## 1 Introduction  
Minimizing radiation to healthy brain tissue while delivering a sufficient radiation dose to tumors is an important balance to achieve during intracranial radiotherapy. Recent studies have shown that the 4 mm region surrounding the ventricles, the periventricular space (PVS), seems particularly sensitive to radiation damage, likely due to lower vascular supply and variability in biological damage when using particle therapy [1,2]. Due to its sensitivity to radiation, the PVS was officially classified as an organ at risk (OAR) in the 2021 EPTN Neuro-Oncology Atlas to limit radiation exposure to this region [3].  
Because the PVS is included in the 2021 EPTN OAR list, its delineation is strongly recommended for all neuro-oncology patients treated with proton therapy in the Netherlands. However, manually contouring OARs is often time-consuming, particularly for larger, more complex structures such as the ventricles and by extension the PVS. Anatomical variations among patients and the narrow complex shape of the ventricles are often hard to detect in the available imaging, leading to interobserver variability [4]. Nevertheless, accurate and consistent PVS delineation is essential to assess its radiation sensitivity and to ensure dose sparing during treatment.  
Given the increasing importance of accurate segmentation for treatment planning in neuro-oncology, there is a growing need for automated segmentation models that precisely segment the entire ventricular system. Deep learning (DL)-based segmentation methods offer a potential solution to the time-consuming and variable process of OAR delineation. Prior research has explored DL-based brain ventricle segmentation. However, these models are not optimized for neuro-oncology patients and fail to fully address the specific needs of radiotherapy. For instance, some models leave out essential structures of the ventricles by only include the lateral ventricles, leaving out the third and fourth ventricles [5,6]. Others are developed for different clinical contexts, such as hydrocephalus diagnosis and monitoring, where the emphasis is on ventricle parcellation rather than precise boundary delineation [7, 8, 9, 10]. These models are typically trained on non-oncologic populations and optimized for different anatomical and diagnostic priorities. Furthermore, most prior studies have not incorporated the multimodal imaging pipelines (e.g., combining CT and T1 contrast-enhanced MRI) that are routinely used in radiotherapy workflows [11,12].  
Creating a sufficiently large dataset suitable for training a DL model is another challenge due to the time-consuming task of data collection and manual delineation [13]. Pretrained off-the-shelf DL models may provide a quick solution for these problems. However, their accuracy and adherence to the EPTN guidelines may be insufficient for achieving clinically acceptable results within radiotherapy. These challenges highlight the need for automated solutions specifically tailored for radiotherapy applications.  
This study had two main objectives. First, we aimed to develop a deep learning model for accurate automated segmentation of the ventricles and periventricular regions using CT and T1 CE MRI in accordance with EPTN guidelines for radiotherapy planning. These guidelines allow the use of dose constraints on organs at risk (OARs), making precise segmentation clinically valuable. Second, we evaluated the accuracy and clinical applicability of this custom model compared to a publicly available pretrained model, using both internal and external test datasets. This study represents, to our knowledge, the first application of DL-based segmentation to support periventricular OAR delineation in neuro-oncology radiotherapy and provides insights into the capabilities and limitations of off-the-shelf DL solutions in this context.