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10.1038/nrclinonc.2012.196 http://scihub22266oqcxt.onion/10.1038/nrclinonc.2012.196 C4555846!4555846!23165123     free     free     free Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Nat+Rev+Clin+Oncol 2013 ; 10 (1): 27-40 Nephropedia Template TP {{tp|p=23165123|t=2013. Predicting outcomes in radiation oncology ?multifactorial decision support systems |pdf=|usr=}}{{23165123}} gab.com Text Predicting outcomes in radiation oncology multifactorial decision support systems JO: Nat Rev Clin Oncol http://www.kidney.de/pget.php?&tw=1&pmid=23165123 #FOAvip With the emergence of individualized medicine and the increasing amount and complexity of available medical data, a growing need exists for the development of clinical decision-support systems based on prediction models of treatment outcome. In radiation oncology, these models combine both predictive and prognostic data factors from clinical, imaging, molecular and other sources to achieve the highest accuracy to predict tumour response and follow-up event rates. In this Review, we provide an overview of the factors that are correlated with outcome?including survival, recurrence patterns and toxicity?in radiation oncology and discuss the methodology behind the development of prediction models, which is a multistage process. Even after initial development and clinical introduction, a truly useful predictive model will be continuously re-evaluated on different patient datasets from different regions to ensure its population-specific strength. In the future, validated decision-support systems will be fully integrated in the clinic, with data and knowledge being shared in a standardized, instant and global manner. Twit Text FOAVip Predicting outcomes in radiation oncology multifactorial decision support systems ????Nat Rev Clin Oncol http://www.kidney.de/pget.php?&tw=1&pmid=23165123 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=23165123 #FOAvip English Wikipedia <ref>{{Cite pmid|23165123}}</ref> Predicting outcomes in radiation oncology ?multifactorial decision support systems #MMPMID23165123 Lambin P; van Stiphout RGPM; Starmans MHW; Rios-Velazquez E; Nalbantov G; Aerts HJWL; Roelofs E; van Elmpt W; Boutros PC; Granone P; Valentini V; Begg AC; De Ruysscher D; Dekker A Nat Rev Clin Oncol 2013[Jan]; 10 (1): 27-40 PMID23165123show ga With the emergence of individualized medicine and the increasing amount and complexity of available medical data, a growing need exists for the development of clinical decision-support systems based on prediction models of treatment outcome. In radiation oncology, these models combine both predictive and prognostic data factors from clinical, imaging, molecular and other sources to achieve the highest accuracy to predict tumour response and follow-up event rates. In this Review, we provide an overview of the factors that are correlated with outcome?including survival, recurrence patterns and toxicity?in radiation oncology and discuss the methodology behind the development of prediction models, which is a multistage process. Even after initial development and clinical introduction, a truly useful predictive model will be continuously re-evaluated on different patient datasets from different regions to ensure its population-specific strength. In the future, validated decision-support systems will be fully integrated in the clinic, with data and knowledge being shared in a standardized, instant and global manner. äPredicting outcomes in radiation oncology ?multifactorial decision sup(epmc).pdf DeepDyve Pubget Overpricing