000			03414nam a22003615i 4500
999			_c80482 _d80482 _x1
001			80482
003			ES-MaUEC
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007			cr nn 008mamaa
008			140313s2014 gw | s |||| 0|eng d
020			_a9783642371028
024	7		_a10.1007/978-3-642-37102-8 _2doi
040			_dES-MaUEC
050		4	_aRC271.R3 _bD435 2014
245	1	0	_aDecision Tools for Radiation Oncology : _bPrognosis, Treatment Response and Toxicity _cedited by Carsten Nieder, Laurie E. Gaspar.
260			_aBerlin, Heidelberg _bSpringer International Publishing _c2014
300			_a1 recurso en línea (XIII, 305 p.) _b90 ilustraciones, 45 ilustraciones en color
336			_aTexto (visual) _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
490	0		_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		7	_aRadioterapia _2embne _9139979
700	1		_aNieder, Carsten _eeditor literario _988708 _0Local
700	1		_aGaspar, Laurie E. _eeditor literario _0Local _988709
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://link.springer.com/book/10.1007/978-3-642-37102-8 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
907			_a.b12882355 _b10-10-17 _c11-11-15
942			_2lcc _cLE
945			_aRC271.R3 D435 2014 EB _g1 _ieBOOK _j0 _lmae _o- _pEUR0.00 _q- _r- _sb _t15 _u0 _v0 _w0 _x0 _y.i11562171 _z06-04-17
988			_aEBSPRINGER
998			_am _a_alco _a_vill _b11-11-15 _cm _dz _e- _feng _ggw _h0