LEADER 04008nam 22006375 450 001 9910300082703321 005 20200702022427.0 010 $a3-642-37102-7 024 7 $a10.1007/978-3-642-37102-8 035 $a(CKB)3710000000093959 035 $a(EBL)1698202 035 $a(OCoLC)881166021 035 $a(SSID)ssj0001187103 035 $a(PQKBManifestationID)11702538 035 $a(PQKBTitleCode)TC0001187103 035 $a(PQKBWorkID)11257653 035 $a(PQKB)10966086 035 $a(MiAaPQ)EBC1698202 035 $a(DE-He213)978-3-642-37102-8 035 $a(PPN)17782493X 035 $a(EXLCZ)993710000000093959 100 $a20140313d2014 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aDecision Tools for Radiation Oncology $ePrognosis, Treatment Response and Toxicity /$fedited by Carsten Nieder, Laurie E. Gaspar 205 $a1st ed. 2014. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2014. 215 $a1 online resource (307 p.) 225 1 $aRadiation Oncology 300 $aDescription based upon print version of record. 311 $a3-642-37101-9 320 $aIncludes bibliographical references at the end of each chapters and index. 327 $aPrognosis and Predictive Factors for Tumours and Organs at risk: Background and Purpose -- Specific issues for prognostic factors related to radiotherapy -- Role of ICT in decision models -- Statistics of Prediction of survival and toxicity and Nomogram Development -- Treatment decisions based on Gene Signatures: Methods and Caveats -- Brain tumors -- Head and neck cancer -- Breast cancer -- Lung cancer -- Esophageal cancer -- Gastric cancer -- Pancreas and biliary tract cancer -- Liver cancer and metastases -- Rectal and anal cancer -- Cervix and corpus uteri, vulva and vaginal cancers -- Bladder cancer -- Prostate cancer -- Sarcomas -- Lymphomas -- Brain metastases -- Bone metastases. 330 $aA look at the recent oncology literature or a search of one of the common databases reveals a steadily increasing number of nomograms and other prognostic models, some of which are also available in the form of web-based tools. These models may predict the risk of relapse, lymphatic spread of a given malignancy, toxicity, survival, etc. Pathology information, gene signatures, and clinical data may all be used to compute the models. This trend reflects increasingly individualized treatment concepts and also the need for approaches that achieve a favorable balance between effectiveness and side-effects. Moreover, optimal resource utilization requires prognostic knowledge, for example to avoid lengthy and aggressive treatment courses in patients with a short survival expectation. In order to avoid misuse, it is important to understand the limits and caveats of prognostic and predictive models. This book provides a comprehensive overview of such decision tools for radiation oncology, stratified by disease site, which will enable readers to make informed choices in daily clinical practice and to critically follow the future development of new tools in the field. 410 0$aRadiation Oncology 606 $aRadiotherapy 606 $aOncology   606 $aRadiotherapy$3https://scigraph.springernature.com/ontologies/product-market-codes/H29056 606 $aOncology$3https://scigraph.springernature.com/ontologies/product-market-codes/H33160 615 0$aRadiotherapy. 615 0$aOncology  . 615 14$aRadiotherapy. 615 24$aOncology. 676 $a616.9940642 702 $aNieder$b Carsten$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aGaspar$b Laurie E$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910300082703321 996 $aDecision Tools for Radiation Oncology$91522879 997 $aUNINA