Pub Date : 2025-08-01Epub Date: 2025-05-09DOI: 10.1016/j.jpi.2025.100447
F.H. Reith , A. Jarosch , J.P. Albrecht , F. Ghoreschi , A. Flörcken , A. Dörr , S. Roohani , F.M. Schäfer , R. Öllinger , S. Märdian , K. Tielking , P. Bischoff , N. Frühauf , F. Brandes , D. Horst , C. Sers , D. Kainmüller
Tumoral PD-L1 expression is assessed to weigh immunotherapy options in the treatment of various types of cancer. To determine PD-L1 expression, each tumor cell needs to be assessed to calculate the percentage of PD-L1 positive tumor cells, called tumor proportion score (TPS). Pathologists cannot evaluate each cell individually due to time constraints and thus need to approximate TPS, which has been shown to result in low concordance rates.
Decision quality could be improved by an AI-based TPS prediction tool which serves as a “second opinion”. Establishing such a tool requires a certain amount of training data, which manifests a bottleneck for rare cancer types such as Angiosarcoma.
To address this challenge, we developed and open sourced a pipeline that leverages pre-trained and generalist models to achieve strong TPS prediction performance on limited data. Pathologists were asked to reassess patients for which their TPS strongly disagreed with the AI's prediction. In many of these cases, pathologists updated their TPS score, improving their assessment, thus demonstrating the technical feasibility and practical value of AI-based TPS scoring assistance for rare cancers.
{"title":"PD-L1 expression assessment in Angiosarcoma improves with artificial intelligence support","authors":"F.H. Reith , A. Jarosch , J.P. Albrecht , F. Ghoreschi , A. Flörcken , A. Dörr , S. Roohani , F.M. Schäfer , R. Öllinger , S. Märdian , K. Tielking , P. Bischoff , N. Frühauf , F. Brandes , D. Horst , C. Sers , D. Kainmüller","doi":"10.1016/j.jpi.2025.100447","DOIUrl":"10.1016/j.jpi.2025.100447","url":null,"abstract":"<div><div>Tumoral PD-L1 expression is assessed to weigh immunotherapy options in the treatment of various types of cancer. To determine PD-L1 expression, each tumor cell needs to be assessed to calculate the percentage of PD-L1 positive tumor cells, called tumor proportion score (TPS). Pathologists cannot evaluate each cell individually due to time constraints and thus need to approximate TPS, which has been shown to result in low concordance rates.</div><div>Decision quality could be improved by an AI-based TPS prediction tool which serves as a “second opinion”. Establishing such a tool requires a certain amount of training data, which manifests a bottleneck for rare cancer types such as Angiosarcoma.</div><div>To address this challenge, we developed and open sourced a pipeline that leverages pre-trained and generalist models to achieve strong TPS prediction performance on limited data. Pathologists were asked to reassess patients for which their TPS strongly disagreed with the AI's prediction. In many of these cases, pathologists updated their TPS score, improving their assessment, thus demonstrating the technical feasibility and practical value of AI-based TPS scoring assistance for rare cancers.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"18 ","pages":"Article 100447"},"PeriodicalIF":0.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144166082","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 : 2025-08-01Epub Date: 2025-05-18DOI: 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 : 2025-08-01Epub Date: 2025-05-14DOI: 10.1016/j.jpi.2025.100448
B. Sturm , P. Lock , D. Kumar , W.A.M. Blokx , J.A.W.M. van der Laak
Background
Triple negative breast cancer (TNBC) is an aggressive subcategory of breast cancer with poor prognosis and high risk of recurrence after treatment. In a subset of cases systemic chemotherapy is offered before surgery, so called neoadjuvant chemotherapy (NAC), to downstage the disease resulting in 40–50% of cases to a pathological complete response. Meanwhile, patients receiving NAC suffer from toxic side effects and in a proportion of patients a significant amount of residual tumor remains. This study aims to predict the outcome of NAC with deep learning technology based on the microscopic morphological characteristics in whole slide images of hematoxylin and eosin (H&E) slides from the pre-operative tumor biopsy before chemotherapy.
Methods
A convolutional neural network was trained on 221 H&E-stained biopsies of carcinoma of no special type from 205 patients scanned at 40×. Cases were divided in three cohorts, with a good, moderate, or bad response to NAC based on the EUSOMA scoring according to the pathology report of the subsequent tumor surgery specimen. We defined good, moderate, and bad response as residual tumor <10%, 10–50%, and >50%, respectively. Manual segmentation of the tumor area was performed comprising invasive carcinoma with a small rim of surrounding benign tissue. The model was tested on 52 new biopsies of 50 patients. Because of the relative low number of moderate and bad responder cases, and to achieve a better discrimination for potential visual biomarkers, the moderate and bad response cohorts were merged.
