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.
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.
{"title":"Automatic labels are as effective as manual labels in digital pathology images classification with deep learning","authors":"Niccolo Marini , Stefano Marchesin , Lluis Borras Ferris , Simon Püttmann , Marek Wodzinski , Riccardo Fratti , Damian Podareanu , Alessandro Caputo , Svetla Boytcheva , Simona Vatrano , Filippo Fraggetta , Iris Nagtegaal , Gianmaria Silvello , Manfredo Atzori , 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}
Pub Date : 2025-08-01DOI: 10.1016/j.jpi.2025.100446
Orly Ardon , Allyne Manzo , Jamaal Spencer , Victor E. Reuter , Meera Hameed , Matthew G. Hanna
<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
{"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}
Pub Date : 2025-07-23eCollection Date: 2025-08-01DOI: 10.1016/j.jpi.2025.100464
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
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.
联邦学习（FL）允许机构在保持数据隐私的同时协同训练深度学习模型，这是计算病理学（CPATH）等领域的一个关键方面。然而，现有的研究侧重于模拟环境中的性能改进，而忽视了FL的实际应用。在本研究中，我们通过透明地分享FL在临床CPATH用例中的实际应用中遇到的挑战来解决这一需求。我们建立了一个由三个客户端和一个中央服务器组成的FL框架，利用NVIDIA联邦学习应用运行时环境（NVIDIA FLARE）在来自四个国家的机构的四个独立网络上联合训练用于转移性黑色素瘤数字免疫表型的深度学习模型。我们的发现揭示了几个关键的挑战：首先，FL模型在所有客户的测试集中表现最好，但在他们自己的客户测试集中表现不优于所有本地模型。其次，由于系统和数据的异构性，实验时间长，限制了实验频率，通过优化本地客户端时间可以缓解这一问题。第三，基础设施设计受到医院和企业网络限制的阻碍，需要为服务器提供一个开放端口，我们通过将服务器部署在半公共网络中的Amazon Web Services基础设施上来解决这个问题。最后，有效的实验管理需要IT专业知识和对NVIDIA FLARE的熟悉程度，以实现编排、代码管理、参数配置和日志记录。我们的研究结果为在CPATH中实施FL提供了一个实用的视角，提倡在未来的研究中提高透明度，并为在现实世界的医疗环境中实施FL制定最佳实践和指南。
{"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}
Pub Date : 2025-07-18eCollection Date: 2025-08-01DOI: 10.1016/j.jpi.2025.100463
Ekaterina Redekop, Mara Pleasure, Zichen Wang, Anthony Sisk, Yang Zong, Kimberly Flores, William Speier, Corey W Arnold
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.
{"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}
Pub Date : 2025-07-09DOI: 10.1016/j.jpi.2025.100461
Ryan Erik Landvater, Michael David Olp, Mustafa Yousif, Ulysses Balis
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.
{"title":"The Iris File Extension","authors":"Ryan Erik Landvater, Michael David Olp, Mustafa Yousif, 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}
Pub Date : 2025-07-07DOI: 10.1016/j.jpi.2025.100457
Elzbieta Budginaite , Derek R. Magee , Maximilian Kloft , Henry C. Woodruff , Heike I. Grabsch
{"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 , Derek R. Magee , Maximilian Kloft , Henry C. Woodruff , 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}
Pub Date : 2025-07-03DOI: 10.1016/j.jpi.2025.100460
Jochen K. Lennerz , Alexandra Farfsing , Tim-Rasmus Kiehl , Sven Perner , Joost van Duuren , Marleen Christ , Jan W. Farfsing
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.
{"title":"Enhancing diagnostic innovation by leveraging the co-creation approach","authors":"Jochen K. Lennerz , Alexandra Farfsing , Tim-Rasmus Kiehl , Sven Perner , Joost van Duuren , Marleen Christ , 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}
Pub Date : 2025-07-01DOI: 10.1016/j.jpi.2025.100458
Jasmine Caballero , Daniel Gonzalez , Dustin La Fleur , Sai Karan Vamsi Guda , Cynthia Duran , Kaitlin Sime
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.
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.
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
数字病理学是一个新兴的领域，由于其比传统病理学方法有许多优点而越来越受欢迎。数字病理学允许对组织样本进行远程检查，提高效率并降低成本。数字病理学领域正在经历数据的繁荣，为以前未在病理学中使用的新工具的实施创造了空间。本福德定律是一种统计工具，通常被其他顶级组织用于分析大型数据集。本福德定律是关于第一位和第二位数字出现的频率以及它们是否会在自然中正常出现的定律。随着医学多个领域的研究进入数字时代，曾经在其他地方用于分析数字图像的工具可以很好地转化为病理学。定量组织形态计量学是数字病理学中的一种工具，用于分析数字图像并收集整个幻灯片图像的形态学和组织学数据，随着数字病理学中越来越多的技术被开发，例如深度学习，创建更准确的细胞3D分析。需要简单快捷的工具来分析快速提取的大型数据集。我们相信本福德定律是一种统计前景，可以很容易地在全幻灯片图像分析中实现类似的应用。当一个系统被疾病破坏时，它会扭曲整个器官中细胞的正常、自然生长。像QuPath这样的开放获取工具已经创造了一种方法来获取要分析的数据类别，比如细胞的大小或它吸收的染色量。收集正常肝细胞的切片，并与肝癌的切片进行比较。选择肝脏是因为它的细胞质边界和细胞核划分清楚。共收集肝组织切片25张。对自然度图进行比较，分析检测正常肝细胞和癌肝细胞之间差异的方法。从7例肿瘤患者的15片肝组织样本（共15片）中收集了206,700个细胞，从5片正常肝组织样本中收集了116,339个细胞，从20片中收集了323,039个细胞。在这7名癌症患者中，5名先前被诊断为胆管癌，2名被诊断为腺瘤/腺癌。研究发现，在QuPath提供的细胞大小、细胞核大小、吸光度等13个数据类别中，与Benford’s Law of Naturalness相比，有2个数据类别符合卡方拟合优度（χ2）标准，提供了最显著的反馈。由于QuPath无法准确区分所有细胞质边界，因此没有使用描述尺寸测量的类别。在两种确实相关的类别中，比如那些使用染色吸光度的，超过临界值的载玻片中有62.5%含有被诊断为癌症的人的细胞。相比之下，所有正常的幻灯片显示非常低的方差。所有来自癌症患者的玻片的检验统计量都在6分以上，而正常组织玻片的检验统计量低于1.5分，这与本福德定律密切相关。
{"title":"Benford's Law in histology","authors":"Jasmine Caballero , Daniel Gonzalez , Dustin La Fleur , Sai Karan Vamsi Guda , Cynthia Duran , 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 (χ<sup>2</sup>) 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}
Pub Date : 2025-06-25DOI: 10.1016/j.jpi.2025.100459
Jeffrey Benitez , Adam An , Alec B. Santos , Amelia Flaus , Matt Wawrzyszko , Beverley Young , Eleanor Latta , Catherine J. Streutker , Ju-Yoon Yoon
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 >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.
{"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 , Adam An , Alec B. Santos , Amelia Flaus , Matt Wawrzyszko , Beverley Young , Eleanor Latta , Catherine J. Streutker , 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 >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}
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