Journal of Pathology Informatics最新文献

{"title":"Digital Pathology Standards: A Response to WG-26","authors":"Peter Gershkovich","doi":"10.1016/j.jpi.2025.100510","DOIUrl":"10.1016/j.jpi.2025.100510","url":null,"abstract":"","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"19 ","pages":"Article 100510"},"PeriodicalIF":0.0,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144933546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Raining on the WSI interoperability parade – incorrect assertions with respect to DICOM and fur coats in the summertime","authors":"David A. Clunie","doi":"10.1016/j.jpi.2025.100511","DOIUrl":"10.1016/j.jpi.2025.100511","url":null,"abstract":"","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"19 ","pages":"Article 100511"},"PeriodicalIF":0.0,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144917923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Wearing a fur coat in the summertime: Should digital pathology redefine medical imaging?","authors":"Peter Gershkovich","doi":"10.1016/j.jpi.2025.100450","DOIUrl":"10.1016/j.jpi.2025.100450","url":null,"abstract":"<div><div>Slides are data. Once digitized, they function like any enterprise asset: accessible anywhere, ready for AI, and integrated into cloud workflows. But in pathology, they enter a realm of clinical complexity—demanding systems that handle nuance, integrate diverse data streams, scale effectively, enable computational exploration, and enforce rigorous security.</div><div>Although the Digital Imaging and Communications in Medicine (DICOM) standard revolutionized radiology, it is imperative to explore its adequacy in addressing modern digital pathology's orchestration needs. Designed more than 30 years ago, DICOM reflects assumptions and architectural choices that predate modular software, cloud computing, and AI-driven workflows.</div><div>This article shows that by embedding metadata, annotations, and communication protocols into a unified container, DICOM limits interoperability and exposes architectural vulnerabilities. The article begins by examining these innate design risks, then challenges DICOM's interoperability claims, and ultimately presents a modular, standards-aligned alternative.</div><div>The article argues that separating image data from orchestration logic improves scalability, security, and performance. Standards such as HL7 FHIR (Health Level Seven Fast Healthcare Interoperability Resources) and modern databases manage clinical metadata; formats like Scalable Vector Graphics handle annotations; and fast, cloud-native file transfer protocols, and microservices support tile-level image access. This separation of concerns allows each component to evolve independently, optimizes performance across the system, and better adapts to emerging AI-driven workflows—capabilities that are inherently constrained in monolithic architectures where these elements are tightly coupled.</div><div>It further shows that security requirements should not be embedded within the DICOM standard itself. Instead, security must be addressed through a layered, format-independent framework that spans systems, networks, applications, and data governance. Security is not a discrete feature but an overarching discipline—defined by its own evolving set of standards and best practices. Overlays such as those outlined in the National Institute of Standards and Technology SP 800-53 support modern Transport Layer Security, single sign-on, cryptographic hashing, and other controls that protect data streams without imposing architectural constraints or restricting technological choices.</div><div>Pathology stands at a rare inflection point. Unlike radiology, where DICOM is deeply entrenched, pathology workflows still operate in polyglot environments—leveraging proprietary formats, hybrid standards, and emerging cloud-native tools. This diversity, often seen as a limitation, offers a clean slate: an opportunity to architect a modern, modular infrastructure free from legacy constraints. While a full departure from DICOM is unnecessary, pathology is uniquely position","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"18 ","pages":"Article 100450"},"PeriodicalIF":0.