Outcome prediction in cancer /:
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...
Gespeichert in:
| Weitere Verfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Amsterdam ; Boston :
Elsevier,
©2007.
|
| Ausgabe: | 1st ed. |
| Schlagworte: | |
| Online-Zugang: | Volltext Volltext |
| Zusammenfassung: | 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 r̥le 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. |
| Beschreibung: | 1 online resource (xx, 461 pages) : illustrations |
| Bibliographie: | Includes bibliographical references and index. |
| ISBN: | 9780444528551 0444528555 9780080468037 0080468039 |
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| 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 r̥le 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. | ||
| 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 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. | |
| 504 | |a Includes bibliographical references and index. | ||
| 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. | |
| 588 | 0 | |a Print version record. | |
| 650 | 0 | |a Cancer |x Diagnosis. |0 http://id.loc.gov/authorities/subjects/sh85019501 | |
| 650 | 0 | |a Cancer |x Prognosis. |0 http://id.loc.gov/authorities/subjects/sh85019529 | |
| 650 | 0 | |a Neural networks (Computer science) |0 http://id.loc.gov/authorities/subjects/sh90001937 | |
| 650 | 0 | |a Survival analysis (Biometry) |0 http://id.loc.gov/authorities/subjects/sh90003967 | |
| 650 | 0 | |a Prognosis. |0 http://id.loc.gov/authorities/subjects/sh85107299 | |
| 650 | 1 | 2 | |a Neoplasms |x diagnosis |
| 650 | 1 | 2 | |a Prognosis |
| 650 | 2 | 2 | |a Decision Support Systems, Clinical |
| 650 | 2 | 2 | |a Neural Networks, Computer |
| 650 | 2 | 2 | |a Survival Analysis |
| 650 | 6 | |a Cancer |x Diagnostic. | |
| 650 | 6 | |a Cancer |x Pronostic. | |
| 650 | 6 | |a Réseaux neuronaux (Informatique) | |
| 650 | 6 | |a Analyse de survie (Biométrie) | |
| 650 | 6 | |a Pronostics (Pathologie) | |
| 650 | 7 | |a MEDICAL |x Oncology. |2 bisacsh | |
| 650 | 7 | |a HEALTH & FITNESS |x Diseases |x Cancer. |2 bisacsh | |
| 650 | 7 | |a Prognosis |2 fast | |
| 650 | 7 | |a Cancer |x Diagnosis |2 fast | |
| 650 | 7 | |a Cancer |x Prognosis |2 fast | |
| 650 | 7 | |a Neural networks (Computer science) |2 fast | |
| 650 | 7 | |a Survival analysis (Biometry) |2 fast | |
| 655 | 0 | |a Electronic books. | |
| 655 | 4 | |a Computer network resources. | |
| 655 | 7 | |a dissertations. |2 aat | |
| 655 | 7 | |a Academic theses |2 fast | |
| 655 | 7 | |a Academic theses. |2 lcgft |0 http://id.loc.gov/authorities/genreForms/gf2014026039 | |
| 655 | 7 | |a Thèses et écrits académiques. |2 rvmgf | |
| 700 | 1 | |a Taktak, Azzam F. G. |0 http://id.loc.gov/authorities/names/n2007180817 | |
| 700 | 1 | |a Fisher, Anthony C., |c Dr. |1 https://id.oclc.org/worldcat/entity/E39PCjrx4cDr8MbDqt74RmjgXb |0 http://id.loc.gov/authorities/names/n2007180983 | |
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| contents | 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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2007eb</subfield></datafield><datafield tag="060" ind1=" " ind2="4"><subfield code="a">2007 B-597</subfield></datafield><datafield tag="060" ind1=" " ind2="4"><subfield code="a">QZ 241</subfield><subfield code="b">O94 2007</subfield></datafield><datafield tag="072" ind1=" " ind2="7"><subfield code="a">RC</subfield><subfield code="2">lcco</subfield></datafield><datafield tag="072" ind1=" " ind2="7"><subfield code="a">MED</subfield><subfield code="x">062000</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="072" ind1=" " ind2="7"><subfield code="a">HEA</subfield><subfield code="x">039030</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="082" ind1="7" ind2=" "><subfield code="a">616.99/4075</subfield><subfield code="2">22</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">MAIN</subfield></datafield><datafield tag="245" ind1="0" ind2="0"><subfield code="a">Outcome prediction in cancer /</subfield><subfield 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Taktak and Anthony C. Fisher.</subfield></datafield><datafield tag="250" ind1=" " ind2=" "><subfield code="a">1st ed.</subfield></datafield><datafield tag="260" ind1=" " ind2=" "><subfield code="a">Amsterdam ;</subfield><subfield code="a">Boston :</subfield><subfield code="b">Elsevier,</subfield><subfield code="c">©2007.</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 online resource (xx, 461 pages) :</subfield><subfield code="b">illustrations</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">computer</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">online resource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="347" ind1=" " ind2=" "><subfield code="a">text file</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="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 r̥le 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.</subfield></datafield><datafield tag="505" ind1="0" ind2=" "><subfield code="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 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. 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| genre | Electronic books. Computer network resources. dissertations. aat Academic theses fast Academic theses. lcgft http://id.loc.gov/authorities/genreForms/gf2014026039 Thèses et écrits académiques. rvmgf |
