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020 _a9783642371028
_9978-3-642-37102-8
024 7 _a10.1007/978-3-642-37102-8
_2doi
035 _ato000544473
040 _aSpringer
_cSpringer
_dRU-ToGU
050 4 _aRM845-862.5
072 7 _aMJCL1
_2bicssc
072 7 _aMED080000
_2bisacsh
082 0 4 _a615.842
_223
245 1 0 _aDecision Tools for Radiation Oncology
_helectronic resource
_bPrognosis, Treatment Response and Toxicity /
_cedited by Carsten Nieder, Laurie E. Gaspar.
260 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2014.
300 _aXIII, 305 p. 90 illus., 45 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
490 1 _aMedical Radiology,
_x0942-5373
505 0 _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.
520 _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.
650 0 _amedicine.
_9566220
650 0 _aRadiology, Medical.
_9303290
650 0 _aRadiotherapy.
_9307053
650 0 _aOncology.
_9303086
650 1 4 _aMedicine & Public Health.
_9566221
650 2 4 _aRadiotherapy.
_9307053
650 2 4 _aImaging / Radiology.
_9281358
650 2 4 _aOncology.
_9303086
700 1 _aNieder, Carsten.
_eeditor.
_9321952
700 1 _aGaspar, Laurie E.
_eeditor.
_9452877
710 2 _aSpringerLink (Online service)
_9143950
773 0 _tSpringer eBooks
830 0 _aMedical Radiology,
_9324828
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-37102-8
912 _aZDB-2-SME
999 _c402677