Journal of Pathology Informatics最新文献

{"title":"Fully automatic HER2 tissue segmentation for interpretable HER2 scoring","authors":"Mathias Öttl ,&nbsp;Jana Steenpass ,&nbsp;Frauke Wilm ,&nbsp;Jingna Qiu ,&nbsp;Matthias Rübner ,&nbsp;Corinna Lang-Schwarz ,&nbsp;Cecilia Taverna ,&nbsp;Francesca Tava ,&nbsp;Arndt Hartmann ,&nbsp;Hanna Huebner ,&nbsp;Matthias W. Beckmann ,&nbsp;Peter A. Fasching ,&nbsp;Andreas Maier ,&nbsp;Ramona Erber ,&nbsp;Katharina Breininger","doi":"10.1016/j.jpi.2025.100435","DOIUrl":"10.1016/j.jpi.2025.100435","url":null,"abstract":"<div><div>Breast cancer is the most common cancer in women, with HER2 (human epidermal growth factor receptor 2) overexpression playing a critical role in regulating cell growth and division. HER2 status, assessed according to established scoring guidelines, offers important information for treatment selection. However, the complexity of the task leads to variability in human rater assessments. In this work, we propose a fully automated, interpretable HER2 scoring pipeline based on pixel-level semantic segmentations, designed to align with clinical guidelines. Using polygon annotations, our method balances annotation effort with the ability to capture fine-grained details and larger structures, such as non-invasive tumor tissue.</div><div>To enhance HER2 segmentation, we propose the use of a Wasserstein Dice loss to model class relationships, ensuring robust segmentation and HER2 scoring performance. Additionally, based on observations of pathologists' behavior in clinical practice, we propose a calibration step to the scoring rules, which positively impacts the accuracy and consistency of automated HER2 scoring. Our approach achieves an F1 score of 0.832 on HER2 scoring, demonstrating its effectiveness. This work establishes a potent segmentation pipeline that can be further leveraged to analyze HER2 expression in breast cancer tissue.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"17 ","pages":"Article 100435"},"PeriodicalIF":0.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of the efficiency of digital pathology with the conventional methodology for the diagnosis of biopsies in an anatomical pathology laboratory in Spain","authors":"J.I. Echeveste ,&nbsp;L. Alvarez-Gigli ,&nbsp;D. Carcedo ,&nbsp;Y. Soto-Serrano ,&nbsp;M.D. Lozano","doi":"10.1016/j.jpi.2025.100439","DOIUrl":"10.1016/j.jpi.2025.100439","url":null,"abstract":"<div><h3>Background/objective</h3><div>Digital pathology (DP) encompasses the digitization of processes related to the acquisition, storage, transmission, and analysis of pathological data, contrasting with conventional methodology (CM) using optical microscopes. This study evaluates the efficiency of DP versus CM in a Spanish pathology department.</div></div><div><h3>Methods</h3><div>Observational, retrospective, and non-interventional study comparing biopsy samples from 2021 (cases diagnosed using CM) and 2022 (using DP). Variables analyzed were the pathologist who made the diagnosis, the number of slides, and the case area. Outcome efficiency variables were the turnaround-time (TaT), pending cases (active cases each pathologist accumulates daily), and pathologist workload. A significance level of 5% was established, and an exploratory cost-analysis was also performed.</div></div><div><h3>Results</h3><div>11,922 cases were analyzed: 5,836 and 6,086 diagnosed with CM and DP methodologies, respectively. Mean TaT for CM-diagnosed cases was 10.58 (standard deviation [SD] 7.10) days, compared to 6.86 (SD 5.10) days for DP-diagnosed cases, reflecting a reduction of 3.72 days (<em>P</em> &lt; 0.001). With DP, the average reduction in pending cases over a year was around 25 cases, with peaks of 100 fewer pending cases during high workload months. Additionally, DP decreased the pathologist workload by 29.2% on average, with reductions exceeding 50% during peak months.</div></div><div><h3>Conclusion</h3><div>Our study is the first in Spain to compare the efficiency and costs of DP and CM. DP demonstrated significant efficiency improvements over CM, reducing TaT and pathologist workload. Despite higher initial costs, DP's operational benefits indicate its potential as a transformative diagnostic tool. Further studies are needed to evaluate its long-term cost-effectiveness.