Neuropathological Case Research Articles

Large language models as a diagnostic support tool in neuropathology.

The WHO guidelines for classifying central nervous system (CNS) tumours are changing considerably with each release. The classification of CNS tumours is uniquely complex among most other solid tumours as it incorporates not just morphology, but also genetic and epigenetic features. Keeping current with these changes across medical fields can be challenging, even for clinical specialists. Large language models (LLMs) have demonstrated their ability to parse and process complex medical text, but their utility in neuro-oncology has not been systematically tested. We hypothesised that LLMs can effectively diagnose neuro-oncology cases from free-text histopathology reports according to the latest WHO guidelines. To test this hypothesis, we evaluated the performance of ChatGPT-4o, Claude-3.5-sonnet, and Llama3 across 30 challenging neuropathology cases, which each presented a complex mix of morphological and genetic information relevant to the diagnosis. Furthermore, we integrated these models with the latest WHO guidelines through Retrieval-Augmented Generation (RAG) and again assessed their diagnostic accuracy. Our data show that LLMs equipped with RAG, but not without RAG, can accurately diagnose the neuropathological tumour subtype in 90% of the tested cases. This study lays the groundwork for a new generation of computational tools that can assist neuropathologists in their daily reporting practice.

LGI1 encephalitis: potentially complement-activating anti-LGI1-IgG subclasses 1/2/3 are associated with the development of hippocampal sclerosis

Two-thirds of published patients with anti-leucine rich, glioma inactivated 1 (LGI1) encephalitis develop hippocampal sclerosis (HS). It is likely that this contributes to residual cognitive long-term deficits and the risk of epilepsy. Almost all patients harbor anti-LGI1-immunoglobulin G-(IgG-) subclass 4, which is considered a “benign”, non-destructive subclass. In contrast, neuropathological case studies have suggested that the classical complement cascade may contribute to mediotemporal cell death in patients with LGI1 antibodies. IgG subclasses 1, 2, or 3 are required to initiate this cascade. We hypothesized that patients with these anti-LGI1-IgG1/2/3 in addition to IgG4 have a higher risk of developing HS than patients with anti-LGI1-IgG4 alone. We retrospectively assessed all anti-LGI1 encephalitis patients from this center with anti-LGI1-IgG-subclass information and follow-up MRI available. Nine out of 20 patients had developed HS (45%). Volumetric FreeSurfer analysis confirmed the visual HS diagnoses. HS and a lower hippocampal volume were associated with anti-LGI1-IgG1/2/3. All six patients with this IgG subclass status developed HS. There was no association with older or younger age at onset, female sex, longer latency from disease onset to start of immunotherapy, less intense immunotherapy, higher serum titers of LGI1 antibodies, LGI1 antibodies in CSF or higher LGI1-specific antibody indices. There was no association between anti-LGI1-IgG1/2/3 status and neuropsychological performance, epilepsy, or general neurological performance. This confirms our hypothesis that anti-LGI1-IgG1/2/3 in serum puts patients at risk of developing HS. If these findings can be confirmed and clinically corroborated, patients with anti-LGI1-IgG1/2/3 might become candidates for anti-complement-directed immunological treatments.

A Neuropathology Case Report of a Woman with Down Syndrome who Remained Cognitively Stable.

In this neuropathology case report, we present findings from an individual with Down syndrome (DS) who remained cognitively stable despite Alzheimer's disease (AD) neuropathology. Clinical assessments, fluid biomarkers, neuroimaging, and neuropathological examinations were conducted to characterize her condition. Notably, her ApoE genotype was E2/3, which is associated with a decreased risk of dementia. Neuroimaging revealed stable yet elevated amyloid profiles and moderately elevated tau levels, while neuropathology indicated intermediate AD neuropathologic change with Lewy body pathology and cerebrovascular pathology. Despite the presence of AD pathology, the participant demonstrated intact cognitive functioning, potentially attributed to factors such as genetic variations, cognitive resilience, and environmental enrichment. The findings suggest a dissociation between clinical symptoms and neuropathological changes, emphasizing the complexity of AD progression in DS. Further investigation into factors influencing cognitive resilience in individuals with DS, including comorbidities and social functioning, is warranted. Understanding the mechanisms underlying cognitive stability in DS could offer insights into resilience to AD neuropathology in people with DS and in the general population and inform future interventions.

