Al-Asi, Hussien

Post-transplant lymphoproliferative disorder (PTLD) involving the central nervous system (CNS) is a rare but serious life-threatening complication seen in recipients of solid organ transplant. Primary CNS encompasses 5–15% of all types of PTLD diagnoses, and heart transplant recipients represent 3–5% of those reported cases. Diagnosis is often delayed due to the highly variable presentation, with some cases remaining undiagnosed for years. Multidisciplinary collaboration is crucial for early diagnosis and management. A 53-year-old woman patient presented with altered mental status. MRI revealed nodular ventriculitis and bilateral periventricular hyperdense infiltrates. CSF studies demonstrated lymphocytic pleocytosis, elevated protein, and EBV-PCR-positive results. A stereotactic brain needle biopsy confirmed the presence of EBV-positive diffuse large B-cell lymphoma, consistent with primary CNS PTLD, 14 months after her heart transplant. Despite appropriate management, the patient experienced progressive neurological decline and ultimately suffered a fatal intracerebral hemorrhage. We demonstrate the importance of the early diagnosis and variable presentation of post-heart-transplant PTLD. The importance of surveillance regardless of EBV status and close monitoring of disease progression due to potential life-threatening complications, such as fatal hemorrhages. Therefore, primary CNS-PTLD remains a challenging disease and is being increasingly recognized with improved transplant recipient survival and prolonged exposure to chronic immunosuppression.

Foundation models are reshaping computational pathology by enabling scalable task-agnostic representations of histopathological whole-slide images (WSIs). Unlike earlier task-specific deep learning systems, pathology foundation models (PFMs) leverage massive whole-slide image repositories and self-supervised Vision Transformer architectures to achieve broad generalization and few-shot adaptability. Their evolution reflects a shift from weakly supervised approaches such as Clustering-Constrained Attention Multiple Instance Learning (CLAM) and hierarchical architectures such as Hierarchical Image Pyramid Transformer (HIPT) to large-scale efforts including foundation models, UNI, Virchow, Phikon, CONtrastive learning from Captions for Histopathology (CONCH), GigaPath, H-Optimus, Transformer-Based Pathology Image and Text Alignment Network (TITAN), and the Mayo Clinic Atlas. These models demonstrate impressive performance across diagnostic and prognostic benchmarks while also opening pathways for multimodal integration with genomics and clinical data. Yet significant barriers remain including inconsistent generalization across institutions, interpretability lagging behind clinical needs, and slow integration into routine laboratory workflows. Certain domains of anatomic pathology such as cytopathology, transplant pathology, frozen sections, and rare tumor subtypes remain particularly resistant to current models. Here, we review the development of PFMs, critically evaluate their strengths and limitations, and outline priorities for their safe and effective clinical translation. We argue that the next phase of PFM development will depend on rigorous benchmarking, pathologist-in-the-loop deployment, and multimodal fusion ensuring these models evolve from research tools into clinically robust systems.

Artificial Intelligence (AI) has the potential to revolutionize cytopathology by enhancing diagnostic accuracy, efficiency, and accessibility. However, the implementation of AI in this field presents significant challenges and opportunities. This review paper explores the current landscape of AI applications in cytopathology, highlighting the critical challenges, including data quality and availability, algorithm development, integration and standardization, and clinical validation. We discuss challenges such as the limitation of only one optical section and z-stack scanning, the complexities associated with acquiring high-quality labeled data, the intricacies of developing robust and generalizable AI models, and the difficulties in integrating AI tools into existing laboratory workflows. The review also identifies substantial opportunities that AI brings to cytopathology. These include the potential for improved diagnostic accuracy through enhanced detection capabilities and consistent, reproducible results, which can reduce observer variability. AI-driven automation of routine tasks can significantly increase efficiency, allowing cytopathologists to focus on more complex analyses. Furthermore, AI can serve as a valuable educational tool, augmenting the training of cytopathologists and facilitating global health initiatives by making high-quality diagnostics accessible in resource-limited settings. The review underscores the importance of addressing these challenges to harness the full potential of AI in cytopathology, ultimately improving patient care and outcomes.