{"product_id":"outcome-prediction-in-cancer-hardback-9780444528551","title":"Outcome Prediction in Cancer (Hardback) 9780444528551","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eOutcome Prediction in Cancer\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cem\u003eA multi-disciplinary book addressing the question of outcome prediction in cancer addressing topics on clinical medicine, mathematics, biology, and bioinformatics.\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eAzzam F.G. Taktak (Edited by), Anthony C. Fisher (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780444528551, Elsevier Science\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 28 November 2006\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e482 pages\u003cbr\u003e24 x 16.5 x 2.9 cm, 1.01 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003eThis 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.\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSection 1 – The Clinical Problem.\u003cbr\u003e\u003cbr\u003eTHE PREDICTIVE VALUE OF DETAILED HISTOLOGICAL STAGING OF SURGICAL RESECTION SPECIMENS IN ORAL CANCER\u003cbr\u003e\u003cbr\u003eChapter 1: The predictive value of detailed histological staging of surgical resection specimens in oral cancer. \u003cbr\u003eJ. Woolgar \u003cbr\u003eLiverpool Dental School, UK\u003cbr\u003e\u003cbr\u003eChapter 2: Survival after Treatment of Intraocular Melanoma. \u003cbr\u003eB.E. Damato, A.F.G. Taktak, \u003cbr\u003eRoyal Liverpool University Hospital, UK \u003cbr\u003e\u003cbr\u003eChapter 3: Recent developments in relative survival analysis. \u003cbr\u003eT. Hakulinen, T.A. Dyba, \u003cbr\u003eFinnish Cancer Registry\u003cbr\u003e\u003cbr\u003eSection 2 – Biological and Genetic Factors\u003cbr\u003e\u003cbr\u003eChapter 4: Environmental and genetic risk factors of lung cancer. \u003cbr\u003eA. Cassidy, J.K. Field, \u003cbr\u003eUniversity of Liverpool, UK\u003cbr\u003e\u003cbr\u003eChapter 5: Chaos, cancer, the cellular operating system and the prediction of survival in head and neck cancer. \u003cbr\u003eA.S. Jones, \u003cbr\u003eUniversity Hospital Aintree, UK\u003cbr\u003e\u003cbr\u003eSection 3 – Mathematical Background of Prognostic Models\u003cbr\u003e\u003cbr\u003eChapter 6: Flexible hazard modelling for outcome prediction in cancer - perspectives for the use of bioinformatics knowledge.\u003cbr\u003eE.Biganzoli1, P. Boracchi2 \u003cbr\u003e1 Istituto Nazionale per lo Studio e la Cura dei Tumori, Milano, Italy\u003cbr\u003e2 Università degli Studi di Milano, Milano, Italy\u003cbr\u003e\u003cbr\u003eChapter 7: Information geometry for survival analysis and feature selection by neural networks. \u003cbr\u003eA. Eleuteri 1,2, R. Tagliaferri 3,4, L. Milano 1,2, M. De Laurentiis 1\u003cbr\u003e 1Università di Napoli, Italy\u003cbr\u003e2INFN sez. Napoli, Italy\u003cbr\u003e3Universit`a di Salerno, Italy\u003cbr\u003e4INFN sez. distaccata di Salerno, Italy\u003cbr\u003e\u003cbr\u003eChapter 8: Artificial neural networks used in the survival analysis of breast cancer patients: A node negative study. \u003cbr\u003eC.T.C. Arsene, P.J. Lisboa, \u003cbr\u003eLiverpool John Moores University, UK\u003cbr\u003e\u003cbr\u003eSection 4 – Application of Machine Learning Methods \u003cbr\u003e\u003cbr\u003eChapter 9: The use of artificial neural networks for the diagnosis and estimation of prognosis in cancer patients. \u003cbr\u003eA. Marchevsky, \u003cbr\u003eCedars-Sinai Medical Center, Los Angeles, USA\u003cbr\u003e\u003cbr\u003eChapter 10: Machine learning contribution to solve prognosis medical problems. \u003cbr\u003eF. Baronti, A. Micheli, A. Passaro, A.Starita,\u003cbr\u003eUniversity of Pisa, Italy\u003cbr\u003e\u003cbr\u003eChapter 11: Classification of brain tumours by pattern recognition of Magnetic Resonance Imaging and Spectroscopic data.\u003cbr\u003eA. Devos1, S. Van Huffel1  A.W. Simonetti1, M. van der Graaf2, A. Heerschap2, L.M.C. Buydens3 \u003cbr\u003e1Katholieke Universiteit Leuven, Belgium\u003cbr\u003e2University Nijmegen Medical Centre, The Netherlands\u003cbr\u003e3Radboud University Nijmegen, The Netherlands\u003cbr\u003e \u003cbr\u003eChapter 12: Towards automatic risk analysis for hereditary non-polyposis colorectal cancer based on pedigree data.\u003cbr\u003eM. Kokuer1, R.N.G. Naguib1, P. Jancovic2, H.B. Younghusband3, R. Green3\u003cbr\u003e1Coventry University, UK\u003cbr\u003e2University of Birmingham, UK\u003cbr\u003e3University of Newfoundland, Canada\u003cbr\u003e\u003cbr\u003eChapter 13: The impact of microarray technology in brain cancer.\u003cbr\u003eM. Kounelakis1, M. Zervakis1, X. Kotsiakis2\u003cbr\u003e1Technical University of Crete, GREECE\u003cbr\u003e2District Hospital of Chania, GREECE\u003cbr\u003e\u003cbr\u003eSection 5 – Dissemination of Information\u003cbr\u003e\u003cbr\u003eChapter 14: The web and the new generation of medical information. \u003cbr\u003eJ.M. Fonseca, A.D. Mora, P. Barroso\u003cbr\u003eUniversity of Lisbon, Portugal\u003cbr\u003e\u003cbr\u003eChapter 15: Geoconda: a web environment for multi-centre research.\u003cbr\u003eC. Setzkorn, A.F.G. Taktak, B.E. Damato\u003cbr\u003eRoyal Liverpool University Hospital, Liverpool, UK\u003cbr\u003e\u003cbr\u003eChapter 16: The development and execution of medical prediction models. \u003cbr\u003eM.W. Kattan1, M. Gönen2, P.T. Scardino2\u003cbr\u003e1The Cleveland Clinic Fondation, Cleveland, USA\u003cbr\u003e2Memorial Sloan-Kettering Cancer Center, New York, USA\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Oncology [\u003ca title=\"See our other books on Oncology\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Oncology%20%5BMJCL%5D%22\"\u003eMJCL\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Elsevier Science","offers":[{"title":"Default Title","offer_id":46651489583384,"sku":"9780444528551","price":88.88,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9780444528551.jpg?v=1694980031","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/outcome-prediction-in-cancer-hardback-9780444528551","provider":"Freshly Printed Books","version":"1.0","type":"link"}