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On the prediction of Hodgkin lymphoma treatment response

Author:
Andrés Galiana, Enrique Juan deUniovi authority; Fernández Martínez, Juan LuisUniovi authority; Luaces Rodríguez, ÓscarUniovi authority; Coz Velasco, Juan José delUniovi authority; Fernández, R.; Solano, Julia; Nogués, E. A.; González Rodríguez, Ana PilarUniovi authority
Subject:

Hodgkin lymphoma

Treatment response

Machine learning

Publication date:
2015
Publisher version:
http://dx.doi.org/10.1007/s12094-015-1285-z
Citación:
Clinical and Translational Oncology, 17(8), p. (2015); doi:10.1007/s12094-015-1285-z
Descripción física:
p. 612-619
Abstract:

The cure rate in Hodgkin lymphoma is high, but the response along with treatment is still unpredictable and highly variable among patients. Detecting those patients who do not respond to treatment at early stages could bring improvements in their treatment. This research tries to identify the main biological prognostic variables currently gathered at diagnosis and design a simple machine learning methodology to help physicians improve the treatment response assessment

The cure rate in Hodgkin lymphoma is high, but the response along with treatment is still unpredictable and highly variable among patients. Detecting those patients who do not respond to treatment at early stages could bring improvements in their treatment. This research tries to identify the main biological prognostic variables currently gathered at diagnosis and design a simple machine learning methodology to help physicians improve the treatment response assessment

URI:
http://hdl.handle.net/10651/33362
ISSN:
1699-048X
DOI:
10.1007/s12094-015-1285-z
Patrocinado por:

Enrique J. de Andrés was supported by the Spanish Ministerio de Economía y Competitividad (Grant TIN2011-23558), and the medical analysis was supported by the Fondo de Investigaciones Sanitarias (Instituto Carlos III-Grant PI12/01280). No other financial support has been received to perform this retrospective analysis

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