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144925135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative analysis of a 5G campus network and existing networks for real-time consultation in remote pathology","authors":"Ilgar I. Guseinov,&nbsp;Arnab Bhowmik,&nbsp;Somaia AbuBaker,&nbsp;Anna E. Schmaus-Klughammer,&nbsp;Thomas Spittler","doi":"10.1016/j.jpi.2025.100444","DOIUrl":"10.1016/j.jpi.2025.100444","url":null,"abstract":"<div><div>The rapid advancements in digital pathology, particularly in whole-slide imaging (WSI), have transformed remote histological analysis by enabling high-resolution digitization and real-time consultations. However, these workflows place significant demands on network infrastructure, requiring high bandwidth, low latency, and consistent performance. Whereas 5G networks have been extensively studied in controlled lab environments, their real-world applications in clinical settings remain underexplored.</div><div>This study provides a comparative analysis of 5G Campus Networks (5G CN) and traditional institutional networks, focusing on their performance during remote pathology tasks. Key metrics such as throughput, latency, and image quality were evaluated under various device loads to simulate real-world conditions. Although 5G CN did not consistently outperform in absolute throughput, it demonstrated superior adaptability, lower latency, and reduced variability, ensuring stable performance even with increasing network demand. These attributes are critical for time-sensitive workflows like frozen section analysis, where reliability and speed are paramount.</div><div>The findings highlight the potential of 5G CN to support emerging digital pathology applications, including real-time consultation. Furthermore, the study underscores the need for future research on 5G slicing and its ability to optimize network resources for high-demand medical applications. This work provides valuable insights into optimizing network infrastructure for the evolving demands of remote diagnostics in digital pathology.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"18 ","pages":"Article 100444"},"PeriodicalIF":0.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144925495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digital slide scanning at scale: Comparison of whole slide imaging devices in a clinical setting","authors":"Orly Ardon ,&nbsp;Allyne Manzo ,&nbsp;Jamaal Spencer ,&nbsp;Victor E. Reuter ,&nbsp;Meera Hameed ,&nbsp;Matthew G. Hanna","doi":"10.1016/j.jpi.2025.100446","DOIUrl":"10.1016/j.jpi.2025.100446","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Background&lt;/h3&gt;&lt;div&gt;Digital pathology requires additional resources such as specialized whole slide imaging systems, staffing, space, and information technology infrastructure. Optimization of slide scanner throughput and quality are critical to achieve proper digital scanning operations. However, vendor supplied scanner throughput and scan speeds are often cited for a theoretical 15 × 15 mm tissue area and do not capture the real-world complexities of pathology slides or clinical workflows that contribute to the total time to scan a glass slide (e.g., scanner operator time). This study compares real-world scanner throughput using clinically generated glass slides, evaluating image quality errors, and total true scan time for seven different vendors' commercially available high-throughput scanners.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Design&lt;/h3&gt;&lt;div&gt;Glass slides generated in a tertiary care CLIA-certified lab were retrieved from the departmental slide library including biopsies, surgical resections, and departmental consultation material from all surgical pathology subspecialties. Glass slide stain types include hematoxylin and eosin, immunohistochemical stains, or special stains per routine lab protocols. Slides were sequentially scanned by digital scan technicians on 16 different whole slide scanners from 7 different hardware vendor manufacturers. Two senior digital scan technicians reviewed each digital image that was generated from this study. One pathologist reviewed the set of slides for missing tissue determination. Scan times including scanner scan time, and time dedicated for pre- and post-scan work were recorded and summarized for the slide set for each scanner. Whole slide scanner models used in this study included: Leica Aperio AT2 and GT450 (Leica Biosystems, Buffalo Grove, Illinois); 3DHistech Pannoramic 1000, Philips UFS (Philips, Amsterdam, the Netherlands); Hamamatsu NanoZoomer S360 (Hamamatsu, Japan), Hologic Genius (Marlborough, MA), Huron TissueScope iQ (St. Jacobs Ontario, Canada) and 2-head Pramana Spectral HT scanning system (Pramana, Inc., Cambridge MA). Scanning was performed at ×40 equivalent magnification (∼0.25 μm per pixel) on each device, except for the Aperio AT2 and Huron TissueScope iQ which was ×20 equivalent magnification (0.5 μm per pixel). All scanner data were anonymized to guarantee unbiased interpretation of the results.