Journal of Pathology Informatics最新文献

{"title":"Digital pathology enabling lean management of HER2/neu testing in breast Cancer","authors":"Aishwarya Sharma ,&nbsp;Prarthna Shah ,&nbsp;Manali Ranade ,&nbsp;Trupti Pai ,&nbsp;Ayushi Sahay ,&nbsp;Asawari Patil ,&nbsp;Tanuja Shet ,&nbsp;Heena Gupta ,&nbsp;Devika Chauhan ,&nbsp;Puneet Somal ,&nbsp;Sankalp Sancheti ,&nbsp;Sangeeta Desai","doi":"10.1016/j.jpi.2025.100515","DOIUrl":"10.1016/j.jpi.2025.100515","url":null,"abstract":"<div><h3>Introduction</h3><div>Invasive breast carcinomas with an equivocal result of HER2/neu on immunohistochemistry (IHC) are reflex tested by fluorescent in situ hybridisation (FISH). Molecular testing is often not available in rural laboratories, from where it is routinely outsourced to central laboratories. Histopathology review (HPR) and IHC analysis of the tissue sample is thus repeated at the central laboratory before molecular testing which increases the turnaround time (TAT) and cost incurred by both the patient and the hospital. We aimed to assess the reduction in TAT and the cost effectiveness after introducing Digital Pathology (DP).</div></div><div><h3>Methods</h3><div>The tumours with equivocal HER2/neu results were outsourced for FISH from HBCH, Sangrur (rural laboratory) to Molecular Pathology Laboratory, TMH, Mumbai (central laboratory). The Haematoxylin-Eosin (HE) and IHC slides of 47 cases were virtually shared after scanning by Philips SG 60 digital slide scanner. Paraffin blocks of these cases were sent for FISH testing only. TAT of these prospectively shared cases were compared with a retrospective cohort in which virtual slides were not available. The cost benefits were also assessed.</div></div><div><h3>Results</h3><div>With the availability of DP, we were able to obviate repeat IHC testing. We were able to achieve a 43.9 % reduction in the TAT (15.65 days to 8.775 days). We also achieved a 30 % reduction in cost.</div></div><div><h3>Conclusion</h3><div>This is a prototype study highlighting the utility of DP in the lean management of HER2/neu testing. The integration of DP in the referral process reduces the TAT and expenditure optimizing resource utilisation.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"19 ","pages":"Article 100515"},"PeriodicalIF":0.0,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158261","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":"Quantification of HER2-low and ultra-low expression in breast cancer specimens by quantitative IHC and artificial intelligence","authors":"Frederik Aidt ,&nbsp;Elad Arbel ,&nbsp;Itay Remer ,&nbsp;Oded Ben-David ,&nbsp;Amir Ben-Dor ,&nbsp;Daniela Rabkin ,&nbsp;Kirsten Hoff ,&nbsp;Karin Salomon ,&nbsp;Sarit Aviel-Ronen ,&nbsp;Gitte Nielsen ,&nbsp;Jens Mollerup ,&nbsp;Lars Jacobsen ,&nbsp;Anya Tsalenko","doi":"10.1016/j.jpi.2025.100513","DOIUrl":"10.1016/j.jpi.2025.100513","url":null,"abstract":"<div><div>Recent results of clinical trials in antibody drug conjugate (ADC) therapies have significantly broadened treatment options for the HER2 low and ultra-low breast cancer patients. However, sensitive, accurate and quantitative evaluation of HER2 expression based on current immunohistochemistry (IHC) assays remains challenging, especially in low and ultra-low HER2 expression ranges.</div><div>We developed a novel methodology for quantifying HER2 protein expression, targeting breast cancer cases in the HER2 IHC 0 and 1+ categories. We measured HER2 expression using quantitative IHC (qIHC) that enables precise and tunable HER2 detection across different expression levels as demonstrated in formalin-fixed paraffin-embedded cell lines. Additionally, we developed an AI-based interpretation of HercepTest™ mAb pharmDx (Dako Omnis) (HercepTest™ mAb) using qIHC measurements as the ground truth. Both methodologies allowed spatial resolution and visualization of low and ultra-low levels of HER2 expression across entire tissue sections to demonstrate and enable quantification of heterogeneity of HER2 expression.</div><div>Serial sections of 82 formalin-fixed paraffin-embedded tissue blocks of invasive breast carcinoma with HER2 IHC scores 0 or 1+ were stained with H&amp;E, HercepTest™ (mAb), qIHC and p63, then scanned and digitally aligned. Tumor areas were manually selected and reviewed by expert pathologists. HER2 expression was quantitatively evaluated based on the qIHC assay in each 128x128μm² area within tumor regions. We observed statistically significant differences in HER2 expression between IHC 0, 0 &lt; IHC &lt; 1+, and IHC 1+ groups, and a high degree of spatial heterogeneity of the HER2 expression levels within the same tissue, up to five-fold in some cases. We demonstrated high slide-level tumor region agreement of estimates of HER2 expression between the AI-based interpretation of HercepTest™ mAb and the qIHC ground truth with a Pearson correlation of 0.94, and R² of 0.87.</div><div>The developed methodologies can be used to stratify HER2 low-expression patient groups, potentially improving the interpretation of IHC assays and maximizing therapeutic benefits. This method can be implemented in histology labs without requiring a specialized workflow.