Pub Date : 2025-09-16DOI: 10.1016/j.jpi.2025.100512
Behnaz Elhaminia , Abdullah Alsalemi , Esha Nasir , Mostafa Jahanifar , Ruqayya Awan , Lawrence S. Young , Nasir M. Rajpoot , Fayyaz Minhas , Shan E. Ahmed Raza
Whole slide image (WSI) registration is an essential task for analyzing the tumor microenvironment (TME) in histopathology. It involves the alignment of spatial information between WSIs of the same section or serial sections of a tissue sample. The tissue sections are usually stained with single or multiple biomarkers before imaging, and the goal is to identify neighboring nuclei along the Z-axis for creating a 3D image or identifying subclasses of cells in the TME. This task is considerably more challenging compared to radiology image registration, such as magnetic resonance imaging or computed tomography, due to various factors. These include gigapixel size of images, variations in appearance between differently stained tissues, changes in structure and morphology between non-consecutive sections, and the presence of artifacts, tears, and deformations. Currently, there is a noticeable gap in the literature regarding a review of the current approaches and their limitations, as well as the challenges and opportunities they present. We aim to provide a comprehensive understanding of the available approaches and their application for various purposes. Furthermore, we investigate current deep learning methods used for WSI registration, emphasizing their diverse methodologies. We examine the available datasets and explore tools and software employed in the field. Finally, we identify open challenges and potential future trends in this area of research.
{"title":"From traditional to deep learning approaches in whole slide image registration: A methodological review","authors":"Behnaz Elhaminia , Abdullah Alsalemi , Esha Nasir , Mostafa Jahanifar , Ruqayya Awan , Lawrence S. Young , Nasir M. Rajpoot , Fayyaz Minhas , Shan E. Ahmed Raza","doi":"10.1016/j.jpi.2025.100512","DOIUrl":"10.1016/j.jpi.2025.100512","url":null,"abstract":"<div><div>Whole slide image (WSI) registration is an essential task for analyzing the tumor microenvironment (TME) in histopathology. It involves the alignment of spatial information between WSIs of the same section or serial sections of a tissue sample. The tissue sections are usually stained with single or multiple biomarkers before imaging, and the goal is to identify neighboring nuclei along the Z-axis for creating a 3D image or identifying subclasses of cells in the TME. This task is considerably more challenging compared to radiology image registration, such as magnetic resonance imaging or computed tomography, due to various factors. These include gigapixel size of images, variations in appearance between differently stained tissues, changes in structure and morphology between non-consecutive sections, and the presence of artifacts, tears, and deformations. Currently, there is a noticeable gap in the literature regarding a review of the current approaches and their limitations, as well as the challenges and opportunities they present. We aim to provide a comprehensive understanding of the available approaches and their application for various purposes. Furthermore, we investigate current deep learning methods used for WSI registration, emphasizing their diverse methodologies. We examine the available datasets and explore tools and software employed in the field. Finally, we identify open challenges and potential future trends in this area of research.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"19 ","pages":"Article 100512"},"PeriodicalIF":0.0,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145267201","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 : 2025-09-08DOI: 10.1016/j.jpi.2025.100516
Liron Pantanowitz, Anil Parwani
{"title":"The path forward: Evolving standards for a smarter digital pathology ecosystem","authors":"Liron Pantanowitz, Anil Parwani","doi":"10.1016/j.jpi.2025.100516","DOIUrl":"10.1016/j.jpi.2025.100516","url":null,"abstract":"","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"19 ","pages":"Article 100516"},"PeriodicalIF":0.0,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145267200","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 : 2025-09-08DOI: 10.1016/j.jpi.2025.100514
Meghdad Sabouri Rad , Junze (Vincent) Huang , Mohammad Mehdi Hosseini , Rakesh Choudhary , Harmen Siezen , Ratilal Akabari , Tamara Jamaspishvili , Ola El-Zammar , Palak G Patel , Saverio J. Carello , Michel R. Nasr , Bardia Rodd
Deep learning frameworks have transformed the field of digital pathology by automating complex tasks and revealing intricate patterns within histopathological data. These advanced methodologies provide exceptional accuracy and scalability, facilitating the analysis of high-dimensional whole-slide images with unparalleled precision. In this article, we present a comprehensive deep learning framework highlighting recent advancements in computational pathology. We critically examine mathematical innovations and offer a comparative analysis of various models demonstrating the significant and ongoing improvements in the field.
