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Can refined categorization improve prediction of patient survival from data?

Date:
April 15, 2014
Source:
European Organisation for Research and Treatment of Cancer
Summary:
Researchers have explored whether a more refined categorization of tumor response or various aspects of progression could improve prediction of overall survival in the RECIST database. They found that modeling target lesion tumor growth did not improve the prediction of overall survival above and beyond that of the other components of progression.
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FULL STORY

In a recent analysis by the RECIST Working Group published in the European Journal of Cancer, EORTC researchers had explored whether a more refined categorization of tumor response or various aspects of progression could improve prediction of overall survival in the RECIST database. They found that modeling target lesion tumor growth did not improve the prediction of overall survival above and beyond that of the other components of progression. The RECIST Working Group includes the EORTC, the United States National Cancer Institute, and the National Cancer Institute of Canada Clinical Trials Group.

Dr. Saskia Litière, EORTC Biostatistician and lead author of this study says, "The World Health Organization criteria developed back in 1979, and more recently RECIST (Response Evaluation Criteria in Solid Tumors) in 2000 and then revised in 2009 have provided us with a unified set of tools for assessing tumor burden. These criteria allow standardized, comparable evaluation of tumor shrinkage in clinical trials, between patients, between trials, and across a wide range of tumor types. Analyses such as ours are indispensable in understanding the role of each component when evaluating progressive disease."

In the RECIST Working Group analysis focusing on patients with breast cancer, colorectal cancer and lung cancer, 36% of the patients had new lesions, 28% had non-target progressive disease, and 49% had experienced target lesion growth. The researchers found that no matter which type of tumor the patient had, next to initial response and measurable progression, the presence of new lesions (Hazard Ratio ranging from 1.5 -- 2.3) and non-target progressive disease (Hazard Ratio ranging from 1.5 -- 2.0) were independent factors linked with worse overall survival in a multivariate model. Furthermore, the presence of new lesions, the occurrence of non-target progressive disease and initial response carried at least as much explanatory value for overall survival as progression based on measurable disease.


Story Source:

The above post is reprinted from materials provided by European Organisation for Research and Treatment of Cancer. The original item was written by John Bean. Note: Materials may be edited for content and length.


Journal Reference:

  1. Saskia Litière, Elisabeth G.E. de Vries, Lesley Seymour, Dan Sargent, Lalitha Shankar, Jan Bogaerts. The components of progression as explanatory variables for overall survival in the Response Evaluation Criteria in Solid Tumours 1.1 database. European Journal of Cancer, 2014; DOI: 10.1016/j.ejca.2014.03.014

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European Organisation for Research and Treatment of Cancer. "Can refined categorization improve prediction of patient survival from data?." ScienceDaily. ScienceDaily, 15 April 2014. <www.sciencedaily.com/releases/2014/04/140415125628.htm>.
European Organisation for Research and Treatment of Cancer. (2014, April 15). Can refined categorization improve prediction of patient survival from data?. ScienceDaily. Retrieved July 1, 2015 from www.sciencedaily.com/releases/2014/04/140415125628.htm
European Organisation for Research and Treatment of Cancer. "Can refined categorization improve prediction of patient survival from data?." ScienceDaily. www.sciencedaily.com/releases/2014/04/140415125628.htm (accessed July 1, 2015).

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