Journal of Pathology Informatics最新文献

{"title":"Digital slide scanning at scale: Comparison of whole slide imaging devices in a clinical setting","authors":"Orly Ardon , Allyne Manzo , Jamaal Spencer , Victor E. Reuter , Meera Hameed , Matthew G. Hanna","doi":"10.1016/j.jpi.2025.100446","DOIUrl":"10.1016/j.jpi.2025.100446","url":null,"abstract":"<div><h3>Background</h3><div>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.</div></div><div><h3>Design</h3><div>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.</div></div><div><h3>Results</h3><div>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":"Generating 2.5D pathology for enhanced viewing and AI diagnosis.","authors":"Ekaterina Redekop, Mara Pleasure, Zichen Wang, Anthony Sisk, Yang Zong, Kimberly Flores, William Speier, Corey W Arnold","doi":"10.1016/j.jpi.2025.100463","DOIUrl":"10.1016/j.jpi.2025.100463","url":null,"abstract":"<p><p>Histological analysis of biopsy samples by pathologists can require the evaluation of complex three-dimensional (3D) tissue structures. This process involves studying the same tissue region across slides, which requires laborious zooming and panning for localization. Additionally, standard deep learning frameworks typically focus on cross-sections cut from biopsy specimens, limiting their ability to capture 3D tissue spatial information. We present a novel framework that constructs 2.5D biopsy cores via the extraction and co-alignment of serial tissue sections using a novel morphology-preserving alignment framework. These 2.5D cores can then be used for enhanced viewing by pathologists and as input to video transformer models that can capture depth-wide spatial dependencies. We used our framework to construct 2.5D cores for 10,210 prostate biopsies, 156 breast biopsies, and 1869 renal biopsies. To evaluate the utility of the cores for downstream tasks, we performed additional studies in prostate cancer by: (1) training a deep learning-based cancer grading model and (2) conducting a reader study with pathologists.</p>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"18 ","pages":"100463"},"PeriodicalIF":0.0,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12355132/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144875695","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":"Enhancing diagnostic innovation by leveraging the co-creation approach","authors":"Jochen K. Lennerz ,&nbsp;Alexandra Farfsing ,&nbsp;Tim-Rasmus Kiehl ,&nbsp;Sven Perner ,&nbsp;Joost van Duuren ,&nbsp;Marleen Christ ,&nbsp;Jan W. Farfsing","doi":"10.1016/j.jpi.2025.100460","DOIUrl":"10.1016/j.jpi.2025.100460","url":null,"abstract":"<div><div>Digital innovation in precision diagnostics requires addressing complex challenges, such as implementation, adoption, equity, and sustainability. This study introduces a co-creation framework that leverages the pre-competitive space to drive collaborative innovation in personalized diagnostics. Over 5 years, a multidisciplinary community of stakeholders from computational pathology, oncology, genetics, digital medicine, and industry engaged in design-thinking workshops to identify unmet medical needs and co-develop solutions. These efforts led to 15 pilot projects, with 7 successfully implemented, including an automated lab system enhancing workflow efficiency. The co-creation approach fostered strategic alignment, community building, and integration of diverse perspectives, resulting in tangible outputs (datasets, publications, and resources) and intangible benefits (networking, market insight). This framework demonstrates how collaborative ecosystems accelerate diagnostic innovations and offer a scalable model for advancing personalized healthcare. Co-creation addresses interdisciplinary silos, promotes patient-centered solutions, and adapts to evolving regulatory landscapes, making it a catalyst for impactful healthcare transformation.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"18 ","pages":"Article 100460"},"PeriodicalIF":0.0,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144596402","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":"Benford's Law in histology","authors":"Jasmine Caballero ,&nbsp;Daniel Gonzalez ,&nbsp;Dustin La Fleur ,&nbsp;Sai Karan Vamsi Guda ,&nbsp;Cynthia Duran ,&nbsp;Kaitlin Sime","doi":"10.1016/j.jpi.2025.100458","DOIUrl":"10.1016/j.jpi.2025.100458","url":null,"abstract":"<div><div>Digital pathology is an emerging field that is gaining popularity due to its numerous advantages over traditional pathology methods. Digital pathology allows for the remote examination of tissue samples, increasing efficiency and reducing costs. The field of digital pathology is experiencing a boom of data, creating space for new tools to be implemented that have not been used in pathology prior. Benford's Law is a statistical tool commonly used to analyze large datasets by other top organizations. Benford's Law is a law of frequency of first and second digits and whether they would appear normally in nature. With research in multiple fields of medicine moving into a digital era, tools that had once been used elsewhere to analyze digital images could translate well into pathology. Quantitative histomorphometry is a tool in digital pathology that analyzes digital images and collects morphological and histological data of whole-slide images, with more techniques being developed in digital pathology, such as deep learning, creating a more accurate 3D analysis of the cell. Easy and quick tools are needed to analyze the large datasets that are being extracted quickly. We believe that Benford's Law is a statistical outlook that can be easily implemented for similar use in whole-slide image analysis. When a system is disrupted by disease, it will distort the normal, natural growth of cells throughout the organ.