Anti-neurochondrin antibody as a biomarker in primary autoimmune cerebellar ataxia-a case report and review of the literature.

BACKGROUND AND PURPOSE: Neuronal autoantibodies can support the diagnosis of primary autoimmune cerebellar ataxia (PACA). Knowledge of PACA is still sparce. This article aims to highlight the relevance of anti-neurochondrin antibodies and possible therapeutical consequences in people with PACA. METHODS: This is a case presentation and literature review of PACA associated with anti-neurochondrin antibodies. RESULTS: A 33-year-old man noticed reduced control of the right leg in May 2020. During his first clinic appointment at our institution in September 2021, he complained about gait imbalance, fine motor disorders, tremor, intermittent diplopia and slurred speech. He presented a pancerebellar syndrome with stance, gait and limb ataxia, scanning speech and oculomotor dysfunction. Within 3 months the symptoms progressed. An initial cerebral magnetic resonance imaging, June 2020, was normal, but follow-up imaging in October 2021 and July 2022 revealed marked cerebellar atrophy (29% volume loss). Cerebrospinal fluid analysis showed lymphocytic pleocytosis of 11 x 103 /L (normal range 0-4) and oligoclonal bands type II. Anti-neurochondrin antibodies (immunoglobulin G) were detected in serum (1:10,000) and cerebrospinal fluid (1:320, by cell-based indirect immunofluorescence assay and immunoblot, analysed by the EUROIMMUN laboratory). After ruling out alternative causes and neoplasia, diagnosis of PACA was given and immunotherapy (steroids and cyclophosphamide) was started in January 2022. In March 2022 a stabilization of disease was observed. CONCLUSION: Cerebellar ataxia associated with anti-neurochondrin antibodies has only been described in 19 cases; however, the number of unrecognized PACAs may be higher. As anti-neurochondrin antibodies target an intracellular antigen and exhibit a mainly cytotoxic T-cell-mediated pathogenesis, important therapeutic implications may result. Because of the severe and rapid clinical progression, aggressive immunotherapy was warranted. This case highlights the need for rapid diagnosis and therapy in PACA, as stabilization and even improvement of symptoms are attainable.

QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results.

Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder translating DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties could enable clinical review of the most uncertain regions, thereby building trust and paving the way toward clinical translation. Several uncertainty estimation methods have recently been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentage of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, highlighting the need for uncertainty quantification in medical image analyses. Finally, in favor of transparency and reproducibility, our evaluation code is made publicly available at https://github.com/RagMeh11/QU-BraTS.

Divide and Conquer: Stratifying Training Data by Tumor Grade Improves Deep Learning-Based Brain Tumor Segmentation.

It is a general assumption in deep learning that more training data leads to better performance, and that models will learn to generalize well across heterogeneous input data as long as that variety is represented in the training set. Segmentation of brain tumors is a well-investigated topic in medical image computing, owing primarily to the availability of a large publicly-available dataset arising from the long-running yearly Multimodal Brain Tumor Segmentation (BraTS) challenge. Research efforts and publications addressing this dataset focus predominantly on technical improvements of model architectures and less on properties of the underlying data. Using the dataset and the method ranked third in the BraTS 2018 challenge, we performed experiments to examine the impact of tumor type on segmentation performance. We propose to stratify the training dataset into high-grade glioma (HGG) and low-grade glioma (LGG) subjects and train two separate models. Although we observed only minor gains in overall mean dice scores by this stratification, examining case-wise rankings of individual subjects revealed statistically significant improvements. Compared to a baseline model trained on both HGG and LGG cases, two separately trained models led to better performance in 64.9% of cases (p < 0.0001) for the tumor core. An analysis of subjects which did not profit from stratified training revealed that cases were missegmented which had poor image quality, or which presented clinically particularly challenging cases (e.g., underrepresented subtypes such as IDH1-mutant tumors), underlining the importance of such latent variables in the context of tumor segmentation. In summary, we found that segmentation models trained on the BraTS 2018 dataset, stratified according to tumor type, lead to a significant increase in segmentation performance. Furthermore, we demonstrated that this gain in segmentation performance is evident in the case-wise ranking of individual subjects but not in summary statistics. We conclude that it may be useful to consider the segmentation of brain tumors of different types or grades as separate tasks, rather than developing one tool to segment them all. Consequently, making this information available for the test data should be considered, potentially leading to a more clinically relevant BraTS competition.