Foundation models for fast, label-free detection of glioma infiltration.

A critical challenge in glioma treatment is detecting tumour infiltration during surgery to achieve safe maximal resection. Unfortunately, safely resectable residual tumour is found in the majority of patients with glioma after surgery, causing early recurrence and decreased survival. Here we present FastGlioma, a visual foundation model for fast (<10 s) and accurate detection of glioma infiltration in fresh, unprocessed surgical tissue.

Super-resolution of biomedical volumes with 2D supervision.

Volumetric biomedical microscopy has the potential to increase the diagnostic information extracted from clinical tissue specimens and improve the diagnostic accuracy of both human pathologists and computational pathology models. Unfortunately, barriers to integrating 3-dimensional (3D) volumetric microscopy into clinical medicine include long imaging times, poor depth/z-axis resolution, and an insufficient amount of high-quality volumetric data. Leveraging the abundance of high-resolution 2D microscopy data, we introduce masked slice diffusion for super-resolution (MSDSR), which exploits the inherent equivalence in the data-generating distribution across all spatial dimensions of biological specimens.

Localization of protoporphyrin IX during glioma-resection surgery via paired stimulated Raman histology and fluorescence microscopy.

The most widely used fluorophore in glioma-resection surgery, 5-aminolevulinic acid (5-ALA), is thought to cause the selective accumulation of fluorescent protoporphyrin IX (PpIX) in tumour cells. Here we show that the clinical detection of PpIX can be improved via a microscope that performs paired stimulated Raman histology and two-photon excitation fluorescence microscopy (TPEF). We validated the technique in fresh tumour specimens from 115 patients with high-grade gliomas across four medical institutions.

Development and validation of an artificial intelligence model to accurately predict spinopelvic parameters.

Objective: Achieving appropriate spinopelvic alignment has been shown to be associated with improved clinical symptoms. However, measurement of spinopelvic radiographic parameters is time-intensive and interobserver reliability is a concern. Automated measurement tools have the promise of rapid and consistent measurements, but existing tools are still limited to some degree by manual user-entry requirements.

Hierarchical discriminative learning improves visual representations of biomedical microscopy.

Learning high-quality, self-supervised, visual representations is essential to advance the role of computer vision in biomedical microscopy and clinical medicine. Previous work has focused on self-supervised representation learning (SSL) methods developed for instance discrimination and applied them directly to image patches, or fields-of-view, sampled from gigapixel whole-slide images (WSIs) used for cancer diagnosis. However, this strategy is limited because it (1) assumes patches from the same patient are independent, (2) neglects the patient-slide-patch hierarchy of clinical biomedical microscopy, and (3) requires strong data augmentations that can degrade downstream performance.

Unlocking glioma genetics with deep learning.

The AI era in medicine has ushered in new opportunities to improve the diagnosis and treatment of human disease. CHARM, an AI algorithm described in this issue, has the potential to streamline molecular classification, intraoperative diagnosis, surgical decision making, and trial enrollment for glioma patients.

OpenSRH: optimizing brain tumor surgery using intraoperative stimulated Raman histology.

Accurate intraoperative diagnosis is essential for providing safe and effective care during brain tumor surgery. Our standard-of-care diagnostic methods are time, resource, and labor intensive, which restricts access to optimal surgical treatments. To address these limitations, we propose an alternative workflow that combines stimulated Raman histology (SRH), a rapid optical imaging method, with deep learning-based automated interpretation of SRH images for intraoperative brain tumor diagnosis and real-time surgical decision support.

A comparison of ventricular volume and linear indices in predicting shunt dependence in aneurysmal subarachnoid hemorrhage.

Background: Guidelines for determining shunt dependence after aneurysmal subarachnoid hemorrhage (aSAH) remain unclear. We previously demonstrated change in ventricular volume (VV) between head CT scans taken pre- and post-EVD clamping was predictive of shunt dependence in aSAH. We sought to compare the predictive value of this measure to more commonly used linear indices.

Artificial-intelligence-based molecular classification of diffuse gliomas using rapid, label-free optical imaging.

Molecular classification has transformed the management of brain tumors by enabling more accurate prognostication and personalized treatment. However, timely molecular diagnostic testing for patients with brain tumors is limited, complicating surgical and adjuvant treatment and obstructing clinical trial enrollment. In this study, we developed DeepGlioma, a rapid (<90 seconds), artificial-intelligence-based diagnostic screening system to streamline the molecular diagnosis of diffuse gliomas.

