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Early Detection
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BMC Bioinformatics 2006, 7:539; doi:10.1186/1471-2105-7-539;
Published 21 December 2006
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Abstract |
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A minimally
invasive multiple marker approach allows highly efficient detection of
meningioma tumors
Andreas Keller1,
Nicole Ludwig2, Nicole
Comtesse2, Andreas
Hildebrandt1, Eckart
Meese2 and Hans-Peter
Lenhof1*
1Center
for Bioinformatics, Saarland University, Building 3.11, 66041
Saarbršucken, Germany. 2Department
of Human Genetics, Medical School, Saarland University, Building 60,
66421 Homburg/Saar, Germany -- Email: Andreas Keller - ack@bioinf.uni-sb.de;
Nicole Ludwig - lud.nic@arcor.de; Nicole Comtesse -
hgncom@uniklinik-saarland.de; Andreas Hildebrandt - anhi@bioinf.uni-sb.de;
Eckart Meese - hgemee@uniklinikum-saarland.de; Hans-Peter Lenhof* -
lenhof@bioinf.uni-sb.de; *Corresponding author -- Submission date 13
September 2006; Acceptance date 21 December 2006; Publication date 21
December 2006
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Background.
The development of effective frameworks that permit an accurate
diagnosis of tumors, especially in their early stages, remains a grand
challenge in the field of bioinformatics.
Our approach uses statistical learning techniques applied to multiple
antigen tumor antigen markers utilizing the immune system as a very
sensitive marker of molecular pathological processes.
For validation purposes we choose the intracranial meningioma tumors
as model system since they occur very frequently, are mostly benign,
and are genetically stable.
Results.
A total of 183 blood samples from 93 meningioma patients (WHO
stages I-III) and 90 healthy controls were screened for seroreactivity
with a set of 57 meningioma-associated antigens.
We tested several established statistical learning methods on the
resulting reactivity patterns using 10-fold cross validation.
The best performance was achieved by Naive Bayes Classifiers.
With this classification method, our framework, called Minimally
Invasive Multiple Marker (MIMM) approach, yielded a specificity of
96.2%, a sensitivity of 84.5%, and an accuracy of 90.3%, the
respective area under the ROC curve was 0.957.
Detailed analysis revealed that prediction performs particularly well
on low-grade (WHO I) tumors, consistent with our goal of early stage
tumor detection.
For these tumors the best classification result with a specificity of
97.5%, a sensitivity of 91.3%, an accuracy of 95.6%, and an area under
the ROC curve of 0.971 was achieved using a set of 12 antigen markers
only.
This antigen set was detected by a subset selection method based on
Mutual Information. Remarkably, our study proves that the inclusion of
non-specific antigens, detected not only in tumor but also in normal
sera, increases the performance significantly, since non-specific
antigens contribute additional diagnostic information.
Conclusions.
Our approach offers the possibility to screen members of risk
groups as a matter of routine such that tumors hopefully can be
diagnosed immediately after their genesis.
The early detection will finally result in a higher cure- and lower
morbidity-rate.
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© 2006 Keller et al., licensee
BioMed Central Ltd.
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Abstract |
Reprint
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