## 4 Discussion  
The proposed CNN model achieved an AUC-ROC of 0.63 on the RSNA-MICCAI data set, surpassing all previous submissions to the BraTS 2021 challenge [21]. This result positions the model as the current benchmark within this evaluation framework. The model was trained on skull-stripped T1-weighted MRIs and subsequently applied to the EGD, which includes full-head scans with different anatomical representations. Despite these morphological and acquisition differences, the model achieved an F1-score of 0.88, a precision of 0.78, and a false negative rate of 0.21 on the EGD data set [23]. In prior work on MRI-based glioma classification, researchers generally trained and tested CNN models on the same data set without external validation or bias analysis [42; 43; 44; 45]. Notably, [PERSON] et al. [42] trained a CNN to predict genetic mutations (e.g., IDH1) in gliomas from MR images and reported accuracy using only internal train/test splits. [PERSON] et al. [43] developed 3D CNN pipelines to grade gliomas using BraTS-2018 and TCGA-LGG via five-fold cross-validation. [PERSON] et al. [44] and [PERSON] et al. [45] also used single-institutional data sets or limited validation. None of these studies performed external evaluation or assessed demographic bias. In contrast, our study incorporates cross-data set validation and statistical bias testing, addressing both generalisation and fairness. To assess the fairness of model predictions, a statistical evaluation was conducted. The Mann-Whitney U test did not identify significant differences in age distributions between correct and incorrect predictions within age cohorts, with \(p\)-values of 0.599, 0.466, and 0.583 for young, adult, and older adults, respectively. Similarly, the Chi-squared test showed no significant association between sex and prediction correctness (\(\chi^{2}=0.314\), \(p=0.575\)). Confidence intervals overlapped, and power analyses confirmed test sensitivity to moderate effects. These results support the absence of demographic bias. Unlike previous works reporting performance variability due to imbalance or hidden covariates [46], our model showed consistency across demographic subgroups. Although CNNs can internalise latent patterns that lead to disparities [46], the methodology applied here reinforces the robustness of our findings. This aligns with best practices in fairness-aware AI and contrasts with studies where data set imbalance introduced bias [47]. The preprocessing strategy contributed to the performance of the model and generalisation. Non-informative slices (27.7%) were excluded, and all images were resampled to a common resolution. Input was restricted to T1-weighted MRIs, and intensity normalisation was applied. Prior work has shown that such standardisation improves classification accuracy [48]. In contrast, other studies have used raw or mixed-contrast inputs without  
Figure 13: Chi-square distribution with 1 degree of freedom, highlighting the observed statistic (0.314) and the median (0.455). The shaded region indicates the p-value area, consistent with the non-significant result. consistent filtering or resolution adjustment, increasing heterogeneity in training data. The architecture was also optimised through systematic analysis of depth and capacity, rather than adopting generic CNNs. Studies using fixed designs, such as VGG-like architectures, report lower performance under similar conditions [49]. Unlike previous CNN-based methods that incorporate multiple MRI modalities like T2w, FLAIR, or diffusion sequences, this study introduces a technique that uses only T1w MRI images for input. T1w images deliver excellent contrast between tumours and adjacent brain tissue and possess the necessary anatomical details for detecting and assessing gliomas. Research has demonstrated that relying solely on T1w images can achieve competitive classification results while streamlining the acquisition process [50,51].