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"17 ","pages":"Article 100439"},"PeriodicalIF":0.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143883255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ARMSprimer3: An open-source primer design Python program for amplification refractory mutation system PCR (ARMS-PCR)","authors":"Jingwen Guo ,&nbsp;Jeremy Grojean ,&nbsp;Huazhang Guo","doi":"10.1016/j.jpi.2025.100442","DOIUrl":"10.1016/j.jpi.2025.100442","url":null,"abstract":"<div><div>Single nucleotide polymorphisms (SNPs) are DNA sequence variations of a single base pair. They are the underlying mechanism of most human genetic variation and etiology of many heritable human diseases. SNPs can be reliably detected by amplification refractory mutation system PCR (ARMS-PCR). ARMS-PCR is based on allele-specific PCR primers that only amplify DNA samples with the target allele and do not amplify DNA samples without the target allele. In addition to the allele-specific mismatch at the 3′ end, ARMS-PCR introduces additional deliberate mismatches near the 3′ end of the allele-specific primers to further destabilize the non-specific binding and priming on non-target alleles. This modification increases the specificity for SNP detection, but also increases the complexity of PCR primer design. We developed ARMSprimer3, a Python program to automate the ARMS-PCR primer design process. The validity of ARMSprimer3 was confirmed by successfully using it to develop diagnostic tests in our clinical molecular diagnostic lab. ARMSprimer3 is open-source software and can be freely downloaded from <span><span>https://github.com/PCRPrimerDesign/ARMSprimer3</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"17 ","pages":"Article 100442"},"PeriodicalIF":0.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143899418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Erratum to “Artificial intelligence-based tumor segmentation in mouse models of lung adenocarcinoma” [Journal of Pathology Informatics Volume 13, 2022, 100007]","authors":"Alena Arlova ,&nbsp;Chengcheng Jin ,&nbsp;Abigail Wong-Rolle ,&nbsp;Eric S. Chen ,&nbsp;Curtis Lisle ,&nbsp;G. Thomas Brown ,&nbsp;Nathan Lay ,&nbsp;Peter L. Choyke ,&nbsp;Baris Turkbey ,&nbsp;Stephanie Harmon ,&nbsp;Chen Zhao","doi":"10.1016/j.jpi.2025.100425","DOIUrl":"10.1016/j.jpi.2025.100425","url":null,"abstract":"","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"17 ","pages":"Article 100425"},"PeriodicalIF":0.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143874461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of artificial intelligence image processing with manual leucocyte differential to score immune cell infiltration in a mouse infection model of cystic fibrosis","authors":"Madeline G. Williams,&nbsp;Zachary J. Faber,&nbsp;Thomas J. Kelley","doi":"10.1016/j.jpi.2025.100438","DOIUrl":"10.1016/j.jpi.2025.100438","url":null,"abstract":"<div><div>Immune cell differentials are most commonly performed manually or with the use of automated cell sorting devices. However, manual review by research personnel can be both subjective and time consuming, and cell sorting approaches consume samples and demand additional reagents to perform the differential. We have created an artificial intelligence (AI) image processing pipeline using the Biodock.ai platform to classify immune cell types from Giemsa-stained cytospins of mouse bronchoalveolar lavage fluid. Through multiple rounds of training and refinement, we have created a tool that is as accurate as manual review of slide images while removing the subjectivity and making the process mostly hands off, saving researcher time for other tasks and improving core turnaround for experiments.</div><div>This AI-based image processing is directly compatible with current workflows utilizing stained slides, in contrast to a change to a flow cytometry-based approach, which requires both specialized equipment, reagents, and expertise.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"17 ","pages":"Article 100438"},"PeriodicalIF":0.