Assessing the performance of four different categories of histological criteria in brain tumours grading by means of a computer‐aided diagnosis image analysis system

Summary 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. Lay Description 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. Even though neuroimaging combined with tissue microscopy are fundamentally important for patient management, the potential of diagnostic errors still remains substantially high. One of the approaches that have been proposed, as a precautionary measure against potential diagnostic misinterpretations, is the utilization of computer‐aided diagnostic systems as second opinion tools. In this study, we have designed computer software for computer‐aided diagnosis of brain tumours. The software functions by (i) evaluating image properties, after suitable processing of the brain tumour's microscopy images, (ii) quantifying the physician's descriptive criteria, which are verbal expressions used by the physician when assessing the tumour's tissue samples under the microscope and (iii) characterizing the type of brain tumour by means of a specially designed classification engine that is fed with informa