Document Detail


Modeling minimal residual disease (MRD)-testing.
MedLine Citation:
PMID:  12531219     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
There is considerable effort to develop more sensitive methods to detect minimal residual disease (MRD) in bone marrow and blood samples of persons with cancer. Results of MRD-testing are used to predict clinical outcome and determine if more anti-cancer therapy is needed. Mathematical models were developed to assess factors affecting sensitivity and specificity of MRD-testing at diverse cancer cell prevalences. Modeling results and predictions were compared to results of large published studies.Accuracy of MRD-testing depends on cancer cell prevalence and distribution in the blood or bone marrow of the subject, sensitivity and specificity of the MRD-test and sample size. In subjects with low cancer cell prevalences (< or = 10(-4)) results of MRD testing are likely inaccurate. Increasingly sensitive MRD-tests are only marginally useful; the major obstacle to accuracy is inadequate sampling. Increasing sensitivity of methods to detect MRD is unlikely sufficient to increase accuracy of MRD-testing. In contrast, increased sampling (size and frequency) and assigning a high cut-off value (for example, > or = 10(-3)) to declare a MRD-test positive will increase sensitivity and specificity, respectively.
Authors:
Anna Butturini; John Klein; Robert Peter Gale
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Leukemia research     Volume:  27     ISSN:  0145-2126     ISO Abbreviation:  Leuk. Res.     Publication Date:  2003 Apr 
Date Detail:
Created Date:  2003-01-17     Completed Date:  2003-04-16     Revised Date:  2009-11-03    
Medline Journal Info:
Nlm Unique ID:  7706787     Medline TA:  Leuk Res     Country:  England    
Other Details:
Languages:  eng     Pagination:  293-300     Citation Subset:  IM    
Affiliation:
Department of Pediatrics, Division of Hematology Oncology, Childrens' Hospital Los Angeles, 4650 Sunset Blvd., Los Angeles, CA 90027-6016, USA. abutturini@chla.usc.edu
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MeSH Terms
Descriptor/Qualifier:
Biological Markers
Diagnostic Errors
False Negative Reactions
Humans
Models, Biological*
Models, Statistical
Neoplasm, Residual / diagnosis,  pathology*
Neoplasms / pathology*
Predictive Value of Tests
Recurrence
Sensitivity and Specificity
Treatment Outcome
Chemical
Reg. No./Substance:
0/Biological Markers

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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