter 2: Survival after Treatment of Intraocular Melanoma. B.E. Damato, A.F.G. Taktak, Royal Liverpool University Hospital, UK Chapter 3: Recent developments in relative survival analysis. T. Hakulinen, T.A. Dyba, Finnish Cancer Registry Section 2 - Biological and Genetic Factors Chapter 4: Environmental and genetic risk factors of lung cancer. A. Cassidy, J.K. Field, University of Liverpool, UK Chapter 5: Chaos, cancer, the cellular operating system and the prediction of survival in head and neck cancer. A.S. Jones, University Hospital Aintree, UK Section 3 - Mathematical Background of Prognostic Models Chapter 6: Flexible hazard modelling for outcome prediction in cancer - perspectives for the use of bioinformatics knowledge. E.Biganzoli1, P. Boracchi2 1 Istituto Nazionale per lo Studio e la Cura dei Tumori, Milano, Italy 2 Università degli Studi di Milano, Milano, Italy Chapter 7: Information geometry for survival analysis and feature selection by neural networks. A. Eleuteri 1,2, R. Tagliaferri 3,4, L. Milano 1,2, M. De Laurentiis 1 1Università di Napoli, Italy 2INFN sez. Napoli, Italy 3Universit`a di Salerno, Italy 4INFN sez. distaccata di Salerno, Italy Chapter 8: Artificial neural networks used in the survival analysis of breast cancer patients: A node negative study. C.T.C. Arsene, P.J. Lisboa, Liverpool John Moores University, UK...

Autorentext
Azzam Taktak is a Principal Clinical Scientist in the Department of Clinical Engineering, Royal Liverpool University Hospital and an Honorary Lecturer at the University of Liverpool. His main research interests are the application of mathematical models and artificial intelligence to medical applications specifically in cancer. Anthony Fisher is a Consultant Clinical Scientist in the Department of Clinical Engineering, Royal Liverpool University Hospital. Previously he was a Senior Lecturer in Bioengineering at the University of Strathclyde. Glasgow. His principal academic interests are biomedical instrumentation and signal processing.

Klappentext
This book is organized into 4 sections, each looking at the question of outcome prediction in cancer from a different angle. The first section describes the clinical problem and some of the predicaments that clinicians face in dealing with cancer. Amongst issues discussed in this section are the TNM staging, accepted methods for survival analysis and competing risks. The second section describes the biological and genetic markers and the rôle of bioinformatics. Understanding of the genetic and environmental basis of cancers will help in identifying high-risk populations and developing effective prevention and early detection strategies. The third section provides technical details of mathematical analysis behind survival prediction backed up by examples from various types of cancers. The fourth section describes a number of machine learning methods which have been applied to decision support in cancer. The final section describes how information is shared within the scientific and medical communities and with the general population using information technology and the World Wide Web.

Inhalt

Section 1 - The Clinical Problem.

THE PREDICTIVE VALUE OF DETAILED HISTOLOGICAL STAGING OF SURGICAL RESECTION SPECIMENS IN ORAL CANCER

Chapter 1: The predictive value of detailed histological staging of surgical resection specimens in oral cancer.
J. Woolgar
Liverpool Dental School, UK

Chapter 2: Survival after Treatment of Intraocular Melanoma.
B.E. Damato, A.F.G. Taktak,
Royal Liverpool University Hospital, UK

Chapter 3: Recent developments in relative survival analysis.
T. Hakulinen, T.A. Dyba,
Finnish Cancer Registry

Section 2 - Biological and Genetic Factors

Chapter 4: Environmental and genetic risk factors of lung cancer.
A. Cassidy, J.K. Field,
University of Liverpool, UK

Chapter 5: Chaos, cancer, the cellular operating system and the prediction of survival in head and neck cancer.
A.S. Jones,
University Hospital Aintree, UK

Section 3 - Mathematical Background of Prognostic Models

Chapter 6: Flexible hazard modelling for outcome prediction in cancer - perspectives for the use of bioinformatics knowledge.
E.Biganzoli1, P. Boracchi2
1 Istituto Nazionale per lo Studio e la Cura dei Tumori, Milano, Italy
2 Università degli Studi di Milano, Milano, Italy

Chapter 7: Information geometry for survival analysis and feature selection by neural networks.
A. Eleuteri 1,2, R. Tagliaferri 3,4, L. Milano 1,2, M. De Laurentiis 1
1Università di Napoli, Italy
2INFN sez. Napoli, Italy
3Universit`a di Salerno, Italy
4INFN sez. distaccata di Salerno, Italy

Chapter 8: Artificial neural networks used in the survival analysis of breast cancer patients: A node negative study.
C.T.C. Arsene, P.J. Lisboa,
Liverpool John Moores University, UK

Section 4 - Application of Machine Learning Methods

Chapter 9: The use of artificial neural networks for the diagnosis and estimation of prognosis in cancer patients.
A. Marchevsky,
Cedars-Sinai Medical Center, Los Angeles, USA

Chapter 10: Machine learning contribution to solve prognosis medical problems.
F. Baronti, A. Micheli, A. Passaro, A.Starita,
University of Pisa, Italy

Chapter 11: Classification of brain tumours by pattern recognition of Magnetic Resonance Imaging and Spectroscopic data.
A. Devos1, S. Van Huffel1 A.W. Simonetti1, M. van der Graaf2, A. Heerschap2, L.M.C. Buyden…