Zubir S. Rentiya

Background: Volumetric analysis and segmentation of magnetic resonance imaging (MRI) data is an important tool for evaluating neurological disease progression and neurodevelopment. Fully automated segmentation pipelines offer faster and more reproducible results. However, since these analysis pipelines were trained on or run based on atlases consisting of neurotypical controls, it is important to evaluate how accurate these methods are for neurodegenerative diseases. In this study, we compared five fully automated segmentation pipelines, including FSL, Freesurfer, volBrain, SPM12, and SimNIBS, with a manual segmentation process in GM1 gangliosidosis patients and neurotypical controls. Methods: We analyzed 45 MRI scans from 16 juvenile GM1 gangliosidosis patients, 11 MRI scans from 8 late-infantile GM1 gangliosidosis patients, and 19 MRI scans from 11 neurotypical controls. We compared the results for seven brain structures, including volumes of the total brain, bilateral thalamus, ventricles, bilateral caudate nucleus, bilateral lentiform nucleus, corpus callosum, and cerebellum. Results: We found volBrain’s vol2Brain pipeline to have the strongest correlations with the manual segmentation process for the whole brain, ventricles, and thalamus. We also found Freesurfer’s recon-all pipeline to have the strongest correlations with the manual segmentation process for the caudate nucleus. For the cerebellum, we found a combination of volBrain’s vol2Brain and SimNIBS’ headreco to have the strongest correlations, depending on the cohort. For the lentiform nucleus, we found a combination of recon-all and FSL’s FIRST to give the strongest correlations, depending on the cohort. Lastly, we found segmentation of the corpus callosum to be highly variable. Conclusions: Previous studies have considered automated segmentation techniques to be unreliable, particularly in neurodegenerative diseases. However, in our study, we produced results comparable to those obtained with a manual segmentation process. While manual segmentation processes conducted by neuroradiologists remain the gold standard, we present evidence to the capabilities and advantages of using an automated process that includes the ability to segment white matter throughout the brain or analyze large datasets, which pose feasibility issues to fully manual processes. Future investigations should consider the use of artificial intelligence-based segmentation pipelines to determine their accuracy in GM1 gangliosidosis, lysosomal storage disorders, and other neurodegenerative diseases.

Background: Volumetric analysis and segmentation of magnetic resonance imaging (MRI) data is an important tool for evaluating neurological disease progression and neurodevelopment. Fully automated segmentation pipelines offer faster and more reproducible results. However, since these analysis pipelines were trained on or run based on atlases consisting of neurotypical controls, it is important to evaluate how accurate these methods are for neurodegenerative diseases. In this study, we compared 5 fully automated segmentation pipelines including FSL, Freesurfer, volBrain, SPM12, and SimNIBS with a manual segmentation process in GM1 gangliosidosis patients and neurotypical controls. Methods: We analyzed 45 MRI scans from 16 juvenile GM1 gangliosidosis patients, 11 MRI scans from 8 late-infantile GM1 gangliosidosis patients, and 19 MRI scans from 11 neurotypical controls. We compared results for 7 brain structures including volumes of the total brain, bilateral thalamus, ventricles, bilateral caudate nucleus, bilateral lentiform nucleus, corpus callosum, and cerebellum. Results: We found volBrain’s vol2Brain pipeline to have the strongest correlations with the manual segmentation process for the whole brain, ventricles, and thalamus. We also found Freesurfer’s recon-all pipeline to have the strongest correlations with the manual segmentation process for the caudate nucleus. For the cerebellum, we found a combination of volBrain’s vol2Brain and SimNIBS’ headreco to have the strongest correlations depending on the cohort. For the lentiform nucleus, we found a combination of recon-all and FSL’s FIRST to give the strongest correlations depending on the cohort. Lastly, we found segmentation of the corpus callosum to be highly variable. Conclusion: Previous studies have considered automated segmentation techniques to be unreliable, particularly in neurodegenerative diseases. However, in our study we produced results comparable to those obtained with a manual segmentation process. While manual segmentation processes conducted by neuroradiologists remain the gold standard, we present evidence to the capabilities and advantages of using an automated process including the ability to segment white matter throughout the brain or analyze large datasets, which pose feasibility issues to fully manual processes. Future investigations should consider the use of artificial intelligence-based segmentation pipelines to determine their accuracy in GM1 gangliosidosis, lysosomal storage disorders, and other neurodegenerative diseases.

Ewing's sarcoma (ES), the second most prevalent malignant osseous tumor in children and adolescents, primarily affects the extremities' long bones and pelvic region. Characterized by its aggressive growth, ES often presents with symptoms like swelling, pain, and neurological deficits, impacting various skeletal sites. ES involving the spine, particularly the sacral region, poses a significant challenge due to its rarity, aggressive nature, and limited sensitivity to treatments. We report the case of an 18-year-old male with recurrent metastatic ES presenting with fever, cough, and a lesion in the right humerus. Despite prior treatments and complications including spinal metastasis and cord compression, the patient's condition deteriorated, resulting in an unfortunate outcome. This case highlights the complexities in managing recurrent metastatic ES, emphasizing the need for tailored multidisciplinary approaches and early detection strategies.

Renal cell carcinoma (RCC) arises from the renal tubular epithelial cells and comprises a group of heterogenous renal tumors. Renal tumors can metastasize to involve almost any body organ, the common sites being the lung, liver, bone, brain, adrenal gland, head, neck, and rarely, inferior vena cava (IVC), leading to lethal outcomes. We present a case of RCC with IVC invasion in a patient who presented with right-sided flank pain and gross hematuria. His routine biochemical and hematological parameters were unremarkable, and an abdominal examination revealed a complex renal mass with mild hydronephrosis. The patient underwent contrast-enhanced magnetic resonance angiography with venography, which showed a right renal upper polar mass lesion extending into the right vein obliterating it up to its junction with the IVC. Integrating examination and imaging findings were suggestive of right renal RCC. Our case highlights the importance of standard preoperative MRI imaging to assess IVC invasion and its morphologic features including vessel breach or complete occlusion of the IVC.

Purpose: Transradial access (TRA) for diagnostic and interventional neurointerventional procedures has recently gained traction over transfemoral access (TFA) in the neurointerventional community. This meta-analysis aims to assess and summarize the utility of TRA in cerebral angiography and neurointerventional procedures. Methods: A systematic literature review was performed utilizing Pubmed, Embase, and Scopus databases. Using PRISMA guidelines, records were extracted with the following search terms: transradial approach, transradial access, radial access, cerebral angiogram, cerebral angiography, neurointervention, and neuroendovascular. The primary outcomes assessed were case success rate, complication rate, and crossover rate from TRA to TFA. Secondary analysis was performed on procedure time, fluoroscopy time, fluoroscopy time per vessel (diagnostic procedures only), contrast dose, radial artery diameter, distal radial artery diameter, and patient preference for TRA over TFA. Results: Sixty-two full-text articles were analyzed for this meta-analysis, representing 12,927 diagnostic and interventional TRA access patients. Our analysis revealed a combined diagnostic and interventional case success rate of 95.9% and complication rate of 3.5%, with crossover to TFA occurring in 4.9% of cases. Conclusion: This meta-analysis demonstrates that TRA access for diagnostic angiography and neurointerventional procedures is a safe and effective approach, though determining a true complication rate is challenging as the definition of TRA complications has changed in various publications over time.