## 5 Discussion
Despite the increased computational cost in terms of inference time and peak memory usage, the multi-encoder model achieves significantly better performance than SegFormer3D across all tumor subregions. As a result, the model offers a balanced tradeoff between computational demand and segmentation accuracy, making it particularly suitable for applications such as clinical decision support systems and radiomic analysis where precision and reliability are essential. Considering recent results on the same segmentation regions, the Dice scores achieved by our model exceed those reported by [PERSON] et al. [38] on BraTS 2018 (0.32 for NCR, 0.62 for ED, and 0.70 for ET) and by [PERSON] et al. [39] on BraTS 2021 (0.60 for NCR, 0.75 for ED, and 0.79 for ET). Moreover, performance on ET (Dice score of 0.88 \(\pm\) 0.15) are in line with those obtained by the top-ranked BraTS 2023 model on the test set (0.85) [32]. These results were further improved when evaluating the performance of our model on WT and TC regions, yielding high mean Dice scores of 0.91 \(\pm\) 0.13 on TC and 0.92 \(\pm\) 0.08 on WT. It is worth noting that in addition to achieving higher overall metrics, our model also showed lower standard deviation values across all tumor subregions, suggesting a higher level of consistency and robustness across inter-patient variability. Nonetheless, a direct comparative analysis relative to the top-ranking BraTS 2023 methods is not feasible due to dataset limitations. Specifically, our model was trained and tested on the training set portion of the challenge dataset, whereas the official results of the challenge were computed on a separate test set which was not released. Therefore, in order to prove the generalizability and robustness of our approach, we conducted an external validation on two publicly available datasets, UPENN-GBM and TCGA-GBM, while carefully excluding any subjects that were part of the BraTS 2023 training cohort. This allowed us to evaluate the model's performance on unseen data collected from different institutions and with different acquisition protocols, providing a more realistic assessment of its clinical applicability. The external results support the strength of the proposed architecture, particularly on ET and WT regions, which are clinically relevant for treatment planning and response monitoring. NCR segmentation shows slightly lower Dice scores and higher standard deviation, reflecting the challenges previously raised due to its small size and ill-defined boundaries. Furthermore, the balance between precision and recall observed across all subregions suggests that the model avoids both oversegmentation and undersegmentation, an important property for clinical reliability. Considering recent results on the same datasets for further comparisons, [PERSON] et al. [48] trained their BRAINNET framework on the UPENN-GBM dataset, testing it on a subset of 122 patients with Dice scores of 0.894 (TC), 0.891 (WT), and 0.812 (ET). Moreover, authors have also reported the performance in terms of Dice scores of 0.766 (ET), 0.815 (TC), and 0.884 (WT) obtained through 3D Autoencoder [49] and 0.845 (ET), 0.878 (TC), and 0.928 (WT) obtained through nnU-Net. Another 3D DL-based approach presented by [PERSON] et al. [50] and tested on 80 samples from the UPENN-GBM dataset reached Dice scores of 0.45 (NCR), 0.75 (ED), and 0.74 (ET). Overall, our method either outperforms or matches these results, further underscoring the effectiveness of the proposed architecture compared to state-of-the-art models. To better assess the model's clinical relevance across different patients and tumor morphologies, future work will include further validation on additional cohorts, also involving private datasets. Future studies will also incorporate other state-of-the-art models such as nnU-Net or Swin-UNETR into the benchmarking pipeline in order to more comprehensively evaluate the tradeoff between performance and efficiency.