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143838643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient merging and validation of deep learning-based nuclei segmentations in H&E slides from multiple models","authors":"Jagadheshwar Balan,&nbsp;Shannon K. McDonnell,&nbsp;Zachary Fogarty,&nbsp;Nicholas B. Larson","doi":"10.1016/j.jpi.2025.100443","DOIUrl":"10.1016/j.jpi.2025.100443","url":null,"abstract":"<div><div>Characterizing cellular composition in tissue samples offers fundamental insights into functional and biological processes. Understanding the abundance or lack of specific cell types, such as inflammatory cells in the context of microenvironments such as tumor can help guide disease progression and personalized medicine. Several clinical laboratory methods to characterize the cellular composition are limited by scalability and high-costs. Digitizing pathology slides and applying deep learning (DL) models have enabled efficient and cost-effective nuclei segmentation and cell type quantification; however, the DL-models are limited by their inability to segment specific cell types and specific models may be more effective than others at certain tasks. Consequently, there remains a need for methods that leverage the strengths of multiple models to efficiently integrate nuclei segmentation for various cell types. In this study, we propose a novel solution for integrating nuclei segmentation from multiple DL-methods on hematoxylin and eosin slides from 471 normal prostate samples and highlight the limitations of using a single DL-method. We validate the DL-derived cell type proportions, by comparing against estimates from a manual pathologist review and show that the integrated approach results in higher concordance over the individual models. We further validate the derived cell type proportions from the DL-methods by their ability to explain the variance of RNA gene expression. The integrated approach yields robust cell type proportions that explain the variance of the gene expression with 12% and 22% relative improvement than current state-of-the-art model and manual pathologist review, respectively. The subset of 403 genes with high explained variation (&gt;30%) by epithelial proportion were significantly enriched for relevant biological pathways. These findings indicate that ensemble approaches to nuclei segmentation and cell-type classification may provide more accurate representations of cellular composition from digitized slides.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"17 ","pages":"Article 100443"},"PeriodicalIF":0.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143927403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Erratum to “MiNuGAN: Dual segmentation of mitoses and nuclei using conditional GANs on multi-center breast H&E images” [Journal of Pathology Informatics Volume 13, 2022, 100002]","authors":"Salar Razavi ,&nbsp;Fariba D. Khameneh ,&nbsp;Hana Nouri ,&nbsp;Dimitrios Androutsos ,&nbsp;Susan J. Done ,&nbsp;April Khademi","doi":"10.1016/j.jpi.2025.100426","DOIUrl":"10.1016/j.jpi.2025.100426","url":null,"abstract":"","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"17 ","pages":"Article 100426"},"PeriodicalIF":0.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143883256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning model for detecting high-grade dysplasia in colorectal adenomas","authors":"Eric Steimetz ,&nbsp;Zeliha Celen Simsek ,&nbsp;Asmita Saha ,&nbsp;Rong Xia ,&nbsp;Raavi Gupta","doi":"10.1016/j.jpi.2025.100441","DOIUrl":"10.1016/j.jpi.2025.100441","url":null,"abstract":"<div><h3>Objective</h3><div>Early detection and removal of suspicious polyps during routine colonoscopies play an important role in reducing the risk of colorectal cancer. Patient management and follow-up are determined by the type of polyps removed and the degree of dysplasia present on histological evaluation. Whereas discerning between a benign polyp and a dysplastic one is a trivial task, distinguishing between tubular adenomas with low-grade dysplasia (LGD) and high-grade dysplasia (HGD) is a challenging task. In this study, we trained a deep learning model to distinguish between colorectal adenomas with LGD and HGD.