A minimally invasive multiple marker approach allows highly efficient detection of meningioma tumors. Keller, A., Ludwig, N., Comtesse, N., Hildebrandt, A., Meese, E., & Lenhof, H. BMC bioinformatics, 7:539, December, 2006.
doi  abstract   bibtex   
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. 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 Naïve 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. 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.
@Article{Keller2006,
  author          = {Keller, Andreas and Ludwig, Nicole and Comtesse, Nicole and Hildebrandt, Andreas and Meese, Eckart and Lenhof, Hans-Peter},
  title           = {A minimally invasive multiple marker approach allows highly efficient detection of meningioma tumors.},
  journal         = {BMC bioinformatics},
  year            = {2006},
  volume          = {7},
  pages           = {539},
  month           = dec,
  issn            = {1471-2105},
  abstract        = {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. 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 Naïve 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. 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.},
  chemicals       = {Antigens, Neoplasm, Biomarkers, Tumor},
  citation-subset = {IM},
  completed       = {2007-02-06},
  country         = {England},
  doi             = {10.1186/1471-2105-7-539},
  issn-linking    = {1471-2105},
  keywords        = {Algorithms; Antigens, Neoplasm, analysis; Biomarkers, Tumor, analysis; Diagnosis, Computer-Assisted, methods; Gene Expression Profiling, methods; Humans; Immunoassay, methods; Meningeal Neoplasms, diagnosis, immunology; Meningioma, diagnosis, immunology; Pattern Recognition, Automated, methods; Reproducibility of Results; Sensitivity and Specificity},
  nlm-id          = {100965194},
  owner           = {NLM},
  pii             = {1471-2105-7-539},
  pmc             = {PMC1769403},
  pmid            = {17184519},
  pubmodel        = {Electronic},
  pubstatus       = {epublish},
  revised         = {2015-11-19},
}
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