| genre_facet | Electronic books. Computer network resources. dissertations. Academic theses Academic theses. Thèses et écrits académiques. |
| id | ZDB-4-EBA-ocn162131472 |
| illustrated | Illustrated |
| indexdate | 2024-11-27T13:16:06Z |
| institution | BVB |
| isbn | 9780444528551 0444528555 9780080468037 0080468039 |
| language | English |
| oclc_num | 162131472 |
| open_access_boolean | |
| owner | MAIN DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource (xx, 461 pages) : illustrations |
| psigel | ZDB-4-EBA |
| publishDate | 2007 |
| publishDateSearch | 2007 |
| publishDateSort | 2007 |
| publisher | Elsevier, |
| record_format | marc |
| spelling | Outcome prediction in cancer / editors, Azzam F.G. Taktak and Anthony C. Fisher. 1st ed. Amsterdam ; Boston : Elsevier, ©2007. 1 online resource (xx, 461 pages) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier text file 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 r̥le 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. 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. Includes bibliographical references and index. 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. Print version record. Cancer Diagnosis. http://id.loc.gov/authorities/subjects/sh85019501 Cancer Prognosis. http://id.loc.gov/authorities/subjects/sh85019529 Neural networks (Computer science) http://id.loc.gov/authorities/subjects/sh90001937 Survival analysis (Biometry) http://id.loc.gov/authorities/subjects/sh90003967 Prognosis. http://id.loc.gov/authorities/subjects/sh85107299 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. bisacsh HEALTH & FITNESS Diseases Cancer. bisacsh Prognosis fast Cancer Diagnosis fast Cancer Prognosis fast Neural networks (Computer science) fast Survival analysis (Biometry) fast Electronic books. Computer network resources. dissertations. aat Academic theses fast Academic theses. lcgft http://id.loc.gov/authorities/genreForms/gf2014026039 Thèses et écrits académiques. rvmgf Taktak, Azzam F. G. http://id.loc.gov/authorities/names/n2007180817 Fisher, Anthony C., Dr. https://id.oclc.org/worldcat/entity/E39PCjrx4cDr8MbDqt74RmjgXb http://id.loc.gov/authorities/names/n2007180983 has work: Outcome prediction in cancer (Text) https://id.oclc.org/worldcat/entity/E39PCFPT4HVXMP646HBGTM8pyd https://id.oclc.org/worldcat/ontology/hasWork Print version: Outcome prediction in cancer. 1st ed. Amsterdam ; Boston : Elsevier, ©2007 9780444528551 0444528555 (OCoLC)77482420 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=185784 Volltext FWS01 ZDB-4-EBA FWS_PDA_EBA https://www.sciencedirect.com/science/book/9780444528551 Volltext |
| spellingShingle | Outcome prediction in cancer / 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. Cancer Diagnosis. http://id.loc.gov/authorities/subjects/sh85019501 Cancer Prognosis. http://id.loc.gov/authorities/subjects/sh85019529 Neural networks (Computer science) http://id.loc.gov/authorities/subjects/sh90001937 Survival analysis (Biometry) http://id.loc.gov/authorities/subjects/sh90003967 Prognosis. http://id.loc.gov/authorities/subjects/sh85107299 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. bisacsh HEALTH & FITNESS Diseases Cancer. bisacsh Prognosis fast Cancer Diagnosis fast Cancer Prognosis fast Neural networks (Computer science) fast Survival analysis (Biometry) fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85019501 http://id.loc.gov/authorities/subjects/sh85019529 http://id.loc.gov/authorities/subjects/sh90001937 http://id.loc.gov/authorities/subjects/sh90003967 http://id.loc.gov/authorities/subjects/sh85107299 http://id.loc.gov/authorities/genreForms/gf2014026039 |
| title | Outcome prediction in cancer / |
| title_auth | Outcome prediction in cancer / |
| title_exact_search | Outcome prediction in cancer / |
| title_full | Outcome prediction in cancer / editors, Azzam F.G. Taktak and Anthony C. Fisher. |
| title_fullStr | Outcome prediction in cancer / editors, Azzam F.G. Taktak and Anthony C. Fisher. |
| title_full_unstemmed | Outcome prediction in cancer / editors, Azzam F.G. Taktak and Anthony C. Fisher. |
| title_short | Outcome prediction in cancer / |
| title_sort | outcome prediction in cancer |
| topic | Cancer Diagnosis. http://id.loc.gov/authorities/subjects/sh85019501 Cancer Prognosis. http://id.loc.gov/authorities/subjects/sh85019529 Neural networks (Computer science) http://id.loc.gov/authorities/subjects/sh90001937 Survival analysis (Biometry) http://id.loc.gov/authorities/subjects/sh90003967 Prognosis. http://id.loc.gov/authorities/subjects/sh85107299 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. bisacsh HEALTH & FITNESS Diseases Cancer. bisacsh Prognosis fast Cancer Diagnosis fast Cancer Prognosis fast Neural networks (Computer science) fast Survival analysis (Biometry) fast |
| topic_facet | 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. Cancer Diagnosis Cancer Prognosis Electronic books. Computer network resources. dissertations. Academic theses Academic theses. Thèses et écrits académiques. |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=185784 https://www.sciencedirect.com/science/book/9780444528551 |
| work_keys_str_mv | AT taktakazzamfg outcomepredictionincancer AT fisheranthonyc outcomepredictionincancer |