Taherzadeh, Mahsa

Abstract Developmental delay and seizures with or without movement abnormalities (OMIM 617836) caused by heterozygous pathogenic variants in the DHDDS gene (DHDDS-CDG) is a rare genetic disease that belongs to the progressive encephalopathy spectrum. It results in cognitive delay in affected children, accompanied by myoclonus, seizures, ataxia and tremor, which worsens over time. DHDDS encodes a subunit of a DHDDS/NUS1 cis-prenyltransferase ( cis- PTase), a branch point enzyme of the mevalonate pathway essential for N-linked glycosylation. We describe the first mouse model of this disease, Dhdds R37H+/- strain, heterozygous for the human recurrent de novo c.110G>A:p.R37H pathogenic variant. Dhdds R37H+/- mice present with seizures, myoclonus and memory deficits associated with reduced density or/and maturity of inhibitory interneurons in the cortex. Multiomics analyses of mouse CNS tissues, together with the enzymatic/structural characterization of the R37H DHDDS mutant protein, reveal that the variant produces a catalytically inactive enzyme and results in a brain dolichol deficit, aberrant glycosylation of brain glycoproteins, including those involved in synaptic transmission and major perturbations in the CNS proteome and lipidome. Acetazolamide, a carbonic anhydrase inhibitor clinically approved for treatment of glaucoma, epilepsy, and intracranial hypertension, and successfully used “off-label” to treat genetic movement disorders, reduces seizure susceptibility to pentylenetetrazol in Dhdds R37H+/- mice, suggesting potential therapeutic value of using this drug in human DHDDS-CDG patients. Together, our results define cis- PTase as a master regulator of CNS development and function and establish that its monoallelic debilitating variants cause a novel congenital disorder of glycosylation associated with aberrant levels of neuronal proteins and lipids.

The translation of AI-generated brain metastases (BM) segmentation into clinical practice relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS-METS 2023 challenge has gained momentum for testing and benchmarking algorithms using rigorously annotated internationally compiled real-world datasets. This study presents the results of the segmentation challenge and characterizes the challenging cases that impacted the performance of the winning algorithms. Untreated brain metastases on standard anatomic MRI sequences (T1, T2, FLAIR, T1PG) from eight contributed international datasets were annotated in stepwise method: published UNET algorithms, student, neuroradiologist, final approver neuroradiologist. Segmentations were ranked based on lesion-wise Dice and Hausdorff distance (HD95) scores. False positives (FP) and false negatives (FN) were rigorously penalized, receiving a score of 0 for Dice and a fixed penalty of 374 for HD95. The mean scores for the teams were calculated. Eight datasets comprising 1303 studies were annotated, with 402 studies (3076 lesions) released on Synapse as publicly available datasets to challenge competitors. Additionally, 31 studies (139 lesions) were held out for validation, and 59 studies (218 lesions) were used for testing. Segmentation accuracy was measured as rank across subjects, with the winning team achieving a LesionWise mean score of 7.9. The Dice score for the winning team was 0.65 ± 0.25. Common errors among the leading teams included false negatives for small lesions and misregistration of masks in space. The Dice scores and lesion detection rates of all algorithms diminished with decreasing tumor size, particularly for tumors smaller than 100 mm3. In conclusion, algorithms for BM segmentation require further refinement to balance high sensitivity in lesion detection with the minimization of false positives and negatives. The BraTS-METS 2023 challenge successfully curated well- annotated, diverse datasets and identified common errors, facilitating the translation of BM segmentation across varied environments and providing the tools for future development of personalized volumetric reports to patients undergoing BM treatment.

We describe the design and results from the BraTS 2023 Intracranial Meningioma Segmentation Challenge. The BraTS Meningioma Challenge differed from prior BraTS Glioma challenges in that it focused on meningiomas, which are typically benign extra-axial tumors with diverse radiologic and anatomical presentation and a propensity for multiplicity. Nine participating teams each developed deep-learning automated segmentation models using image data from the largest multi-institutional systematically expert annotated multilabel multi-sequence meningioma MRI dataset to date, which included 1000 training set cases, 141 validation set cases, and 283 hidden test set cases. Each case included T2, FLAIR, T1, and T1Gd brain MRI sequences with associated tumor compartment labels delineating enhancing tumor, non-enhancing tumor, and surrounding non-enhancing FLAIR hyperintensity. Participant automated segmentation models were evaluated and ranked based on a scoring system evaluating lesion-wise metrics including dice similarity coefficient (DSC) and 95% Hausdorff Distance. The top ranked team had a lesion-wise median dice similarity coefficient (DSC) of 0.976, 0.976, and 0.964 for enhancing tumor, tumor core, and whole tumor, respectively and a corresponding average DSC of 0.899, 0.904, and 0.871, respectively. These results serve as state-of-the-art benchmarks for future pre-operative meningioma automated segmentation algorithms. Additionally, we found that 1286 of 1424 cases (90.3%) had at least 1 compartment voxel abutting the edge of the skull-stripped image edge, which requires further investigation into optimal pre-processing face anonymization steps.