</div></div><div><h3>Design</h3><div>We retrieved 259 slides of adenomatous polyps taken between January 2011 and October 2024. Slides with HGD were reviewed by a subspecialty-trained GI pathologist. After excluding discordant and duplicate cases, 200 slides remained: 71 (35.5%) with HGD and 129 (64.5%) with LGD. The slides were divided into training (160 slides, 80%) and test (40 slides, 20%) sets. After patch generation and stain normalization, a ResNet34 model (pre-trained on ImageNet) was trained using 5-fold cross-validation. Slide classification was determined by aggregating patch-level predictions.</div></div><div><h3>Results</h3><div>The model's slide-level prediction accuracy was 95.0%, correctly classifying all but 2 out of 40 slides. The model achieved an area under the receiver operating characteristic curve score of 0.981 and an F1 score of 0.923.</div></div><div><h3>Conclusions</h3><div>This study demonstrates that deep learning models can accurately distinguish between colonic adenomas with LGD and HGD. Training on a larger dataset could increase the accuracy and generalizability of the model and should be a focus of further studies.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"17 ","pages":"Article 100441"},"PeriodicalIF":0.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143916886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PIRO: A web-based search platform for pathology reports, leveraging large language models to generate discrete searchable insights","authors":"Scott Robertson ,&nbsp;Venkata Koppireddy ,&nbsp;Jeremy Cumbo ,&nbsp;Hooman Rashidi ,&nbsp;Samer Albahra","doi":"10.1016/j.jpi.2025.100436","DOIUrl":"10.1016/j.jpi.2025.100436","url":null,"abstract":"<div><div>Pathologists rely on access to historical diagnostic case texts for research, education, and peer learning. However, many laboratory information systems (LIS), including Epic Beaker, lack optimized search tools tailored to pathology-specific text queries. To address this need, we developed PIRO (Pathology Information Retrieval Optimizer), a web-based platform enabling efficient text searches of diagnostic archives. Built using FastAPI, Angular, and Apache Solr, PIRO supports both basic and advanced search functionalities, faceted filtering, and data extraction, while ensuring compliance with institutional privacy protocols. PIRO's capabilities extend to case cohort building, search result export, and secure access control within the institutional network. In an 8-month study, we observed significantly higher PIRO adoption rates (67 %) among pathologists compared to Epic Beaker's SlicerDicer (9 %), underscoring PIRO's usability and relevance. Additionally, we implemented a large language model (LLM) to annotate reports with a “Malignancy Risk” label, enhancing search precision and enabling future expansion of automated annotations. Ongoing work focuses on integrating PIRO with our digital pathology platform, enabling direct access to digital slides from case results. PIRO's adaptable design makes it applicable across institutions, advancing search and retrieval efficiency in pathology archives and enhancing support for pathology research and education.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"17 ","pages":"Article 100436"},"PeriodicalIF":0.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143746751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Erratum to “Analysis of the three-year work of a digital pathomorphological laboratory built from the ground” [Journal of Pathology Informatics Volume 13, 2022, 100111]","authors":"Rudenko Ekaterina Evgenievna ,&nbsp;Demura Tatiana Alexandrovna ,&nbsp;Vekhova Ksenia Andreevna ,&nbsp;Lobanova Olga Andreevna ,&nbsp;Yumasheva Valentina Alekseevna ,&nbsp;Zhakota Dmitrii Anatolevich ,&nbsp;Anoshkin Kirill ,&nbsp;Remez Alexey ,&nbsp;Untesco Maksim ,&nbsp;Kroman Nikolay ,&nbsp;Mayer Artem ,&nbsp;Zhuravlev Alexander ,&nbsp;Kryatova Alexandra ,&nbsp;Lyapichev Kirill ,&nbsp;Genis Mikhail","doi":"10.1016/j.jpi.2025.100428","DOIUrl":"10.1016/j.jpi.2025.100428","url":null,"abstract":"","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"17 ","pages":"Article 100428"},"PeriodicalIF":0.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143874458","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}