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results&lt;/h3&gt;&lt;div&gt;347 glass slides representing real-world daily cases were assembled as a standardized slide set that was sequentially scanned on each device in this study. Variation in scan times for both the scanner model and labor time required to operate the scanner device were recorded. Actual instrument run time (e.g., scanner time) ranged between 7:30 and 43:02 (hours:minutes), the dedicated technician scanner operation time ranged from 1:30 to 9:24 h, and the total run time for each set, including the technician's time ranged from 13:30 to 47:02 h. Manual quality contro","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"18 ","pages":"Article 100446"},"PeriodicalIF":0.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144925496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Navigating real-world challenges: A case study on federated learning in computational pathology.","authors":"Lydia A Schoenpflug, Ruben Bagan Benavides, Marta Nowak, Fahime Sheikhzadeh, Arash Moayyedi, Kamil Wasag, Jacob Reimers, Michael Zhou, Raghavan Venugopal, Bettina Sobottka, Yasmin Koeller, Michael Rivers, Holger Moch, Yao Nie, Viktor H Koelzer","doi":"10.1016/j.jpi.2025.100464","DOIUrl":"10.1016/j.jpi.2025.100464","url":null,"abstract":"<p><p>Federated learning (FL) allows institutions to collaboratively train deep learning models while maintaining data privacy, a critical aspect in fields like computational pathology (CPATH). However, existing studies focus on performance improvement in simulated environments and overlook practical aspects of FL. In this study, we address this need by transparently sharing the challenges encountered in the real-world application of FL for a clinical CPATH use case. We set up a FL framework consisting of three clients and a central server to jointly train deep learning models for digital immune phenotyping in metastatic melanoma, utilizing the NVIDIA Federated Learning Application Runtime Environment (NVIDIA FLARE) across four separate networks from institutes in four countries. Our findings reveal several key challenges: First, the FL model performs the best across all clients' test sets but does not outperform all local models on their own client test set. Second, long experiment duration due to system and data heterogeneity limited experiment frequency, alleviated by optimizing local client epochs. Third, infrastructure design was hindered by hospital and corporate network restrictions, necessitating an open port for the server, which we resolved by deploying the server on an Amazon Web Services infrastructure within a semi-public network. Lastly, effective experiment management required IT expertise and strong familiarity with NVIDIA FLARE to enable orchestration, code management, parameter configuration, and logging. Our findings provide a practical perspective on implementing FL for CPATH, advocating for greater transparency in future research and the development of best practices and guidelines for implementing FL in real-world healthcare settings.</p>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"18 ","pages":"100464"},"PeriodicalIF":0.0,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12357140/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144875696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Erratum to “Automatic classification of cancer pathology reports: A systematic review” [Journal of Pathology Informatics Volume 13, 2022, 100003]","authors":"Thiago Santos ,&nbsp;Amara Tariq ,&nbsp;Judy Wawira Gichoya ,&nbsp;Hari Trivedi ,&nbsp;Imon Banerjee","doi":"10.1016/j.jpi.2025.100453","DOIUrl":"10.1016/j.jpi.2025.100453","url":null,"abstract":"","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"18 ","pages":"Article 100453"},"PeriodicalIF":0.0,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144633448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Iris File Extension","authors":"Ryan Erik Landvater,&nbsp;Michael David Olp,&nbsp;Mustafa Yousif,&nbsp;Ulysses Balis","doi":"10.1016/j.jpi.2025.100461","DOIUrl":"10.1016/j.jpi.2025.100461","url":null,"abstract":"<div><div>A modern digital pathology vendor-agnostic binary slide format specifically targeting the unmet need of efficient real-time transfer and display has not yet been established. The growing adoption of digital pathology only intensifies the need for an intermediary digital slide format that emphasizes performance for use between slide servers and image management software. The DICOM standard is a well-established format widely used for the long-term storage of both images and associated critical metadata. However, it was inherently designed for radiology rather than digital