## 1 Introduction  
Primary brain tumors represent a heterogeneous group of neoplasms that originate from cells within the Central Nervous System (CNS). In particular, gliomas develop from glial cells and are the most common brain tumors among adults, representing 75% of malignant primary neoplasms [1]. The WHO 2021 classification of CNS tumors identifies three main categories of diffuse gliomas: pediatric-type diffuse low-grade gliomas, pediatric-type diffuse high-grade gliomas, and adult-type diffuse gliomas, further subdivided into astrocytomas (IDH mutant), oligodendrogliomas (IDH mutant, and encoded 1p/19q) and glioblastomas (IDH wildtype) [2]. Multiparametric brain Magnetic Resonance Imaging (MRI), including T2-weighted, T2-weighted fluid-attenuated inversion recovery (FLAIR),and pre- and post-contrast 3D T1-weighted sequences (T1 CE), is considered the diagnostic gold standard for the detection of brain tumors [3]. Each MRI modality provides complementary information and is employed to assess location, size, margins, structure, and spread of the tumors, along with the treatment response. Specifically, T1 CE imaging mainly highlights areas where the blood-brain barrier is disrupted, while peritumoral edema is generally visible on T2-weighted images and is clearly visible on FLAIR sequences, in which the cerebrospinal fluid (CSF) is suppressed [4]. In this context, the segmentation of tumor structures in brain MRI is an essential step for diagnosis, quantitative assessment, and surgical planning. Although MRI allows for an informative description of brain structures, accurately and reproducibly segmenting brain tumors remains a challenging task due to poor spatial resolution, low contrast, ill-defined boundaries, inhomogeneity, partial volume effect, noise, and other acquisition artifacts in data that can hinder the outcome [5]. Moreover, manual delineation of brain tumors in MRI images is a time-consuming and error-prone process because of inter- and intra-variability among medical operators. This has led to the development of automatic segmentation approaches based mainly on Deep Learning (DL), which enable a faster and more consistent delineation of tumor regions and have the potential to facilitate radiomic analyses and enhance clinical decision-making [6]. Among DL architectures, Convolutional Neural Networks (CNNs) are the most widely adopted models for brain tumor segmentation [7]. CNN-based models such as U-Net [8], its 3D extensions [9], and more recent frameworks like nnU-Net [10], have demonstrated state-of-the-art performance in brain tumor segmentation tasks. More recently, emerging paradigms based on the use of Vision Transformers (ViTs) and attention mechanisms have demonstrated superior performance due to their ability to capture global contextual information, which is often limited in CNN-based approaches [11]. However, higher computational costs and the need for large data availability still limit their clinical adoption. Although numerous DL models have been proposed for glioma segmentation, most studies primarily focus on segmenting three tumor subregions: the enhancing tumor (ET), which is visible in T1 CE images; the tumor core (TC), which includes both the enhancing and necrotic (NCR) areas; and the whole tumor (WT), which represents the overall extension of the tumor including the peritumoral edema (ED). This standardization is derived from large public datasets such as the Brain Tumor Segmentation (BraTS) challenge dataset [12]. However, from a clinical perspective, a finer and more accurate segmentation of individual tumor components is necessary for a better assessment of the neoplasm. Indeed, the WHO 2021 classification of CNS tumors highlighted the importance of evaluating necrotic regions in the diagnosis and prognosis of adult-type diffuse gliomas, underlining the diagnostic challenges behind their identification and the impact of this ambiguity in tumor-type differentiation [13].