title: Improving accuracy in astrocytomas grading by integrating a robust least squares mapping driven support vector machine classifier into a two level grade classification scheme.
abstract: Grading of astrocytomas is an important task for treatment planning; however, it suffers from significantly great inter-observer variability. Computer-assisted diagnosis systems have been propose to assist towards minimizing subjectivity, however, these systems present either moderate accuracy or utilize specialized staining protocols and grading systems that are difficult to apply in daily clinical practice. The present study proposes a robust mathematical formulation by integrating state-of-art technologies (support vector machines and least squares mapping) in a cascade classification scheme for separating low from high and grade III from grade IV astrocytic tumours. Results have indicated that low from high-grade tumours can be correctly separated with a certainty as high as 97.3%, whereas grade III from grade IV tumours with 97.8%. The overall performance was 95.2%. These high rates have been a result of applying the least squares mapping technique to features prior to classification. A significant byproduct of least squares mapping is that the number of support vectors of the SVM classifiers dropped dramatically from about 80% when no mapping was used to less than 5% when mapping was used. The latter is a clear indication that the SVM classifier has a greater potential to generalize well to new data. In this way, digital image analysis systems for automated grading of astrocytomas are brought closer to clinical practice.
authors.affiliation: Department of Medical Instruments Technology, Technological Educational Institution of Athens, Ag. Spyridonos Street, Aigaleo, Athens 122 10, Greece. dimglo@teiath.gr
authors.forename: Dimitris
authors.initials: D
authors.lastname: Glotsos
authors.forename: Ioannis
authors.initials: I
authors.lastname: Kalatzis
authors.forename: Panagiota
authors.initials: P
authors.lastname: Spyridonos
authors.forename: Spiros
authors.initials: S
authors.lastname: Kostopoulos
authors.forename: Antonis
authors.initials: A
authors.lastname: Daskalakis
authors.forename: Emmanouil
authors.initials: E
authors.lastname: Athanasiadis
authors.forename: Panagiota
authors.initials: P
authors.lastname: Ravazoula
authors.forename: George
authors.initials: G
authors.lastname: Nikiforidis
authors.forename: Dionisis
authors.initials: D
authors.lastname: Cavouras
mesh_terms: D001185:Artificial Intelligence; D001254:Astrocytoma / Q000145:classification* / Q000473:pathology*; D003936:Diagnosis, Computer-Assisted / Q000706:statistics & numerical data*; D005909:Glioblastoma / Q000145:classification / Q000473:pathology; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D016018:Least-Squares Analysis; D012372:ROC Curve; D013194:Staining and Labeling