A minimally invasive multiple marker approach allows highly efficient detection of meningioma tumors

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 Na

• Type: Journal
• Year: 2006
• Where: BMCBI
• Authors: Andreas Keller, Nicole Ludwig, Nicole Comtesse, Andreas Hildebrandt, Eckart Meese, Hans-Peter Lenhof
• Keywords: BMCBI 2006, Early Stage, Statistical Learning, Tumor