Comparing the Diagnostic Performance of GPT-4-based ChatGPT, GPT-4V-based ChatGPT, and Radiologists in Challenging Neuroradiology Cases.

To compare the diagnostic performance among Generative Pre-trained Transformer (GPT)-4-based ChatGPT, GPT‑4 with vision (GPT-4V) based ChatGPT, and radiologists in challenging neuroradiology cases. We collected 32consecutive "Freiburg Neuropathology Case Conference" cases from the journal Clinical Neuroradiology between March 2016 and December 2023. We input the medical history and imaging findings into GPT-4-based ChatGPT and the medical history and images into GPT-4V-based ChatGPT, then both generated adiagnosis for each case. Six radiologists (three radiology residents and three board-certified radiologists) independently reviewed all cases and provided diagnoses. ChatGPT and radiologists' diagnostic accuracy rates were evaluated based on the published ground truth. Chi-square tests were performed to compare the diagnostic accuracy of GPT-4-based ChatGPT, GPT-4V-based ChatGPT, and radiologists. GPT‑4 and GPT-4V-based ChatGPTs achieved accuracy rates of 22% (7/32) and 16% (5/32), respectively. Radiologists achieved the following accuracy rates: three radiology residents 28% (9/32), 31% (10/32), and 28% (9/32); and three board-certified radiologists 38% (12/32), 47% (15/32), and 44% (14/32). GPT-4-based ChatGPT's diagnostic accuracy was lower than each radiologist, although not significantly (all p > 0.07). GPT-4V-based ChatGPT's diagnostic accuracy was also lower than each radiologist and significantly lower than two board-certified radiologists (p = 0.02 and 0.03) (not significant for radiology residents and one board-certified radiologist [all p > 0.09]). While GPT-4-based ChatGPT demonstrated relatively higher diagnostic performance than GPT-4V-based ChatGPT, the diagnostic performance of GPT‑4 and GPT-4V-based ChatGPTs did not reach the performance level of either radiology residents or board-certified radiologists in challenging neuroradiology cases.

Toward a generalizable machine learning workflow for neurodegenerative disease staging with focus on neurofibrillary tangles

Machine learning (ML) has increasingly been used to assist and expand current practices in neuropathology. However, generating large imaging datasets with quality labels is challenging in fields which demand high levels of expertise. Further complicating matters is the often seen disagreement between experts in neuropathology-related tasks, both at the case level and at a more granular level. Neurofibrillary tangles (NFTs) are a hallmark pathological feature of Alzheimer disease, and are associated with disease progression which warrants further investigation and granular quantification at a scale not currently accessible in routine human assessment. In this work, we first provide a baseline of annotator/rater agreement for the tasks of Braak NFT staging between experts and NFT detection using both experts and novices in neuropathology. We use a whole-slide-image (WSI) cohort of neuropathology cases from Emory University Hospital immunohistochemically stained for Tau. We develop a workflow for gathering annotations of the early stage formation of NFTs (Pre-NFTs) and mature intracellular (iNFTs) and show ML models can be trained to learn annotator nuances for the task of NFT detection in WSIs. We utilize a model-assisted-labeling approach and demonstrate ML models can be used to aid in labeling large datasets efficiently. We also show these models can be used to extract case-level features, which predict Braak NFT stages comparable to expert human raters, and do so at scale. This study provides a generalizable workflow for various pathology and related fields, and also provides a technique for accomplishing a high-level neuropathology task with limited human annotations.

