Citations

Abstract: Breast cancer1-associated protein 1 (BAP-1)-inactivated melanocytic tumors are a group of familial or sporadic lesions with distinctive histology and molecular features. Inherited germline inactivating mutations in BAP1 have been associated with the development of multiple epithelioid melanocytic neoplasms resembling Spitz nevi and increased susceptibility for developing several malignancies, including uveal melanoma, cutaneous melanoma, renal cell carcinoma, mesothelioma, and other tumors. Cutaneous melanoma with loss of BAP1 expression is rare. We present a unique case of BAP1 -inactivated melanoma with anaplastic lymphoma kinase ( ALK ) fusion arising in a pre-existing BAP1 -inactivated nevus in a 47-year-old female patient who presented with a dome-shaped red papule on the superior crus of the right antihelix. Histology revealed intradermal melanocytic proliferation with biphenotypic morphology. There was a proliferation of atypical melanocytes showing epithelioid features in the background of nevus. Mitotic figures were identified in the cytologically atypical component of the lesion. Mart-1/Ki67 dual stain demonstrated a higher proliferation index in the larger epithelioid atypical cells, supporting the diagnosis of melanoma. Nuclear BAP-1 expression was lost in the larger atypical cells and associated nevoid cells. Preferentially expressed antigen in melanoma stain demonstrated focal positive staining in 20%–30% of the melanocytes. Immunostaining for B-Raf proto-oncogene, serine/threonine kinase V600E was diffusely positive and ALK demonstrated patchy immunoreactivity in the melanocytic proliferation. Interphase fluorescence in situ hybridization studies showed gains at chromosome 6p25 (Ras responsive element binding protein 1) in the tumor cells. The comprehensive next-generation sequencing revealed B-Raf proto-oncogene, serine/threonine kinase V600E mutation, TP53 mutation, ALK fusion, BAP1 loss (copy number variation = 0.0, potentially germline), and loss of MAP2K7, Von Hippel-Lindau tumor suppressor , FGFR3, CDKN2A , 19q, and telomerase reverse transcriptase . The tumor was microsatellite stable with a low tumor mutational burden (5.76 mutations/Mb). The tumor was completely excised with negative margins. The patient is doing well at 17 months follow-up with no signs of recurrence.

Context.— Computational pathology combines clinical pathology with computational analysis, aiming to enhance diagnostic capabilities and improve clinical productivity. However, communication barriers between pathologists and developers often hinder the full realization of this potential. Objective.— To propose a standardized framework that improves mutual understanding of clinical objectives and computational methodologies. The goal is to enhance the development and application of computer-aided diagnostic (CAD) tools. Design.— This article suggests pivotal roles for pathologists and computer scientists in the CAD development process. It calls for increased understanding of computational terminologies, processes, and limitations among pathologists. Similarly, it argues that computer scientists should better comprehend the true use cases of the developed algorithms to avoid clinically meaningless metrics. Results.— CAD tools improve pathology practice significantly. Some tools have even received US Food and Drug Administration approval. However, improved understanding of machine learning models among pathologists is essential to prevent misuse and misinterpretation. There is also a need for a more accurate representation of the algorithms’ performance compared to that of pathologists. Conclusions.— A comprehensive understanding of computational and clinical paradigms is crucial for overcoming the translational gap in computational pathology. This mutual comprehension will improve patient care through more accurate and efficient disease diagnosis.

As artificial intelligence (AI) integrates within the intersecting domains of healthcare and computational biology, developing interpretable models tailored to medical contexts is met with significant challenges. Explainable AI (XAI) is vital for fostering trust and enabling effective use of AI in healthcare, particularly in image-based specialties such as pathology and radiology where adjunctive AI solutions for diagnostic image analysis are increasingly utilized. Overcoming these challenges necessitates interdisciplinary collaboration, essential for advancing XAI to enhance patient care. This commentary underscores the critical role of interdisciplinary conferences in promoting the necessary cross-disciplinary exchange for XAI innovation. A literature review was conducted to identify key challenges, best practices, and case studies related to interdisciplinary collaboration for XAI in healthcare. The distinctive contributions of specialized conferences in fostering dialogue, driving innovation, and influencing research directions were scrutinized. Best practices and recommendations for fostering collaboration, organizing conferences, and achieving targeted XAI solutions were adapted from the literature. By enabling crucial collaborative junctures that drive XAI progress, interdisciplinary conferences integrate diverse insights to produce new ideas, identify knowledge gaps, crystallize solutions, and spur long-term partnerships that generate high-impact research. Thoughtful structuring of these events, such as including sessions focused on theoretical foundations, real-world applications, and standardized evaluation, along with ample networking opportunities, is key to directing varied expertise toward overcoming core challenges. Successful collaborations depend on building mutual understanding and respect, clear communication, defined roles, and a shared commitment to the ethical development of robust, interpretable models. Specialized conferences are essential to shape the future of explainable AI and computational biology, contributing to improved patient outcomes and healthcare innovations. Recognizing the catalytic power of this collaborative model is key to accelerating the innovation and implementation of interpretable AI in medicine.