Results
The predictive performance of the model was calculated by means of the area under the receiver operator curve (AUC ROC). 95% Confidence intervals (CIs) were calculated for better understanding of the range of values. In the test set, the AUC ROC performance score was 0.696 with a CI of 0.532–0.861.
Conclusion
This proof-of-concept study shows that H&E pre-operative biopsies from TNBC, by means of deep learning technology, contain valuable information having predictive value for the outcome of NAC resulting in an AUC value of 0.696 outperforming a predictive AUC value of 0.63 based on structured clinical data of histological tumor grade, TILs, and ki-67 known from the literature.
{"title":"Deep learning predicts the effect of neoadjuvant chemotherapy for patients with triple negative breast cancer","authors":"B. Sturm , P. Lock , D. Kumar , W.A.M. Blokx , J.A.W.M. van der Laak","doi":"10.1016/j.jpi.2025.100448","DOIUrl":"10.1016/j.jpi.2025.100448","url":null,"abstract":"<div><h3>Background</h3><div>Triple negative breast cancer (TNBC) is an aggressive subcategory of breast cancer with poor prognosis and high risk of recurrence after treatment. In a subset of cases systemic chemotherapy is offered before surgery, so called neoadjuvant chemotherapy (NAC), to downstage the disease resulting in 40–50% of cases to a pathological complete response. Meanwhile, patients receiving NAC suffer from toxic side effects and in a proportion of patients a significant amount of residual tumor remains. This study aims to predict the outcome of NAC with deep learning technology based on the microscopic morphological characteristics in whole slide images of hematoxylin and eosin (H&E) slides from the pre-operative tumor biopsy before chemotherapy.</div></div><div><h3>Methods</h3><div>A convolutional neural network was trained on 221 H&E-stained biopsies of carcinoma of no special type from 205 patients scanned at 40×. Cases were divided in three cohorts, with a good, moderate, or bad response to NAC based on the EUSOMA scoring according to the pathology report of the subsequent tumor surgery specimen. We defined good, moderate, and bad response as residual tumor <10%, 10–50%, and >50%, respectively. Manual segmentation of the tumor area was performed comprising invasive carcinoma with a small rim of surrounding benign tissue. The model was tested on 52 new biopsies of 50 patients. Because of the relative low number of moderate and bad responder cases, and to achieve a better discrimination for potential visual biomarkers, the moderate and bad response cohorts were merged.</div></div><div><h3>Results</h3><div>The predictive performance of the model was calculated by means of the area under the receiver operator curve (AUC ROC). 95% Confidence intervals (CIs) were calculated for better understanding of the range of values. In the test set, the AUC ROC performance score was 0.696 with a CI of 0.532–0.861.</div></div><div><h3>Conclusion</h3><div>This proof-of-concept study shows that H&E pre-operative biopsies from TNBC, by means of deep learning technology, contain valuable information having predictive value for the outcome of NAC resulting in an AUC value of 0.696 outperforming a predictive AUC value of 0.63 based on structured clinical data of histological tumor grade, TILs, and ki-67 known from the literature.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"18 ","pages":"Article 100448"},"PeriodicalIF":0.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144213221","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}
The emerging trend of vision language models (VLMs) has introduced a new paradigm in artificial intelligence (AI). However, their evaluation has predominantly focused on general-purpose datasets, providing a limited understanding of their effectiveness in specialized domains. Medical imaging, particularly digital pathology, could significantly benefit from VLMs for histological interpretation and diagnosis, enabling pathologists to use a complementary tool for faster morecomprehensive reporting and efficient healthcare service. In this work, we are interested in benchmarking VLMs on histopathology image understanding. We present an extensive evaluation of recent VLMs on the PathMMU dataset, a domain-specific benchmark that includes subsets such as PubMed, SocialPath, and EduContent. These datasets feature diverse formats, notably multiple-choice questions (MCQs), designed to aid pathologists in diagnostic reasoning and support professional development initiatives in histopathology. Utilizing VLMEvalKit, a widely used open-source evaluation framework—we bring publicly available pathology datasets under a single evaluation umbrella, ensuring unbiased and contamination-free assessments of model performance. Our study conducts extensive zero-shot evaluations of more than 60 state-of-the-art VLMs, including LLaVA, Qwen-VL, Qwen2-VL, InternVL, Phi3, Llama3, MOLMO, and XComposer series, significantly expanding the range of evaluated models compared to prior literature. Among the tested models, Qwen2-VL-72B-Instruct achieved superior performance with an average score of 63.97% outperforming other models across all PathMMU subsets. We conclude that this extensive evaluation will serve as a valuable resource, fostering the development of next-generation VLMs for analyzing digital pathology images. Additionally, we have released the complete evaluation results on our leaderboard PathVLM-Eval: https://huggingface.co/spaces/gilalnauman/PathVLMs.
