The choroid plexus (CP) is an organoid structure located in the ventricles of the brain that produces CSF and serves as a port of entry for lymphocytes into the CNS. In multiple sclerosis (MS), the CP acts as an important modulator and target of inflammatory activity, as shown in postmortem studies.1-3 Recently, new evidence has emerged highlighting the importance of in vivo CP measurements for understanding MS pathogenesis. The first study, published in 2020, showed gadolinium enhancement in the CP of patients with MS who had recently experienced a clinical relapse.4 Subsequent work demonstrated that CP volume was larger in patients with RRMS than in healthy controls, particularly in those with radiologically active disease, in whom it also correlated with clinical relapses.5 Clinical relevance of the finding was suggested when it was shown that CP volume correlated with the expanded disability status scale (EDSS).6 CP volume also predicted future EDSS development over a follow-up of 4 years, more than conventional MRI markers, such as the number of T2 or gadolinium-enhancing lesions at baseline. The enlargement of the CP is MS-specific because it was not observed in patients with neuromyelitis optica spectrum disorder or migraine.7 Most recently, CP enlargement in MS was associated with the expansion of chronic lesions and lesion severity measured with diffusion MRI8 and with remyelination failure in periventricular areas quantified with 11C-PiB-PET.9

Background  Accurate segmentation of head and neck squamous cell cancer (HNSCC) is important for radiotherapy treatment planning. Manual segmentation of these tumors is time-consuming and vulnerable to inconsistencies between experts, especially in the complex head and neck region. The aim of this study is to introduce and evaluate an automatic segmentation pipeline for HNSCC using a multi-view CNN (MV-CNN). Methods The dataset included 220 patients with primary HNSCC and availability of T1-weighted, STIR and optionally contrast-enhanced T1-weighted MR images together with a manual reference segmentation of the primary tumor by an expert. A T1-weighted standard space of the head and neck region was created to register all MRI sequences to. An MV-CNN was trained with these three MRI sequences and evaluated in terms of volumetric and spatial performance in a cross-validation by measuring intra-class correlation (ICC) and dice similarity score (DSC), respectively. Results The average manual segmented primary tumor volume was 11.8±6.70 cm3 with a median [IQR] of 13.9 [3.22-15.9] cm3. The tumor volume measured by MV-CNN was 22.8±21.1 cm3 with a median [IQR] of 16.0 [8.24-31.1] cm3. Compared to the manual segmentations, the MV-CNN scored an average ICC of 0.64±0.06 and a DSC of 0.49±0.19. Improved segmentation performance was observed with increasing primary tumor volume: the smallest tumor volume group (<3 cm3) scored a DSC of 0.26±0.16 and the largest group (>15 cm3) a DSC of 0.63±0.11 (p<0.001). The automated segmentation tended to overestimate compared to the manual reference, both around the actual primary tumor and in false positively classified healthy structures and pathologically enlarged lymph nodes. Conclusion An automatic segmentation pipeline was evaluated for primary HNSCC on MRI. The MV-CNN produced reasonable segmentation results, especially on large tumors, but overestimation decreased overall performance. In further research, the focus should be on decreasing false positives and make it valuable in treatment planning.