pathology, a discipline that imposes a unique set of performance requirements due to high-speed multi-pyramidal rendering within whole slide viewer applications. Here, we introduce the Iris file extension, a binary container specification explicitly designed for performance-oriented whole slide image (WSI) viewer systems. The Iris file extension specification is explicit and straightforward, adding modern compression support, a dynamic structure with fully optional metadata features, computationally trivial deep file validation, corruption recovery capabilities, and slide annotations. In addition to the file specification document, we provide source code to allow for (de)serialization and validation of a binary stream against the standard. We also provide corresponding binary builds with C++, Python, and JavaScript language support. Finally, we provide full encoder and decoder implementation source code, as well as binary builds (part of the separate Iris Codec Community module), with language bindings for C++ and Python, allowing for easy integration with existing WSI solutions. We provide the Iris File Extension specification openly to the community in the form of a Creative Commons Attribution-No Derivative 4.0 International license.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"18 ","pages":"Article 100461"},"PeriodicalIF":0.0,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144696900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to “Computational methods for metastasis detection in lymph nodes and characterization of the metastasis-free lymph node microarchitecture: A systematic-narrative hybrid review”. Journal of Pathology Informatics 15(2024) 100367","authors":"Elzbieta Budginaite ,&nbsp;Derek R. Magee ,&nbsp;Maximilian Kloft ,&nbsp;Henry C. Woodruff ,&nbsp;Heike I. Grabsch","doi":"10.1016/j.jpi.2025.100457","DOIUrl":"10.1016/j.jpi.2025.100457","url":null,"abstract":"","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"18 ","pages":"Article 100457"},"PeriodicalIF":0.0,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144570460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PathVLM-Eval: Evaluation of open vision language models in histopathology","authors":"Nauman Ullah Gilal ,&nbsp;Rachida Zegour ,&nbsp;Khaled Al-Thelaya ,&nbsp;Erdener Özer ,&nbsp;Marco Agus ,&nbsp;Jens Schneider ,&nbsp;Sabri Boughorbel","doi":"10.1016/j.jpi.2025.100455","DOIUrl":"10.1016/j.jpi.2025.100455","url":null,"abstract":"<div><div>The emerging trend of vision language models (VLMs) has introduced a new paradigm in artificial intelligence (AI). However, their evaluation has predominantly focused on general-purpose datasets, providing a limited understanding of their effectiveness in specialized domains. Medical imaging, particularly digital pathology, could significantly benefit from VLMs for histological interpretation and diagnosis, enabling pathologists to use a complementary tool for faster morecomprehensive reporting and efficient healthcare service. In this work, we are interested in benchmarking VLMs on histopathology image understanding. We present an extensive evaluation of recent VLMs on the PathMMU dataset, a domain-specific benchmark that includes subsets such as PubMed, SocialPath, and EduContent. These datasets feature diverse formats, notably multiple-choice questions (MCQs), designed to aid pathologists in diagnostic reasoning and support professional development initiatives in histopathology. Utilizing VLMEvalKit, a widely used open-source evaluation framework—we bring publicly available pathology datasets under a single evaluation umbrella, ensuring unbiased and contamination-free assessments of model performance. Our study conducts extensive zero-shot evaluations of more than 60 state-of-the-art VLMs, including LLaVA, Qwen-VL, Qwen2-VL, InternVL, Phi3, Llama3, MOLMO, and XComposer series, significantly expanding the range of evaluated models compared to prior literature. Among the tested models, Qwen2-VL-72B-Instruct achieved superior performance with an average score of 63.97% outperforming other models across all PathMMU subsets. We conclude that this extensive evaluation will serve as a valuable resource, fostering the development of next-generation VLMs for analyzing digital pathology images. Additionally, we have released the complete evaluation results on our leaderboard PathVLM-Eval: <span><span>https://huggingface.co/spaces/gilalnauman/PathVLMs</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"18 ","pages":"Article 100455"},"PeriodicalIF":0.0,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144596403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}