Generation of 3D ex vivo mouse- and patient-derived glioma explant slice model for integration of confocal time-lapse imaging and spatial analysis.

Development of spatial-integrative pre-clinical models is needed for glioblastoma, which are heterogenous tumors with poor prognosis. Here, we present an optimized protocol to generate three-dimensional ex vivo explant slice glioma model from orthotopic tumors, genetically engineered mouse models, and fresh patient-derived specimens. We describe a step-by-step workflow for tissue acquisition, dissection, and sectioning of 300-μm tumor slices maintaining cell viability.

Subclonal evolution and expansion of spatially distinct THY1-positive cells is associated with recurrence in glioblastoma.

Purpose: Glioblastoma(GBM) is a lethal disease characterized by inevitable recurrence. Here we investigate the molecular pathways mediating resistance, with the goal of identifying novel therapeutic opportunities.

Experimental Design: We developed a longitudinal in vivo recurrence model utilizing patient-derived explants to produce paired specimens(pre- and post-recurrence) following temozolomide(TMZ) and radiation(IR).

Perigeniculate Giant Cell Tumor of Temporal Bone.

Giant cell tumors of bone (GCTBs) are benign osteolytic neoplasms that can be treated with either gross-total resection or subtotal resection with adjuvant radiotherapy. For the rare GCTB of the temporal bone, close proximity to critical structures can produce functional deficits and make gross-total resection difficult to achieve without significant morbidity. We present the case of a 28-year-old woman with progressive facial paresis, otalgia, neck pain, imbalance, and subjective hearing loss.

Generating novel pituitary datasets from open-source imaging data and deep volumetric segmentation.

Purpose: The estimated incidence of pituitary adenomas in the general population is 10-30%, yet radiographic diagnosis remains a challenge. Diagnosis is complicated by the heterogeneity of radiographic features in both normal (e.g.

Spatiotemporal analysis of glioma heterogeneity reveals COL1A1 as an actionable target to disrupt tumor progression.

Intra-tumoral heterogeneity is a hallmark of glioblastoma that challenges treatment efficacy. However, the mechanisms that set up tumor heterogeneity and tumor cell migration remain poorly understood. Herein, we present a comprehensive spatiotemporal study that aligns distinctive intra-tumoral histopathological structures, oncostreams, with dynamic properties and a specific, actionable, spatial transcriptomic signature.

Extent of Resection Research in Skull Base Neurosurgery: Previous Studies and Future Directions.

Surgery is the first-line therapy for most benign and malignant skull base tumors. Extent of resection (EOR) is a metric commonly used for preoperative surgical planning and to predict risk of postoperative tumor recurrence. Therefore, understanding the evidence on EOR in skull base neurosurgery is essential to providing optimal care for each patient.

Rapid Automated Analysis of Skull Base Tumor Specimens Using Intraoperative Optical Imaging and Artificial Intelligence.

Background: Accurate specimen analysis of skull base tumors is essential for providing personalized surgical treatment strategies. Intraoperative specimen interpretation can be challenging because of the wide range of skull base pathologies and lack of intraoperative pathology resources.

Objective: To develop an independent and parallel intraoperative workflow that can provide rapid and accurate skull base tumor specimen analysis using label-free optical imaging and artificial intelligence.

Laminectomy at T4 and T5 for Resection of Symptomatic Cavernous Malformation.

Although rare, intramedullary spinal cavernous malformations have a 1.4%-6.8% annual hemorrhage risk and can cause significant morbidity.

Cranio-Orbital Approach for Single-Stage En Bloc Resection of Optic Nerve Glioma: Technical Note.

Background And Importance: There is no consensus on the optimal surgical approach for managing optic nerve gliomas. For solely intraorbital tumors, a single-stage lateral orbitotomy approach for resection may be performed, but when the nerve within the optic canal is affected, two-stage cranial and orbital approaches are often used. The authors describe their technique to safely achieve aggressive nerve resection to minimize the probability of recurrence that might affect the optic tracts, optic chiasm, and contralateral optic nerve.

Far Lateral Craniotomy for Obliteration of High-Risk Craniocervical Junction Arteriovenous Fistula.

Background And Introduction: Dural arteriovenous fistulas (dAVFs) are a rare pathology with a clinical presentation related to their anatomical location. Craniocervical junction (CCJ) dAVFs are challenging to treat given the delicate structures that surround the CCJ. Endovascular treatment has evolved significantly in the past decade, but open microsurgery remains an invaluable tool for this pathology.