Computer-aided radiological diagnostics improves the preoperative diagnoses of medulloblastoma, pilocytic astrocytoma, and ependymoma: A reproducibility study

摘要

Introduction:Imaging-based diagnosis of intra-axial contrast-enhancing brain tumors is frequently challenging. We show that the diagnosis of medulloblastoma (MDB) versus pilocytic astrocytoma (PA) and ependymoma (EPM) profit from computational analyses, based on quantitative image properties (i.e. textural features from apparent diffusion coefficient (ADC)-maps) and an automated machine learning classification (random forests (RF)).Methods:Forty patients who were diagnosed with three types of brain tumors were included in this study: 16 with MDB, 4 with PA, and 10 EPM. Based on the analysis of multi parametric preoperative magnetic resonance images, neuroradiologists gave a clear-cut diagnosis if they were sure of the diagnosis; however, most diagnoses comprise several possible tumor types. To distinguish between the named tumor types, a computer-based differential diagnosis (DD) tool was developed. Tumor lesion volumes were manually defined using ADC-maps only. From the demarked ADC-map, texture-paramete...