Outcome prediction in cancer /

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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...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Taktak, Azzam F. G., Fisher, Anthony C., Dr
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Amsterdam ; Boston : Elsevier, ©2007.
Edición:1st ed.
Temas:
Cancer > Diagnosis.
Cancer > Prognosis.
Neural networks (Computer science)
Survival analysis (Biometry)
Prognosis.
Neoplasms > diagnosis
Prognosis
Decision Support Systems, Clinical
Neural Networks, Computer
Survival Analysis
Cancer > Diagnostic.
Cancer > Pronostic.
Réseaux neuronaux (Informatique)
Analyse de survie (Biométrie)
Pronostics (Pathologie)
MEDICAL > Oncology.
HEALTH & FITNESS > Diseases > Cancer.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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 Universit̉ 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
  • 1Universit̉ 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. Buydens3
  • 1Katholieke Universiteit Leuven, Belgium
  • 2University Nijmegen Medical Centre, The Netherlands
  • 3Radboud University Nijmegen, The Netherlands
  • Chapter 12: Towards automatic risk analysis for hereditary non-polyposis colorectal cancer based on pedigree data.
  • M. Kokuer1, R.N.G. Naguib1, P. Jancovic2, H.B. Younghusband3, R. Green3
  • 1Coventry University, UK
  • 2University of Birmingham, UK
  • 3University of Newfoundland, Canada
  • Chapter 13: The impact of microarray technology in brain cancer.
  • M. Kounelakis1, M. Zervakis1, X. Kotsiakis2
  • 1Technical University of Crete, GREECE
  • 2District Hospital of Chania, GREECE
  • Section 5 Dissemination of Information
  • Chapter 14: The web and the new generation of medical information.
  • J.M. Fonseca, A.D. Mora, P. Barroso
  • University of Lisbon, Portugal
  • Chapter 15: Geoconda: a web environment for multi-centre research.
  • C. Setzkorn, A.F.G. Taktak, B.E. Damato
  • Royal Liverpool University Hospital, Liverpool, UK
  • Chapter 16: The development and execution of medical prediction models.
  • M.W. Kattan1, M. G̲nen2, P.T. Scardino2
  • 1The Cleveland Clinic Fondation, Cleveland, USA
  • 2Memorial Sloan-Kettering Cancer Center, New York, USA.
  • The predictive value of detailed histological staging of surgical resection specimens in oral cancer
  • Survival after treatment of intraocular melanoma
  • Recent developments in relative survival analysis
  • Environmental and genetic risk factors of lung cancer
  • Chaos, cancer, the cellular operating system and the prediction of survival in head and neck cancer
  • Flexible hazard modelling for outcome prediction in cancer: perspectives for the use of bioinformatics knowledge
  • Information geometry for survival analysis and feature selection by neural networks
  • Artificial neural networks used in the survival analysis of breast cancer patients: a node-negative study
  • The use of artificial neural networks for the diagnosis and estimation of prognosis in cancer patients
  • Machine learning contribution to solve prognostic medical problems
  • Classification of brain tumors by pattern recognition of magnetic resonance imaging and spectroscopic data
  • Towards automatic risk analysis for hereditary non-polyposis colorectal cancer based on pedigree data
  • The impact of microarray technology in brain cancer
  • The web and the new generation of medical information systems
  • Geoconda: a web environment for multi-centre research
  • The development and execution of medical prediction models.

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