</div><div>Open access tools such as QuPath have created a way to obtain categories of data to analyze, such as the size of a cell or the amount of staining it absorbs. Slides of normal liver cells were collected and compared to slides of a liver with cancer. The liver was selected because of its well-demarcated cytoplastic borders and nucleus. A total of 25 liver tissue slides were collected. The graph of naturalness is compared to analyze ways to detect variability between normal liver cells and cancer liver cells. 206,700 cells from 15 slides of 7 cancer patients' liver tissue samples (15 slides total) and 116,339 cells from 5 slides of normal liver tissue were collected, totaling 323,039 cells from 20 slides. Of the seven cancer patients, five were previously diagnosed with cholangiocarcinoma, and two were diagnosed with adenomas/adenocarcinoma.</div><div>The study found that of the 13 data categories provided by QuPath, such as cell size, nucleus size, and color absorbance, two met the Chi-square goodness of fit (χ²) criteria compared to Benford's Law of Naturalness, providing the most significant feedback. Due to QuPath's inability to distinguish all cytoplastic borders accurately, categories that depict size measurements were not used. Of the two categories that did correlate, such as those that used stain absorbance, 62.5% of slides that exceeded the critical value contained cells of someone diagnosed with cancer. In contrast, all normal slides showed a very low variance. All slides from a cancer patient showed a test ","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"18 ","pages":"Article 100458"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144623359","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":"Data migration, validation and implementation of a new laboratory information system (LIS) in an academic pathology department, using Ellkay data archive, and Epic Beaker anatomic and clinical pathology modules","authors":"Jeffrey Benitez ,&nbsp;Adam An ,&nbsp;Alec B. Santos ,&nbsp;Amelia Flaus ,&nbsp;Matt Wawrzyszko ,&nbsp;Beverley Young ,&nbsp;Eleanor Latta ,&nbsp;Catherine J. Streutker ,&nbsp;Ju-Yoon Yoon","doi":"10.1016/j.jpi.2025.100459","DOIUrl":"10.1016/j.jpi.2025.100459","url":null,"abstract":"<div><div>Implementation of a new laboratory information system (LIS) poses a significant challenge, amplified when synchronous with launch of a new electronic medical record (EMR) system. Our institution made an executive decision to switch to Epic EMR and Epic Beaker LIS from Cerner Soarian/Altera Sunrise EMR and Cemer CoPath Plus LIS in anatomic pathology and molecular genetic pathology, with a simultaneous go-live date. This synchronous migration required a complete overhaul in our department of laboratory medicine, impacting all standard operating procedures (SOPs). In our efforts to minimize potential risks, we pursued a phased approach to comprehensive validation, starting with iterative rounds of optimization, ending with the final round of validation assessing 45 consecutive pathology cases, simulating the entire workflow in a dry-lab setting, from ordering to reporting, including addenda, with additional cases tested for specific workflow steps. In addition, we pursued validation of result component migration, in form of legacy pathology results to the Epic EMR, and the Ellkay archiving system. We found that our SOP adaptations for Epic Beaker reproduced &gt;99% of the workflows previously established using CoPath Plus. The validation performed was limited to Epic Beaker LIS functionality, and, post-go-live, deficiencies were uncovered largely upstream of the LIS. Based on our experience, we formed a framework for systematic validation of LIS workflows, and share our comprehensive handbook, detailing all workflows built before go-live.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"18 ","pages":"Article 100459"},"PeriodicalIF":0.0,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144722570","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":"Development of a national pathology training system using digital pathology and SNOMED-CT","authors":"Clare McGenity ,&nbsp;Alyn Cratchley ,&nbsp;Jocelyn Aldridge ,&nbsp;Craig Sayers ,&nbsp;W. Scott Campbell ,&nbsp;Rajesh C. Dash ,&nbsp;Adrienne M. Flanagan ,&nbsp;Neil Sebire ,&nbsp;Alexander Wright ,&nbsp;Waseem Akhtar ,&nbsp;Darren Treanor","doi":"10.1016/j.jpi.2025.100456","DOIUrl":"10.1016/j.jpi.2025.100456","url":null,"abstract":"<div><h3>Introduction</h3><div>Digital pathology is an important resource in modern pathology education, with many examples of large databases of educational cases now available online. However, there remains a lack of standardization in retrieval and categorization of cases for training within and between institutions.</div></div><div><h3>Methods</h3><div>Over 1600 teaching terms applicable to histopathology were developed and mapped to corresponding SNOMED CT terms to create a large directory of teaching cases. This was then integrated as a pre-defined list into a clinical PACS system for a national digital pathology project covering multiple hospitals and pathology training programs.</div></div><div><h3>Results</h3><div>This resource allows easy allocation of teaching term labels to cases with educational value. A substantial catalog of educational cases has been generated already, with ongoing efforts to expand this with cases from routine clinical practice. The catalog is fully searchable by term for use in training and examinations.</div></div><div><h3>Conclusions</h3><div>A large directory of digital pathology teaching cases was developed with associated corresponding SNOMED CT terms. The directory of terms will be shared with other healthcare providers to expand its use and utility.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"18 ","pages":"Article 100456"},"PeriodicalIF":0.0,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144570459","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}