Background: Atypical fibroxanthoma (AFX) is a dermal-based, low-grade neoplasm with no specific lineage of differentiation. The occurrence of AFX with osteoclast-like giant cells is exceptionally rare. Less than 20 cases have been reported in the literature. Case Presentation: A 77-year-old man with a medical history of multiple basal and squamous cell carcinomas of the skin, presented with a progressively growing erythematous nodule on the sun-damaged right central parietal scalp. A shave biopsy showed a dermal spindle cell proliferation accompanied by numerous osteoclast-like multinucleated giant cells and predominant atypical mitotic figures. The immunohistochemical staining showed a diffuse positive staining for CD68 and SMA, patchy staining for CD10, and negative staining for SOX-10, pan-cytokeratin, CK5/6, S100, CD34, and desmin. The tumor was completely excised with negative margins. A subsequent follow-up over a period of 13 months showed no recurrence. Conclusion: Distinguishing AFX with osteoclast-like giant cells from both malignant and benign skin lesions with osteoclast-like giant cells is crucial. Although AFX tumors display worrisome malignant histologic features, most cases have a favorable prognosis with a local recurrence rate below 5% and exceedingly rare metastasis.

Abstract Background Network-connected medical devices have rapidly proliferated in the wake of recent global catalysts, leaving clinical laboratories and healthcare organizations vulnerable to malicious actors seeking to ransom sensitive healthcare information. As organizations become increasingly dependent on integrated systems and data-driven patient care operations, a sudden cyberattack and the associated downtime can have a devastating impact on patient care and the institution as a whole. Cybersecurity, information security, and information assurance principles are, therefore, vital for clinical laboratories to fully prepare for what has now become inevitable, future cyberattacks. Content This review aims to provide a basic understanding of cybersecurity, information security, and information assurance principles as they relate to healthcare and the clinical laboratories. Common cybersecurity risks and threats are defined in addition to current proactive and reactive cybersecurity controls. Information assurance strategies are reviewed, including traditional castle-and-moat and zero-trust security models. Finally, ways in which clinical laboratories can prepare for an eventual cyberattack with extended downtime are discussed. Summary The future of healthcare is intimately tied to technology, interoperability, and data to deliver the highest quality of patient care. Understanding cybersecurity and information assurance is just the first preparative step for clinical laboratories as they ensure the protection of patient data and the continuity of their operations.

Diagnostic devices, methodological approaches, and traditional constructs of clinical pathology practice, cultivated throughout centuries, have transformed radically in the wake of explosive technological growth and other, e.g., environmental, catalysts of change. Ushered into the fray of modern laboratory medicine are digital imaging devices and machine-learning (ML) software fashioned to mitigate challenges, e.g., practitioner shortage while preparing clinicians for emerging interconnectivity of environments and diagnostic information in the era of big data. As computer vision shapes new constructs for the modern world and intertwines with clinical medicine, cultivating clarity of our new terrain through examining the trajectory and current scope of computational pathology and its pertinence to clinical practice is vital. Through review of numerous studies, we find developmental efforts for ML migrating from research to standardized clinical frameworks while overcoming obstacles that have formerly curtailed adoption of these tools, e.g., generalizability, data availability, and user-friendly accessibility. Groundbreaking validatory efforts have facilitated the clinical deployment of ML tools demonstrating the capacity to effectively aid in distinguishing tumor subtype and grade, classify early vs. advanced cancer stages, and assist in quality control and primary diagnosis applications. Case studies have demonstrated the benefits of streamlined, digitized workflows for practitioners alleviated by decreased burdens.