## 4 Discussion
[11] developed a DL model for ventricle parcellation across multimodal MR sequences (T1, FLAIR, and T2), reporting a mean DSC of 0.91 in a mixed pathology dataset with a large range of ventricle sizes. In contrast, [PERSON] et al. [12] achieved a DSC of 0.96 using their DL network on 501 TI-weighted MRIs from older adults with various cerebrovascular lesions. However, they explicitly acknowledged the lack of brain tumor patients in their training set, limiting its relevance to oncologic populations. In our study, the nnU-Net achieved a DSC of 0.93 on the internal dataset, which is comparable to these benchmarks despite the different clinical context. Both the nnU-Net and SynthSeg models achieved lower DSCs (0.84) on an external dataset, highlighting a performance drop that may be expected when transitioning from internal to unseen multi-institutional data. Taken together, while our DSC values are slightly lower than some published scores, this may be attributable to a more clinically complex patient population (brain tumor patients with potentially distorted ventricle anatomy) and the use of external validation. A study by [PERSON] et al. [22] looked at the interobserver variability in the delineation of OARs in the brain. They showed that the standard deviations between delineations were below 1 mm in most OARs. In addition, the interobserver DSC scores for organs of comparable size to the ventricles, like the brainstem, were around 0.89. Moreover, a study by [PERSON] et al. [32] showed similar standard deviations and DSC scores for several OARs in head and neck cancer patients. Within the internal test set, the high surface DSCs of the nnU-Net and the SynthSeg model, together with the median and 10-90 percentile range images, show that the models have comparable results to interobserver variability for brain OARs delineations. However, interobserver variability for the EPTN 2021 structures, which includes the ventricles, is not known. Investigating this variability could be a valuable direction for future research, particularly given the differences observed between the internal and external datasets in this study. In the PVS segmentation task, our results highlight the impact that errors in the ventricle segmentation can have on the accuracy of the resulting PVS segmentations. We also observed a general trend of lower DSC and surface DSC and higher HD95 in the PVS segmentation in comparison to the underlying ventricle segmentations. Since the PVS is defined by the region surrounding the segmented ventricles, inaccuracies in the ventricle boundaries propagate into the PVS, affecting both the DSC and surface DSC metrics. This cascade underscores the need for accurate ventricle segmentations to ensure precise PVS delineations, especially in radiotherapy where the dose constraints apply to the PVS rather than the ventricles. Nevertheless, previous studies suggest that contouring variability only affects dosimetric outcomes when the OAR edge lies near a high dose gradient. In most cases, contouring variability does not significantly influence dosimetric outcomes [33; 34]. These finding suggest that despite differences in PVS segmentation accuracy, the performance of the nnU-Net and SynthSeg models may suffice for radiotherapy planning applications. When the OAR edge is not near a high-dose gradient, additional contour precision is unlikely to produce meaningful dosimetric benefits  
Furthermore, emerging studies suggest that the PVS may play a significant role in the development of radiation-induced contrast enhancement (RICE). For instance, [PERSON] et al. [2] demonstrated that the PVS is a predisposition site for RICE occurrence and utilized PVS segmentation as an input for their RICE prediction model. While direct evidence on the clinical benefits of PVS sparing is currently limited, [PERSON] et al. [35]reported that RICE near the PVS was associated with transient declines in cognitive function and health-related quality of life at 12 months post-treatment, although these declines diminished in the long term.