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"19 ","pages":"Article 100513"},"PeriodicalIF":0.0,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145118650","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 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":"Neighborhood clustering analysis to define epithelial–stromal interface for tumor infiltrating lymphocyte evaluation","authors":"Tony Yeung ,&nbsp;Yi Zhang ,&nbsp;Qianghua Zhou ,&nbsp;Richard Burack","doi":"10.1016/j.jpi.2025.100465","DOIUrl":"10.1016/j.jpi.2025.100465","url":null,"abstract":"<div><div>Evaluation of tumor infiltrating lymphocytes as recommended by current guidelines is largely based on stromal regions within the tumor. In the context of epithelial malignancies, the epithelial region and the epithelial–stromal interface are not assessed, because of technical difficulties in manually discerning lymphocytes when admixed with epithelial tumor cells. The inability to quantify immune cells in epithelial-associated areas may negatively impact evaluation of patient response to immune checkpoint therapies. Innovative spatial analysis techniques have emerged that can directly address challenges associated with quantification of lymphocytes in specialized regions like the interface. In this study, we apply supervised neighborhood clustering analysis (via an open-source application CytoMAP) to assess the spatial distribution of CD8+ T cells, CD8+ TIM3+ (T cell immunoglobulin and mucin-domain containing-3) exhausted T cells, and TIM3+ CD8- macrophages on a gynecological tumor microarray. Neighborhood clustering analysis is adept at objectively mapping the epithelial–stromal interface alongside the epithelial and stromal region of each tumor under a three-compartment model. When tumors are partitioned by the conventional two-compartment model (epithelial and stromal region only), the highest density of total CD8+ T cells is found in the stromal region in a slight majority of tumors. In contrast, the interface region surpasses both the epithelial and stromal region in holding the highest density of CD8+ T cells when this unique region is incorporated into the three-compartment model. Further subset analysis shows higher proportion of CD8+ TIM3+ exhausted T cells within the interface and epithelial region, as compared to CD8+ TIM3- T cells which span from the stroma to the interface. These results highlight the utility of implementing quantitative spatial technique and immune subset analysis in the assessment of tumor infiltrating lymphocytes, and underscore the potential significance of the under-reported tumor epithelial–stromal interface.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"19 ","pages":"Article 100465"},"PeriodicalIF":0.0,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144912219","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":"&lt;div&gt;&lt;div&gt;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.&lt;/div&gt;&lt;div&gt;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.&lt;/div&gt;&lt;div&gt;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.&lt;/div&gt;&lt;div&gt;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.&lt;/div&gt;&lt;div&gt;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.&lt;/div&gt;&lt;div&gt;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":"Automatic labels are as effective as manual labels in digital pathology images classification with deep learning","authors":"Niccolo Marini ,&nbsp;Stefano Marchesin ,&nbsp;Lluis Borras Ferris ,&nbsp;Simon Püttmann ,&nbsp;Marek Wodzinski ,&nbsp;Riccardo Fratti ,&nbsp;Damian Podareanu ,&nbsp;Alessandro Caputo ,&nbsp;Svetla Boytcheva ,&nbsp;Simona Vatrano ,&nbsp;Filippo Fraggetta ,&nbsp;Iris Nagtegaal ,&nbsp;Gianmaria Silvello ,&nbsp;Manfredo Atzori ,&nbsp;Henning Müller","doi":"10.1016/j.jpi.2025.100462","DOIUrl":"10.1016/j.jpi.2025.100462","url":null,"abstract":"<div><div>The increasing availability of biomedical data is helping to design more robust deep learning (DL) algorithms to analyze biomedical samples. Currently, one of the main limitations to training DL algorithms to perform a specific task is the need for medical experts to manually label the data. Automatic methods to label data exist; however, automatic labels can be noisy, and it is not completely clear in which situations they can be used to train DL models.</div><div>This paper aims to investigate under which circumstances automatic labels can be used to train a DL model for the classification of whole slide images. The analysis involves multiple architectures, such as convolutional neural networks and vision transformer, and 10,604 WSIs as training data, collected from three use cases: celiac disease, lung cancer, and colon cancer, which include respectively binary, multiclass, and multilabel data. The results identify 10% as the percentage of noisy labels before a performance drop-off, so to train effective models for the classification of WSIs, reaching, respectively, F1-scores of 0.906, 0.757, and 0.833. Therefore, an algorithm generating automatic labels needs to stay within this range to be adopted, as shown by the application of Semantic Knowledge Extractor Tool as a tool to automatically extract concepts and use them as labels. Automatic labels are as effective as manual labels in this case, achieving solid performance comparable to that obtained by training models with manual labels.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"18 ","pages":"Article 100462"},"PeriodicalIF":0.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144841531","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}