Freiburg Neuropathology Case Conference

Freiburg Neuropathology Case Conference

Freiburg Neuropathology Case Conference

Freiburg Neuropathology Case Conference

Freiburg Neuropathology Case Conference

Freiburg Neuropathology Case Conference

Freiburg Neuropathology Case Conference

Freiburg Neuropathology Case Conference

Freiburg Neuropathology Case Conference

Freiburg Neuropathology Case Conference

Neuropathology Outreach in Ghana

Abstract Introduction/Objective Ghana has a population of over 31 million people and approximately 15 neurosurgeons but is currently without a dedicated neuropathologist. Here we present a month of neuropathology outreach performed at the Komfo Anokye Teaching Hospital (KATH) in Kumasi and the Korle-Bu Teaching Hospital (KBTH) in Accra. Methods/Case Report In February of 2022, a second-year neuropathology fellow and a board-certified neuropathologist traveled to Ghana to give 40 approximately 1.5 hour neuropathology didactic and interactive teaching sessions. A two-week format of outreach was developed for each site in Ghana and consisted of didactic lectures and interactive sessions covering the WHO 2021 brain tumor classification system, neuroautopsy, neuroinfectious disease, neurodegenerative disease, neuromuscular disease, forensic neuropathology, neurodevelopment, and unknown case conferences. Meanwhile, a pathology trainee worked remotely to organize 12 one-hour virtual general pathology lectures and develop continuing outreach. A survey of 16 questions including 12 multiple choice questions and four short answer questions was used to assess each component and the overall satisfaction of the outreach. Results (if a Case Study enter NA) Eight brains were examined at four neuroautopsy conferences and ten case consultations were performed upon request. Survey results showed no statistical differences amongst the 20 sessions. Comments were positive and included one respondent planning to redesign their PhD research to focus on neurodegenerative disease in Ghana and several others commenting on the utility of molecular updates. Suggestions for improvement included quizzes, including more morphology and IHC in lectures, sending digital slides before future visits, teaching spinal cord removal, and the need for continuing outreach in neuropathology, dermatopathology, cytopathology and gynecologic pathology. Conclusion This experience provides a useful and well-appreciated approach to delivering international neuropathology outreach. Opportunities in neuropathology on an international level range from educational to clinical practice and research.

A Quantitative and Qualitative Assessment of Frozen Section Diagnosis Accuracy and Deferral Rate Across Organ Systems.

Monitoring of frozen section diagnostic performance provides an important quality improvement measure. Surgical specimens involving a frozen section diagnosis over a 3-year period were retrospectively reviewed. Glass slides were reviewed on cases with discordance. Discordance and deferral rates were calculated. Of 3,675 frozen section diagnoses included, 96 (2.7%) were discordant with the final diagnosis. Additionally, 114 frozen section diagnoses (3.1%) were deferred. The organ-specific discordance rates were lowest in breast and genitourinary specimens and highest for pancreas, lymph node, and gynecologic specimens. Deferral rates were highest in musculoskeletal, breast, and hepatobiliary cases and lowest in thyroid, parathyroid, and neuropathology cases. Discordance was explained by block-sampling error (45%), specimen-sampling error (27%), or interpretation error (27%). Discordant frozen section diagnoses from gynecologic specimens were responsible for 81% of specimen-sampling errors; frozen section diagnoses of lymph nodes, head and neck, and pancreas were responsible for 54% of interpretation errors; 51% of block-sampling errors involved lymph node evaluation for metastatic carcinoma. Careful gross evaluation and microscopic examination of multiple levels should minimize specimen-sampling error and block-sampling error, respectively. Periodic review of accuracy and deferral rates may help reduce errors and improve the overall performance of this essential procedure.

Locus Coeruleus Degeneration Differs Between Frontotemporal Lobar Degeneration Subtypes.

Background:There are few studies on the locus coeruleus (LC) in frontotemporal lobar degeneration (FTLD) and the potential differences in the LC related to the underlying proteinopathy.Objective:The aim of this study was to investigate the LC in FTLD subgroups.Methods:Neuropathological cases diagnosed with FTLD were included. The subgroups consisted of FTLD with tau, transactive response DNA-binding protein 43 (TDP) and fused in sarcoma (FUS). Micro- and macroscopical degeneration of the LC were assessed with respect to the number of neurons and the degree of depigmentation. A group of cognitively healthy subjects and a group with vascular cognitive impairment (VCI) served as comparison groups.Results: A total of 85 FTLD cases were included, of which 44 had FTLD-TDP, 38 had FTLD-tau, and three had FTLD-FUS. The groups were compared with 25 VCI cases and 41 cognitively healthy control cases (N = 151 for the entire study). All FTLD groups had a statistically higher microscopical degeneration of the LC compared to the controls, but the FTLD-tau group had greater micro- and macroscopical degeneration than the FTLD-TDP group. Age correlated positively with the LC score in the FTLD-tau group, but not in the FTLD-TDP group.Conclusion:A greater microscopical degeneration of the LC was observed in all FTLD cases compared to healthy controls and those with VCI. The LC degeneration was more severe in FTLD-tau than in FTLD-TDP. The macroscopically differential degeneration of the LC in FTLD subgroups may facilitate differential diagnostics, potentially with imaging.

