## 2 Materials and Methods
### Data Set
#### 2.1.2 Inference Data Set  
EGD is a valuable resource for neuro-oncology research, representing one of the most comprehensive multicenter collections. It contains preoperative curated T1w magnetic resonance images (Figure 2), molecular biomarkers, and tumour segmentations from 774 patients with glioma treated at Erasmus Medical Center (University Medical Center Rotterdam) between 2008 and 2018.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Field** & **RSNA-MICCAI BraTS 2021** \\ \hline Patients & 672 \\ Demographic data & Not available in the public release \\ Scanner (Tesla) & Multicenter scanners (mainly 1.5T and 3T) \\ Data format & MRI in DICOM and NIfTI formats \\ Total size & 142 GB \\ Origin & Multiple international institutions \\ Acquisition years & Up to 2014–2020 (preoperative scans) \\ Imaging modalities & Native T1, contrast-enhanced T1, T2, T2-FLAIR \\ Access & Public (CC-BY license, registration via TCIA) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Key characteristics of the RSNA-MICCAI BraTS 2021 data set.  
Figure 1: Axial T1w pre-contrast MRI slices from the RSNA-MICCAI data set. Each row shows a different patient, and each column shows a different slice from the same scan.  
Patient demographic data include 281 women, 492 men, and one patient of unspecified sex. The age distribution ranges from 19 to 86 years. However, 49 patients with age records marked with the \(-1\) label were identified, indicating missing information. Among them, one patient had neither age nor sex data available [23]. This data set differs significantly from RSNA-MICCAI in terms of anatomical presentation, as the T1w images in EGD retain full cranial anatomy, whereas the training set consists of skull-stripped images. This contrast enables the assessment of the model's ability to generalise across differing acquisition and preprocessing protocols. A summary of the data set characteristics is provided in Table 2.  
To ensure consistency, the same predictive task identifying imaging features associated with GBM--was applied across both data sets. Although the RSNA-MICCAI data set was originally designed for radiogenomic classification based on MGMT promoter methylation, the analysis instead focused on detecting characteristic GBM visual patterns. In the case of the EGD data set, the model was evaluated by identifying cases consistent with WHO grade IV glioblastoma. Despite differences in labelling criteria and image preprocessing particularly the use of skull-stripped images in RSNA-MICCAI versus full head scans in EGD both data sets are clinically aligned with GBM detection. These variations introduced valuable diversity in the input data, allowing a rigorous assessment of the model's ability to generalise across different anatomical and acquisition settings.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Field** & **Erasmus Glioma Database (EGD)** \\ \hline Patients & 774 glioma cases (281 female, 492 male, 1 unknown) \\ Demographic data & Available (age and sex) \\ Scanner (Tesla) & Siemens, Philips, GE, Toshiba: 1.5T (571), 3T (83), 1T (110), 0.5T (6) \\ Data format & NIfT1 (registered to MNI atlas, skull-stripped) \\ Total size & 101 GB \\ Origin & Erasmus MC, Rotterdam, The Netherlands \\ Acquisition years & 2008–2018 (preoperative scans) \\ Imaging modalities & Native T1, contrast-enhanced T1, T2, T2-FLAIR \\ Access & Restricted \\ \hline \hline \end{tabular}
\end{table}
Table 2: Key characteristics of the Erasmus Glioma Database.  
Figure 2: Axial T1w pre-contrast MRI slices from the EGD data set. Each row shows a different patient, and each column shows a different slice from the same scan.