Brain tumor classification using AFM in combination with data mining techniques

Marlene Huml, René Silye, Gerald Zauner, Stephan Hutterer, Kurt Schilcher

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)


Although classification of astrocytic tumors is standardized by the WHO grading system, which is mainly based on microscopy-derived, histomorphological features, there is great interobserver variability. The main causes are thought to be the complexity of morphological details varying from tumor to tumor and from patient to patient, variations in the technical histopathological procedures like staining protocols, and finally the individual experience of the diagnosing pathologist. Thus, to raise astrocytoma grading to a more objective standard, this paper proposes a methodology based on atomic force microscopy (AFM) derived images made from histopathological samples in combination with data mining techniques. By comparing AFM images with corresponding light microscopy images of the same area, the progressive formation of cavities due to cell necrosis was identified as a typical morphological marker for a computer-assisted analysis. Using genetic programming as a tool for feature analysis, a best model was created that achieved 94.74% classification accuracy in distinguishing grade II tumors from grade IV ones. While utilizing modern image analysis techniques, AFM may become an important tool in astrocytic tumor diagnosis. By this way patients suffering from grade II tumors are identified unambiguously, having a less risk for malignant transformation. They would benefit from early adjuvant therapies.

Original languageEnglish
Article number176519
Pages (from-to)176519
JournalBioMed Research International
Publication statusPublished - 2013


  • Astrocytoma/classification
  • Brain Neoplasms/classification
  • Confidence Intervals
  • Data Mining/methods
  • Glioblastoma/classification
  • Humans
  • Image Processing, Computer-Assisted
  • Microscopy, Atomic Force/methods
  • Neoplasm Grading


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