Rapid Deployment of Whole Slide Imaging for Primary Diagnosis in Surgical Pathology at Stanford Medicine: Responding to Challenges of the COVID-19 Pandemic.

Stanford Pathology began stepwise subspecialty implementation of whole slide imaging (WSI) in 2018 soon after the first US Food and Drug Administration approval. In 2020, during the COVID-19 pandemic, the Centers for Medicare & Medicaid Services waived the requirement for pathologists to perform diagnostic tests in Clinical Laboratory Improvement Amendments (CLIA)-licensed facilities. This encouraged rapid implementation of WSI across all surgical pathology subspecialties. To present our experience with validation and implementation of WSI at a large academic medical center encompassing a caseload of more than 50 000 cases per year. Validation was performed independently for 3 subspecialty services with a diagnostic concordance threshold above 95%. Analysis of user experience, staffing, infrastructure, and information technology was performed after department-wide expansion. Diagnostic concordance was achieved in 96% of neuropathology cases, 100% of gynecologic pathology cases, and 98% of immunohistochemistry cases. After full implementation, 8 high-capacity scanners were operational, with whole slide images generated on greater than 2000 slides per weekday, accounting for approximately 80% of histologic slides at Stanford Medicine. Multiple modifications in workflow and information technology were needed to improve performance. Within months of full implementation, most attending pathologists and trainees had adopted WSI for primary diagnosis. WSI across all surgical subspecialities is achievable at scale at an academic medical center; however, adoption required flexibility to adjust workflows and develop tailored solutions. WSI at scale supported the health and safety of medical staff while facilitating high-quality patient care and education during COVID-19 restrictions.

Application of α-Syn Real-Time Quaking-Induced Conversion for Brain and Skin Specimens of the Chinese Patients With Parkinson's Disease.

The real-time quaking-induced conversion (RT-QuIC) assay has been developed and used as an in vitro diagnostic tool for Parkinson’s disease (PD). In this study, we established α-Syn RT-QuIC using recombinant human α-Syn as the substrate. All 5 brain homogenates of neuropathological PD cases and 13 skin homogenates of clinical PD cases showed positive results, whereas all the samples of negative controls remain negative. Meantime, randomly selected 6 skin samples of PD cases and 6 skin samples of sCJD cases showed negative in opposite prion RT-QuIC and α-Syn RT-QuIC. Our α-Syn RT-QuIC showed dose-dependent manner between the lag times and peak ThT fluorescent values. Additionally, the detecting limitation was about 10–7 dilution for brain tissues and 10–6 for skins. Those data indicate a reliable specificity and good sensitivity of the established α-Syn RT-QuIC in identifying and amplifying the misfolded α-Syn in brain and skin tissues of patients with PD.

Freiburg Neuropathology Case Conference

Freiburg Neuropathology Case Conference

Integrating morphologic and molecular histopathological features through whole slide image registration and deep learning.

BackgroundModern molecular pathology workflows in neuro-oncology heavily rely on the integration of morphologic and immunohistochemical patterns for analysis, classification, and prognostication. However, despite the recent emergence of digital pathology platforms and artificial intelligence-driven computational image analysis tools, automating the integration of histomorphologic information found across these multiple studies is challenged by large files sizes of whole slide images (WSIs) and shifts/rotations in tissue sections introduced during slide preparation.MethodsTo address this, we develop a workflow that couples different computer vision tools including scale-invariant feature transform (SIFT) and deep learning to efficiently align and integrate histopathological information found across multiple independent studies. We highlight the utility and automation potential of this workflow in the molecular subclassification and discovery of previously unappreciated spatial patterns in diffuse gliomas.ResultsFirst, we show how a SIFT-driven computer vision workflow was effective at automated WSI alignment in a cohort of 107 randomly selected surgical neuropathology cases (97/107 (91%) showing appropriate matches, AUC = 0.96). This alignment allows our AI-driven diagnostic workflow to not only differentiate different brain tumor types, but also integrate and carry out molecular subclassification of diffuse gliomas using relevant immunohistochemical biomarkers (IDH1-R132H, ATRX). To highlight the discovery potential of this workflow, we also examined spatial distributions of tumors showing heterogenous expression of the proliferation marker MIB1 and Olig2. This analysis helped uncover an interesting and unappreciated association of Olig2 positive and proliferative areas in some gliomas (r = 0.62).ConclusionThis efficient neuropathologist-inspired workflow provides a generalizable approach to help automate a variety of advanced immunohistochemically compatible diagnostic and discovery exercises in surgical neuropathology and neuro-oncology.