{"title":"PathVLM-Eval: Evaluation of open vision language models in histopathology","authors":"Nauman Ullah Gilal , Rachida Zegour , Khaled Al-Thelaya , Erdener Özer , Marco Agus , Jens Schneider , Sabri Boughorbel","doi":"10.1016/j.jpi.2025.100455","DOIUrl":"10.1016/j.jpi.2025.100455","url":null,"abstract":"<div><div>The emerging trend of vision language models (VLMs) has introduced a new paradigm in artificial intelligence (AI). However, their evaluation has predominantly focused on general-purpose datasets, providing a limited understanding of their effectiveness in specialized domains. Medical imaging, particularly digital pathology, could significantly benefit from VLMs for histological interpretation and diagnosis, enabling pathologists to use a complementary tool for faster morecomprehensive reporting and efficient healthcare service. In this work, we are interested in benchmarking VLMs on histopathology image understanding. We present an extensive evaluation of recent VLMs on the PathMMU dataset, a domain-specific benchmark that includes subsets such as PubMed, SocialPath, and EduContent. These datasets feature diverse formats, notably multiple-choice questions (MCQs), designed to aid pathologists in diagnostic reasoning and support professional development initiatives in histopathology. Utilizing VLMEvalKit, a widely used open-source evaluation framework—we bring publicly available pathology datasets under a single evaluation umbrella, ensuring unbiased and contamination-free assessments of model performance. Our study conducts extensive zero-shot evaluations of more than 60 state-of-the-art VLMs, including LLaVA, Qwen-VL, Qwen2-VL, InternVL, Phi3, Llama3, MOLMO, and XComposer series, significantly expanding the range of evaluated models compared to prior literature. Among the tested models, Qwen2-VL-72B-Instruct achieved superior performance with an average score of 63.97% outperforming other models across all PathMMU subsets. We conclude that this extensive evaluation will serve as a valuable resource, fostering the development of next-generation VLMs for analyzing digital pathology images. Additionally, we have released the complete evaluation results on our leaderboard PathVLM-Eval: <span><span>https://huggingface.co/spaces/gilalnauman/PathVLMs</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"18 ","pages":"Article 100455"},"PeriodicalIF":0.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144596403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-01Epub Date: 2025-05-23DOI: 10.1016/j.jpi.2025.100454
Zahoor Ahmad , Mahmood Alzubaidi , Khaled Al-Thelaya , Corrado Calí , Sabri Boughorbel , Jens Schneider , Marco Agus
Histopathology is critical for disease diagnosis, and digital pathology has transformed traditional workflows by digitizing slides, enabling remote consultations, and enhancing analysis through computational methods. In this systematic review, we evaluated open-source visual analytics abilities in digital pathology by screening 254 studies and including 52 that met predefined criteria. Our analysis reveals that these solutions—comprising abilities (n = 29), software (n = 13), and frameworks (n = 10)—are predominantly applied in cancer research (e.g., breast, colon, ovarian, and prostate cancers) and primarily utilize whole slide images. Key contributions include advanced image analysis capabilities (as demonstrated by platforms such as QuPath and CellProfiler) and the integration of machine learning for diagnostic support, treatment planning, automated tissue segmentation, and collaborative research. Despite these promising advancements, challenges such as high computational demands, limited external validation, and difficulties integrating into clinical workflows remain. Future research should focus on establishing standardized validation frameworks, aligning with regulatory requirements, and enhancing user-centric designs to promote robust, interoperable solutions for clinical adoption.
