title: Assessing the performance of four different categories of histological criteria in brain tumours grading by means of a computer-aided diagnosis image analysis system.
abstract: Brain tumours are considered one of the most lethal and difficult to treat forms of cancer, with unknown aetiology and lack of any realistic screening. In this study, we examine, whether the combination of descriptive criteria, used by expert histopathologists in assessing histologic tissue samples, and quantitative image analysis features may improve the diagnostic accuracy of brain tumour grading. Data comprised 61 cases of brain cancers (astrocytomas, oligodendrogliomas, meningiomas) collected from the archives of the University Hospital of Patras, Greece. Incorporating physician's descriptive criteria and image analysis's quantitative features into a discriminant function, a computer-aided diagnosis system was designed for discriminating low-grade from high-grade brain tumours. Physician's descriptive features, when solely used in the system, proved of high discrimination accuracy (93.4%). When verbal descriptive features were combined with quantitative image analysis features in the system, discrimination accuracy improved to 98.4%. The generalization of the proposed system to unseen data converged to an overall prediction accuracy of 86.7% ± 5.4%. Considering that histological grading affects treatment selection and diagnostic errors may be notable in clinical practice, the utilization of the proposed system may safeguard against diagnostic misinterpretations in every day clinical practice.
authors.affiliation: Medical Image and Signal Processing Laboratory, Department of Biomedical Engineering, Technological Educational Institute of Athens, 12210, Egaleo, Athens, Greece.
authors.forename: S
authors.initials: S
authors.lastname: Kostopoulos
authors.affiliation: Department of Medical Physics, University of Patras, 26504, Rio, Patras, Greece.
authors.forename: C
authors.initials: C
authors.lastname: Konstandinou
authors.affiliation: School of Engineering and Design, Brunel University West London, Uxbridge, Middlesex, UB8 3PH, United Kingdom.
authors.forename: K
authors.initials: K
authors.lastname: Sidiropoulos
authors.affiliation: Department of Pathology, University Hospital of Patras, 26504, Rio, Patras, Greece.
authors.forename: P
authors.initials: P
authors.lastname: Ravazoula
authors.affiliation: Medical Image and Signal Processing Laboratory, Department of Biomedical Engineering, Technological Educational Institute of Athens, 12210, Egaleo, Athens, Greece.
authors.forename: I
authors.initials: I
authors.lastname: Kalatzis
authors.affiliation: Medical Image and Signal Processing Laboratory, Department of Biomedical Engineering, Technological Educational Institute of Athens, 12210, Egaleo, Athens, Greece.
authors.forename: P
authors.initials: P
authors.lastname: Asvestas
authors.affiliation: Medical Image and Signal Processing Laboratory, Department of Biomedical Engineering, Technological Educational Institute of Athens, 12210, Egaleo, Athens, Greece.
authors.forename: D
authors.initials: D
authors.lastname: Cavouras
authors.affiliation: Medical Image and Signal Processing Laboratory, Department of Biomedical Engineering, Technological Educational Institute of Athens, 12210, Egaleo, Athens, Greece.
authors.forename: D
authors.initials: D
authors.lastname: Glotsos
mesh_terms: D000465:Algorithms; D001932:Brain Neoplasms / Q000175:diagnosis* / Q000473:pathology* / Q000648:ultrastructure; D003951:Diagnostic Errors / Q000517:prevention & control; D006115:Greece; D006652:Histological Techniques; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods* / Q000592:standards; D060787:Neoplasm Grading
keywords: Brain tumours; classification; computer-aided diagnosis; graphics processing unit; histopathology; microscopy