Digital media archive for gross pathology images based on open-source tools and Fast Healthcare Interoperability Resources (FHIR).

Macroscopic examination of surgical pathology and autopsy cases is a fundamental component of anatomic pathology. The photographic documentation of such clinical specimens is essential, and it may be required in certain instances. Our department began using consumer-grade digital cameras in 2005 to improve the practice of gross photography. However, the lack of an application to correctly catalog the photographs resulted in thousands of digital image files scattered across shared network drives, with limited case and patient metadata, making image retrieval a cumbersome and sometimes impossible task. Thirteen years later, we examined the legacy method of acquiring and accessing gross photographs in our department and determined the need for a web-based digital media archive to capture images with structured metadata. Using several open-source tools, including MediaWiki, we developed a flexible platform for building our digital media archive with a data schema based on the Fast Healthcare Interoperability Resources standard. Following a short pilot, we replaced the legacy method of handling gross pathology images with a new acquisition workflow and digital media archive. Through March 2021, 233 distinct users have accessed the system, 58 of which have uploaded 21,024 images. Of those images, 13,684 (65.1%) correspond to surgical pathology images, 4045 (19.2%) belong to neuropathology cases, and 3295 (15.7%) originate from autopsies. We demonstrate the design and implementation of a customizable anatomic pathology digital media archive solution in an academic pathology department setting using a modern standard for exchanging healthcare information electronically. The system’s efficiency and scalability for our current operation will enable us to integrate with other applications and pathology informatics initiatives in the future.

Localizing Clinical Patterns of Blast Traumatic Brain Injury Through Computational Modeling and Simulation.

Blast traumatic brain injury is ubiquitous in modern military conflict with significant morbidity and mortality. Yet the mechanism by which blast overpressure waves cause specific intracranial injury in humans remains unclear. Reviewing of both the clinical experience of neurointensivists and neurosurgeons who treated service members exposed to blast have revealed a pattern of injury to cerebral blood vessels, manifested as subarachnoid hemorrhage, pseudoaneurysm, and early diffuse cerebral edema. Additionally, a seminal neuropathologic case series of victims of blast traumatic brain injury (TBI) showed unique astroglial scarring patterns at the following tissue interfaces: subpial glial plate, perivascular, periventricular, and cerebral gray-white interface. The uniting feature of both the clinical and neuropathologic findings in blast TBI is the co-location of injury to material interfaces, be it solid-fluid or solid-solid interface. This motivates the hypothesis that blast TBI is an injury at the intracranial mechanical interfaces. In order to investigate the intracranial interface dynamics, we performed a novel set of computational simulations using a model human head simplified but containing models of gyri, sulci, cerebrospinal fluid (CSF), ventricles, and vasculature with high spatial resolution of the mechanical interfaces. Simulations were performed within a hybrid Eulerian—Lagrangian simulation suite (CTH coupled via Zapotec to Sierra Mechanics). Because of the large computational meshes, simulations required high performance computing resources. Twenty simulations were performed across multiple exposure scenarios—overpressures of 150, 250, and 500 kPa with 1 ms overpressure durations—for multiple blast exposures (front blast, side blast, and wall blast) across large variations in material model parameters (brain shear properties, skull elastic moduli). All simulations predict fluid cavitation within CSF (where intracerebral vasculature reside) with cavitation occurring deep and diffusely into cerebral sulci. These cavitation events are adjacent to high interface strain rates at the subpial glial plate. Larger overpressure simulations (250 and 500kPa) demonstrated intraventricular cavitation—also associated with adjacent high periventricular strain rates. Additionally, models of embedded intraparenchymal vascular structures—with diameters as small as 0.6 mm—predicted intravascular cavitation with adjacent high perivascular strain rates. The co-location of local maxima of strain rates near several of the regions that appear to be preferentially damaged in blast TBI (vascular structures, subpial glial plate, perivascular regions, and periventricular regions) suggest that intracranial interface dynamics may be important in understanding how blast overpressures leads to intracranial injury.

Don’t Judge a Book by Its Cover: A Clinical Neuropathological Conference Case

Don’t Judge a Book by Its Cover: A Clinical Neuropathological Conference Case