Kostopoulos S, Konstandinou C, Sidiropoulos K, Ravazoula P, Kalatzis I, Asvestas P, Cavouras D, Glotsos D
Medical Image and Signal Processing Laboratory, Department of Biomedical Engineering, Technological Educational Institute of Athens, 12210, Egaleo, Athens, Greece.
Department of Medical Physics, University of Patras, 26504, Rio, Patras, Greece.
J Microsc. 2015 Oct;260(1):37-46. doi: 10.1111/jmi.12264. Epub 2015 May 13.
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.
脑肿瘤被认为是最致命且最难治疗的癌症形式之一，其病因不明且缺乏任何切实可行的筛查方法。在本研究中，我们探究了专家组织病理学家在评估组织学样本时使用的描述性标准与定量图像分析特征相结合，是否能提高脑肿瘤分级的诊断准确性。数据包括从希腊帕特雷大学医院档案中收集的61例脑癌病例（星形细胞瘤、少突胶质细胞瘤、脑膜瘤）。将医生的描述性标准和图像分析的定量特征纳入判别函数，设计了一个计算机辅助诊断系统，用于区分低级别和高级别脑肿瘤。仅在系统中使用医生的描述性特征时，显示出较高的判别准确率（93.4%）。当在系统中将文字描述性特征与定量图像分析特征相结合时，判别准确率提高到了98.4%。将所提出的系统推广到未见过的数据上，总体预测准确率达到了86.7%±5.4%。鉴于组织学分级会影响治疗选择，且临床实践中诊断错误可能较为显著，所提出系统的应用可在日常临床实践中防止诊断误解。
