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Outcome prediction in cancer

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Outcome prediction in cancer/ editors, Azzam F.G. Taktak and Anthony C. Fisher.
other author:	Taktak, Azzam F. G.
Published:	Amsterdam ;Elsevier, : c2007.,
Description:	xx, 461 p. :ill. ; : 25 cm.;
Subject:	Cancer - Diagnosis. -
Online resource:	An electronic book accessible through the World Wide Web; click for information
ISBN:	9780444528551

Outcome prediction in cancer
Outcome prediction in cancer[electronic resource] /editors, Azzam F.G. Taktak and Anthony C. Fisher. - 1st ed. - Amsterdam ;Elsevier,c2007. - xx, 461 p. :ill. ;25 cm.

Includes bibliographical references and index.

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 Universi�t 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 -- 1Universi�t 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. �Gnen2, P.T. Scardino2 -- 1The Cleveland Clinic Fondation, Cleveland, USA -- 2Memorial Sloan-Kettering Cancer Center, New York, USA.

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 �rle 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. * Applications cover 8 types of cancer including brain, eye, mouth, head and neck, breast, lungs, colon and prostate * Include contributions from authors in 5 different disciplines * Provides a valuable educational tool for medical informatics.

Electronic reproduction.
Amsterdam :
Elsevier Science & Technology,
2007.

Mode of access: World Wide Web.

ISBN: 9780444528551

Source: 134303:134431Elsevier Science & Technologyhttp://www.sciencedirect.comSubjects--Topical Terms:

661324
Cancer
--Diagnosis.Index Terms--Genre/Form:

554714
Electronic books.

LC Class. No.: RC270 / .O98 2007eb

Dewey Class. No.: 616.99/4075

National Library of Medicine Call No.: 2007 B-597

Outcome prediction in cancer
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245 0 0 $a Outcome prediction in cancer $h [electronic resource] / $c editors, Azzam F.G. Taktak and Anthony C. Fisher.
250 $a 1st ed.
260 $a Amsterdam ; $a Boston : $b Elsevier, $c c2007.
300 $a xx, 461 p. : $b ill. ; $c 25 cm.
504 $a Includes bibliographical references and index.
505 0 $a 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 Universi�t 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 -- 1Universi�t 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. �Gnen2, P.T. Scardino2 -- 1The Cleveland Clinic Fondation, Cleveland, USA -- 2Memorial Sloan-Kettering Cancer Center, New York, USA.
505 0 $a 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.
520 $a 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 �rle 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. * Applications cover 8 types of cancer including brain, eye, mouth, head and neck, breast, lungs, colon and prostate * Include contributions from authors in 5 different disciplines * Provides a valuable educational tool for medical informatics.
533 $a Electronic reproduction. $b Amsterdam : $c Elsevier Science & Technology, $d 2007. $n Mode of access: World Wide Web. $n System requirements: Web browser. $n Title from title screen (viewed on July 25, 2007). $n Access may be restricted to users at subscribing institutions.
650 0 $a Cancer $x Diagnosis. $3 661324
650 0 $a Cancer $x Prognosis. $3 791557
650 0 $a Neural networks (Computer science) $3 528588
650 0 $a Survival analysis (Biometry) $3 528451
650 1 2 $a Neoplasms $x diagnosis. $3 581039
650 1 2 $a Prognosis. $3 791558
650 2 2 $a Decision Support Systems, Clinical. $3 791559
650 2 2 $a Neural Networks (Computer) $3 654860
650 2 2 $a Survival Analysis. $3 791560
655 7 $a Electronic books. $2 local $3 554714
700 1 $a Taktak, Azzam F. G. $3 791555
700 1 $a Fisher, Anthony C., $c Dr. $3 791556
710 2 $a ScienceDirect (Online service) $3 636041
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