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Predicting relapse in patients with medulloblastoma by integrating evidence from clinical and genomic features.
|Publication Type||Journal Article|
|Authors||Tamayo, P., Cho YJ, Tsherniak A., Greulich H., Ambrogio L., Schouten-van Meeteren N., Zhou T., Buxton A., Kool M., Meyerson M., Pomeroy SL, and Mesirov J. P.|
|Abstract||Despite significant progress in the molecular understanding of medulloblastoma, stratification of risk in patients remains a challenge. Focus has shifted from clinical parameters to molecular markers, such as expression of specific genes and selected genomic abnormalities, to improve accuracy of treatment outcome prediction. Here, we show how integration of high-level clinical and genomic features or risk factors, including disease subtype, can yield more comprehensive, accurate, and biologically interpretable prediction models for relapse versus no-relapse classification. We also introduce a novel Bayesian nomogram indicating the amount of evidence that each feature contributes on a patient-by-patient basis.|
|Year of Publication||2011|
|Journal||Journal of clinical oncology : official journal of the American Society of Clinical Oncology|
|Date Published (YYYY/MM/DD)||2011/04/10|