The Multifaceted Appearance of Supratentorial Ependymoma with ZFTA-MAML2 Fusion

Ependymomas are glial neoplasms with a wide morphological spectrum. The majority of supratentorial ependymomas are known to harbor ZFTA fusions, most commonly to RELA. We present an unusual case of a 9-year-old boy with a supratentorial ependymoma harboring a noncanonical ZFTA-MAML2 fusion. This case had unusual histomorphological features lacking typical findings of ependymoma and bearing resemblance to a primitive neoplasm with focal, previously undescribed myogenic differentiation. We discuss the diagnostic pitfalls in this case and briefly review the histological features of ependymoma with noncanonical gene fusions. Our report underscores the importance of molecular testing in such cases to arrive at the correct diagnosis. Supratentorial ependymomas with noncanonical fusions are rare, and more studies are necessary for better risk stratification and identification of potential treatment targets

Diagnosis and Management of Radiation Necrosis in Patients With Brain Metastases

The use of radiotherapy, either in the form of stereotactic radiosurgery (SRS) or whole-brain radiotherapy (WBRT), remains the cornerstone for the treatment of brain metastases (BM). As the survival of patients with BM is being prolonged, due to improved systemic therapy (i.e., for better extra-cranial control) and increased use of SRS (i.e., for improved intra-cranial control), patients are clinically manifesting late effects of radiotherapy. One of these late effects is radiation necrosis (RN). Unfortunately, symptomatic RN is notoriously hard to diagnose and manage. The features of RN overlap considerably with tumor recurrence, and misdiagnosing RN as tumor recurrence may lead to deleterious treatment which may cause detrimental effects to the patient. In this review, we will explore the pathophysiology of RN, risk factors for its development, and the strategies to evaluate and manage RN

A case of coexisting Warthin tumor and langerhans cell histiocytosis associated with necrosis, eosinophilic abscesses and a granulomatous reaction in intraparotid lymph nodes

We present a patient (50-year-old male) with coexisting Warthin tumor and involvement of two intraparotid lymph nodes by Langerhans cell histiocytosis associated with necrosis, eosinophilic abscesses and a granulomatous reaction. This is the second documented case of this unusual combination of histological changes in nodal Langerhans cell histiocytosis and the first case involving intraparotid lymph nodes occurring together with an ipsilateral Warthin tumor

A comprehensive AI model development framework for consistent Gleason grading

Background: Artificial Intelligence(AI)-based solutions for Gleason grading hold promise for pathologists, while image quality inconsistency, continuous data integration needs, and limited generalizability hinder their adoption and scalability. Methods: We present a comprehensive digital pathology workflow for AI-assisted Gleason grading. It incorporates A!MagQC (image quality control), A!HistoClouds (cloud-based annotation), Pathologist-AI Interaction (PAI) for continuous model improvement, Trained on Akoya-scanned images only, the model utilizes color augmentation and image appearance migration to address scanner variations. We evaluate it on Whole Slide Images (WSI) from another five scanners and conduct validations with pathologists to assess AI efficacy and PAI. Results: Our model achieves an average F1 score of 0.80 on annotations and 0.71 Quadratic Weighted Kappa on WSIs for Akoya-scanned images. Applying our generalization solution increases the average F1 score for Gleason pattern detection from 0.73 to 0.88 on images from other scanners. The model accelerates Gleason scoring time by 43% while maintaining accuracy. Additionally, PAI improve annotation efficiency by 2.5 times and led to further improvements in model performance. Conclusions: This pipeline represents a notable advancement in AI-assisted Gleason grading for improved consistency, accuracy, and efficiency. Unlike previous methods limited by scanner specificity, our model achieves outstanding performance across diverse scanners. This improvement paves the way for its seamless integration into clinical workflows

Histological approach to neuronal and mixed neuronal-glial tumors of the central nervous system

The 2016 updated World Health Organization Classification of Tumors of the Central Nervous System shows an increasing number of entities under the classification of neuronal and mixed neuronal-glial tumors. Despite being a biogenetically heterogeneous group of tumors, the members frequently display some overlapping histological and clinical features, leading to diagnostic dilemmas among neuropathologists, especially when the aid of advanced molecular and immunohistochemical tools is not available. Nonetheless, meticulous assessment of the morphological features with careful interpretation of the immunophenotypes can be rewarding often without the investment of an expensive molecular investigation. We propose a method of approaching the neuronal-glial tumors based on pattern recognition. We briefly discuss the key histological features that are helpful in narrowing down the differentials, with the aid of immunohistochemistry or available molecular information, directing the pathologist toward the correct diagnosis

Detecting Tumor Infiltration in Diffuse Gliomas with Deep Learning

Glioblastoma tumor recurrences often occur in brain tissue areas harboring infiltrating tumor cells, resembling healthy tissue in brain imaging. Demarcating infiltrative regions for aggressive resections is critical for improving prognostic outcomes but is challenging in neurosurgery. Herein, a multilayer sigmoid‐activated convolutional neural network (MLS‐CNN) is developed for rapidly distinguishing glioma tumor infiltration in brain tissue histology. Unlike conventional multiclass classifiers, the MLS‐CNN employs sigmoidal activation to accommodate coexisting classes within patch images. 59 811 image patches (25 807 infiltrating edge, 15 178 normal brain, 18 826 cellular tumor) from 73 brain tissue samples are extracted to train the classifier. The model achieves an accuracy of 91.70% (sensitivity: 91.62%; specificity: 91.78%) and area under the curve (AUC) of 0.964 in distinguishing infiltrating edges, outperforming the current state‐of‐the‐art Vision Transformer (ViT) (accuracy: 89.45; AUC: 0.947). The MLS‐CNN is computationally efficient, generating predictions within 11.5 s in comparison to 81.4 s for ViT. The predictions strongly correlate with In Situ Hybridization expression intensities, validating the utility of the MLS‐CNN model in spatial genomics investigations in gliomas. The robust model can therefore serve as an automatic and accurate classifier to help pathologists identify infiltrative glioma for better diagnosis and patient outcomes in brain oncology

IgG4-positive extranodal marginal zone lymphoma arising in Hashimoto's thyroiditis: clinicopathological and cytogenetic features of a hitherto undescribed condition

10.1111/his.12854HISTOPATHOLOGY686931-937UNITED KINGDO