title: Computer-based malignancy grading of astrocytomas employing a support vector machine classifier, the WHO grading system and the regular hematoxylin-eosin diagnostic staining procedure.
abstract: OBJECTIVE
  To investigate and develop an automated technique for astrocytoma malignancy grading compatible with the clinical routine.
  
  
  METHODS
  One hundred forty biopsies of astrocytomas were collected from 2 hospitals. The degree of tumor malignancy was defined as low or high according to the World Health Organization grading system. From each biopsy, images were digitized and segmented to isolate nuclei from background tissue. Morphologic and textural nuclear features were quantified to encode tumor malignancy. Each case was represented by a 40-dimensional feature vector. An exhaustive search procedure in feature space was utilized to determine the best feature combination that resulted in the smallest classification error. Low and high grade tumors were discriminated using support vector machines (SVMs). To evaluate the system performance, all available data were split randomly into training and test sets.
  
  
  RESULTS
  The best vector combination consisted of 3 textural and 2 morphologic features. Low and high grade cases were discriminated with an accuracy of 90.7% and 88.9%, respectively, using an SVM classifier with polynomial kernel of degree 2.
  
  
  CONCLUSIONS
  The proposed methodology was based on standards that are common in daily clinical practice and might be used in parallel with conventional grading as a second-opinion tool to reduce subjectivity in the classification of astrocytomas.
authors.affiliation: Computer Laboratory, School of Medicine, University of Patras, 265 00 Rio, Patras, Greece.
authors.forename: Dimitris
authors.initials: D
authors.lastname: Glotsos
authors.forename: Panagiota
authors.initials: P
authors.lastname: Spyridonos
authors.forename: Panagiotis
authors.initials: P
authors.lastname: Petalas
authors.forename: Dionisis
authors.initials: D
authors.lastname: Cavouras
authors.forename: Panagiota
authors.initials: P
authors.lastname: Ravazoula
authors.forename: Petroula-Arampatoni
authors.initials: PA
authors.lastname: Dadioti
authors.forename: Ioanna
authors.initials: I
authors.lastname: Lekka
authors.forename: George
authors.initials: G
authors.lastname: Nikiforidis
mesh_terms: D001185:Artificial Intelligence; D001254:Astrocytoma / Q000175:diagnosis / Q000473:pathology*; D001706:Biopsy; D001932:Brain Neoplasms / Q000175:diagnosis / Q000473:pathology*; D004801:Eosine Yellowish-(YS) / Q000737:chemistry; D006416:Hematoxylin / Q000737:chemistry; D006801:Humans; D007091:Image Processing, Computer-Assisted; D012984:Software; D014944:World Health Organization
chemical_list: D004801:Eosine Yellowish-(YS); D006416:Hematoxylin