{"title":"Deep learning for digital pathology: A critical overview of methodological framework","authors":"Meghdad Sabouri Rad , Junze (Vincent) Huang , Mohammad Mehdi Hosseini , Rakesh Choudhary , Harmen Siezen , Ratilal Akabari , Tamara Jamaspishvili , Ola El-Zammar , Palak G Patel , Saverio J. Carello , Michel R. Nasr , Bardia Rodd","doi":"10.1016/j.jpi.2025.100514","DOIUrl":"10.1016/j.jpi.2025.100514","url":null,"abstract":"<div><div>Deep learning frameworks have transformed the field of digital pathology by automating complex tasks and revealing intricate patterns within histopathological data. These advanced methodologies provide exceptional accuracy and scalability, facilitating the analysis of high-dimensional whole-slide images with unparalleled precision. In this article, we present a comprehensive deep learning framework highlighting recent advancements in computational pathology. We critically examine mathematical innovations and offer a comparative analysis of various models demonstrating the significant and ongoing improvements in the field.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"19 ","pages":"Article 100514"},"PeriodicalIF":0.0,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145362623","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}
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).
Methods
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.
Results
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.
Conclusion
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.
{"title":"Digital pathology enabling lean management of HER2/neu testing in breast Cancer","authors":"Aishwarya Sharma , Prarthna Shah , Manali Ranade , Trupti Pai , Ayushi Sahay , Asawari Patil , Tanuja Shet , Heena Gupta , Devika Chauhan , Puneet Somal , Sankalp Sancheti , 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}
Pub Date : 2025-08-26DOI: 10.1016/j.jpi.2025.100513
Frederik Aidt , Elad Arbel , Itay Remer , Oded Ben-David , Amir Ben-Dor , Daniela Rabkin , Kirsten Hoff , Karin Salomon , Sarit Aviel-Ronen , Gitte Nielsen , Jens Mollerup , Lars Jacobsen , Anya Tsalenko
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.
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.
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&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 < IHC < 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.
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.
{"title":"Quantification of HER2-low and ultra-low expression in breast cancer specimens by quantitative IHC and artificial intelligence","authors":"Frederik Aidt , Elad Arbel , Itay Remer , Oded Ben-David , Amir Ben-Dor , Daniela Rabkin , Kirsten Hoff , Karin Salomon , Sarit Aviel-Ronen , Gitte Nielsen , Jens Mollerup , Lars Jacobsen , 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&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 128x128μm<sup>2</sup> area within tumor regions. We observed statistically significant differences in HER2 expression between IHC 0, 0 < IHC < 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 R<sup>2</sup> 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}
Pub Date : 2025-08-22DOI: 10.1016/j.jpi.2025.100510
Peter Gershkovich
{"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}
Pub Date : 2025-08-20DOI: 10.1016/j.jpi.2025.100511
David A. Clunie
{"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}
Pub Date : 2025-08-06DOI: 10.1016/j.jpi.2025.100465
Tony Yeung , Yi Zhang , Qianghua Zhou , Richard Burack
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.
{"title":"Neighborhood clustering analysis to define epithelial–stromal interface for tumor infiltrating lymphocyte evaluation","authors":"Tony Yeung , Yi Zhang , Qianghua Zhou , 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}
Pub Date : 2025-08-01DOI: 10.1016/j.jpi.2025.100450
Peter Gershkovich
<div><div>Slides are data. Once digitized, they function like any enterprise asset: accessible anywhere, ready for AI, and integrated into cloud workflows. But in pathology, they enter a realm of clinical complexity—demanding systems that handle nuance, integrate diverse data streams, scale effectively, enable computational exploration, and enforce rigorous security.</div><div>Although the Digital Imaging and Communications in Medicine (DICOM) standard revolutionized radiology, it is imperative to explore its adequacy in addressing modern digital pathology's orchestration needs. Designed more than 30 years ago, DICOM reflects assumptions and architectural choices that predate modular software, cloud computing, and AI-driven workflows.</div><div>This article shows that by embedding metadata, annotations, and communication protocols into a unified container, DICOM limits interoperability and exposes architectural vulnerabilities. The article begins by examining these innate design risks, then challenges DICOM's interoperability claims, and ultimately presents a modular, standards-aligned alternative.</div><div>The article argues that separating image data from orchestration logic improves scalability, security, and performance. Standards such as HL7 FHIR (Health Level Seven Fast Healthcare Interoperability Resources) and modern databases manage clinical metadata; formats like Scalable Vector Graphics handle annotations; and fast, cloud-native file transfer protocols, and microservices support tile-level image access. This separation of concerns allows each component to evolve independently, optimizes performance across the system, and better adapts to emerging AI-driven workflows—capabilities that are inherently constrained in monolithic architectures where these elements are tightly coupled.