{"title":"Advancing open-source visual analytics in digital pathology: A systematic review of tools, trends, and clinical applications","authors":"Zahoor Ahmad , Mahmood Alzubaidi , Khaled Al-Thelaya , Corrado Calí , Sabri Boughorbel , Jens Schneider , Marco Agus","doi":"10.1016/j.jpi.2025.100454","DOIUrl":"10.1016/j.jpi.2025.100454","url":null,"abstract":"<div><div>Histopathology is critical for disease diagnosis, and digital pathology has transformed traditional workflows by digitizing slides, enabling remote consultations, and enhancing analysis through computational methods. In this systematic review, we evaluated open-source visual analytics abilities in digital pathology by screening 254 studies and including 52 that met predefined criteria. Our analysis reveals that these solutions—comprising abilities (<em>n</em> = 29), software (<em>n</em> = 13), and frameworks (<em>n</em> = 10)—are predominantly applied in cancer research (e.g., breast, colon, ovarian, and prostate cancers) and primarily utilize whole slide images. Key contributions include advanced image analysis capabilities (as demonstrated by platforms such as QuPath and CellProfiler) and the integration of machine learning for diagnostic support, treatment planning, automated tissue segmentation, and collaborative research. Despite these promising advancements, challenges such as high computational demands, limited external validation, and difficulties integrating into clinical workflows remain. Future research should focus on establishing standardized validation frameworks, aligning with regulatory requirements, and enhancing user-centric designs to promote robust, interoperable solutions for clinical adoption.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"18 ","pages":"Article 100454"},"PeriodicalIF":0.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144298275","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 : 2025-08-01Epub 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":"2025-08-01","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}
Pub Date : 2025-08-01Epub Date: 2025-05-16DOI: 10.1016/j.jpi.2025.100451
Yunus Baran Kök, Işın Doğan Ekici, Ümit İnce
Calculation of tumor cell percentage, a critical pre-analytical component in molecular pathology, is typically performed by pathologists estimating a ratio. This semiquantitative approach can lead to inter-observer variability, potentially adversely affecting patient management and treatment outcomes. In era of digital pathology, it became crucial to automate such assessments for more objective approach. This study aims to contribute to this process by developing a model for automated calculation of tumor cell percentage in high-grade serous carcinomas. Tumor containing hematoxylin-eosin slides from 100 patients were divided into training, validation, and test groups. Slides were digitalized and placed in QuPath platform. Image patches were obtained from WSIs of training and validation sets, and were stitched together to form digital microarrays by using ImageJ extension. Subsequently, nuclear detection and segmentation were performed using StarDist software, and tumor and non-tumor cell nuclei were classified using annotations. For binary classifier, random forest algorithm was selected. With hyperparameter tuning, many pre-models were assessed by cross-validation and most suitable pre-model was selected to apply to test set. Testing was performed on WSIs and criterion standard was based on corresponding immunohistochemistry (p53 or PAX8) slides which showed diffuse positivitity for tumor cells. Performance of model was measured using regression metrics. This study is designed to perform and assess a classifier in whole slide images to reflect real-world experience.
{"title":"Automated determination of tumor cell percentages in whole slide images: A nuclear classification study for molecular pathology tests","authors":"Yunus Baran Kök, Işın Doğan Ekici, Ümit İnce","doi":"10.1016/j.jpi.2025.100451","DOIUrl":"10.1016/j.jpi.2025.100451","url":null,"abstract":"<div><div>Calculation of tumor cell percentage, a critical pre-analytical component in molecular pathology, is typically performed by pathologists estimating a ratio. This semiquantitative approach can lead to inter-observer variability, potentially adversely affecting patient management and treatment outcomes. In era of digital pathology, it became crucial to automate such assessments for more objective approach. This study aims to contribute to this process by developing a model for automated calculation of tumor cell percentage in high-grade serous carcinomas. Tumor containing hematoxylin-eosin slides from 100 patients were divided into training, validation, and test groups. Slides were digitalized and placed in QuPath platform. Image patches were obtained from WSIs of training and validation sets, and were stitched together to form digital microarrays by using ImageJ extension. Subsequently, nuclear detection and segmentation were performed using StarDist software, and tumor and non-tumor cell nuclei were classified using annotations. For binary classifier, random forest algorithm was selected. With hyperparameter tuning, many pre-models were assessed by cross-validation and most suitable pre-model was selected to apply to test set. Testing was performed on WSIs and criterion standard was based on corresponding immunohistochemistry (p53 or PAX8) slides which showed diffuse positivitity for tumor cells. Performance of model was measured using regression metrics. This study is designed to perform and assess a classifier in whole slide images to reflect real-world experience.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"18 ","pages":"Article 100451"},"PeriodicalIF":0.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144696478","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 : 2025-08-01Epub 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":"2025-08-01","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 : 2025-08-01Epub Date: 2025-04-22DOI: 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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