Computer-Aided Radiological Diagnostics Improves the Preoperative Diagnoses of Medulloblastoma, Pilocytic Astrocytoma, and Ependymoma: A Reproducibility Study

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-parameters were extracted to train RF classifiers for pairwise DD. Performance of the RF models and reproducibility of the manual segmentation were evaluated. Results: Neuroradiologists gave correct and clear-cut diagnoses for 31% of MDB, 14.3% of PA, and 10% of EPM. Most diagnoses comprised several tumor types and altogether diagnoses containing the right tumor were given in 69% of true MDB, 64% of true PA, and 30% of true EPM. Ambiguous diagnoses could be improved by RF classifiers showing the following PA versus MDB performance: sensitivity 0.888 ± 0.031, specificity 0.886 ± 0.036; EPM versus MDB: sensitivity: 0.938 (95% CI = (0.677, 0.997)) and specificity: 0.7 (95% CI = (0.354, 0.919)); EPM versus PA: sensitivity: 0.786 (95% CI = (0.488, 0.942) and specificity: 0.100 (95% CI = (0.005, 0.458). An inter- and intra-rater analysis (three human raters) was performed and the Fleiss’ kappa test revealed high inter-rater agreement of κ = 0.821 (p value << 0.001) and an intra-rater agreement of κ = 0.822 (p value << 0.001). Conclusion: In the frequent case of ambiguous neuroradiologist diagnoses, a subsequent differential RF classification improves the diagnoses in all cases. The largest benefit is gained for the discrimination PA versus MDB with an accuracy of 88.0 ± 3.0% followed by EPM versus MDB with an accuracy of 84.6%.