</div><div>It further shows that security requirements should not be embedded within the DICOM standard itself. Instead, security must be addressed through a layered, format-independent framework that spans systems, networks, applications, and data governance. Security is not a discrete feature but an overarching discipline—defined by its own evolving set of standards and best practices. Overlays such as those outlined in the National Institute of Standards and Technology SP 800-53 support modern Transport Layer Security, single sign-on, cryptographic hashing, and other controls that protect data streams without imposing architectural constraints or restricting technological choices.</div><div>Pathology stands at a rare inflection point. Unlike radiology, where DICOM is deeply entrenched, pathology workflows still operate in polyglot environments—leveraging proprietary formats, hybrid standards, and emerging cloud-native tools. This diversity, often seen as a limitation, offers a clean slate: an opportunity to architect a modern, modular infrastructure free from legacy constraints. While a full departure from DICOM is unnecessary, pathology is uniquely position
{"title":"Wearing a fur coat in the summertime: Should digital pathology redefine medical imaging?","authors":"Peter Gershkovich","doi":"10.1016/j.jpi.2025.100450","DOIUrl":"10.1016/j.jpi.2025.100450","url":null,"abstract":"<div><div>Slides are data. Once digitized, they function like any enterprise asset: accessible anywhere, ready for AI, and integrated into cloud workflows. But in pathology, they enter a realm of clinical complexity—demanding systems that handle nuance, integrate diverse data streams, scale effectively, enable computational exploration, and enforce rigorous security.</div><div>Although the Digital Imaging and Communications in Medicine (DICOM) standard revolutionized radiology, it is imperative to explore its adequacy in addressing modern digital pathology's orchestration needs. Designed more than 30 years ago, DICOM reflects assumptions and architectural choices that predate modular software, cloud computing, and AI-driven workflows.</div><div>This article shows that by embedding metadata, annotations, and communication protocols into a unified container, DICOM limits interoperability and exposes architectural vulnerabilities. The article begins by examining these innate design risks, then challenges DICOM's interoperability claims, and ultimately presents a modular, standards-aligned alternative.</div><div>The article argues that separating image data from orchestration logic improves scalability, security, and performance. Standards such as HL7 FHIR (Health Level Seven Fast Healthcare Interoperability Resources) and modern databases manage clinical metadata; formats like Scalable Vector Graphics handle annotations; and fast, cloud-native file transfer protocols, and microservices support tile-level image access. This separation of concerns allows each component to evolve independently, optimizes performance across the system, and better adapts to emerging AI-driven workflows—capabilities that are inherently constrained in monolithic architectures where these elements are tightly coupled.</div><div>It further shows that security requirements should not be embedded within the DICOM standard itself. Instead, security must be addressed through a layered, format-independent framework that spans systems, networks, applications, and data governance. Security is not a discrete feature but an overarching discipline—defined by its own evolving set of standards and best practices. Overlays such as those outlined in the National Institute of Standards and Technology SP 800-53 support modern Transport Layer Security, single sign-on, cryptographic hashing, and other controls that protect data streams without imposing architectural constraints or restricting technological choices.</div><div>Pathology stands at a rare inflection point. Unlike radiology, where DICOM is deeply entrenched, pathology workflows still operate in polyglot environments—leveraging proprietary formats, hybrid standards, and emerging cloud-native tools. This diversity, often seen as a limitation, offers a clean slate: an opportunity to architect a modern, modular infrastructure free from legacy constraints. While a full departure from DICOM is unnecessary, pathology is uniquely position","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"18 ","pages":"Article 100450"},"PeriodicalIF":0.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144925135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-01DOI: 10.1016/j.jpi.2025.100444
Ilgar I. Guseinov, Arnab Bhowmik, Somaia AbuBaker, Anna E. Schmaus-Klughammer, Thomas Spittler
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.
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.
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.
{"title":"Comparative analysis of a 5G campus network and existing networks for real-time consultation in remote pathology","authors":"Ilgar I. Guseinov, Arnab Bhowmik, Somaia AbuBaker, Anna E. Schmaus-Klughammer, 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}
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