Journal of Pathology Informatics最新文献

{"title":"Technical note: Impact of tissue section thickness on accuracy of cell classification with a deep learning network","authors":"Ida Skovgaard Christiansen ,&nbsp;Rasmus Hartvig ,&nbsp;Thomas Hartvig Lindkær Jensen","doi":"10.1016/j.jpi.2025.100440","DOIUrl":"10.1016/j.jpi.2025.100440","url":null,"abstract":"<div><h3>Introduction</h3><div>We are currently developing a cell classification system intended for routine histopathology. During observation, cells of interest are added to a deep learning (DL) network, which after training classifies the remaining cells of interest with high and immediately validatable accuracy. In this study, we identify the optimal histological microsection thickness for this process and describe in high detail the morphological differences introduced by variation in microsection thickness.</div></div><div><h3>Method</h3><div>From HE-stained digitized sections of liver cut manually at 5 thicknesses and on an automated microtome (DS), hepatocytes and non-hepatocytes were manually annotated and loaded into a DL convolutional neural network (ResNet). The network was trained at different settings to identify the thickness with optimal relation between number of training cells and validation accuracy. To shed interpretable light on the impact of thickness, exhaustive morphological details of the annotated cells were quantified and the differences between hepatocytes and non-hepatocytes were analyzed with random forest.</div></div><div><h3>Results</h3><div>Classifying hepatocytes from DS sections clearly resulted in highest validation accuracy with least number of cells and for the remaining thicknesses a trend towards thin sections being more efficient was observed. Random forest analysis generally identified variations in nuclear granularity as the most important features in distinguishing cells. In DS and the thinner tissue sections, nuclear granularity features were more distinguished.</div></div><div><h3>Conclusion</h3><div>Microsections cut with DS in particular and thin sections in general are better suited for the intended cell classification system.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"17 ","pages":"Article 100440"},"PeriodicalIF":0.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143874460","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":"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":"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":"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":"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}

{"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 “A study of thyroid fine needle aspiration of follicular adenoma in the ‘atypia of undetermined significance’ Bethesda category” [Journal of Pathology Informatics Volume 13, 2022, 100004]","authors":"Keluo Yao ,&nbsp;Xin Jing ,&nbsp;Jerome Cheng ,&nbsp;Ulysses G.J. Balis ,&nbsp;Liron Pantanowitz ,&nbsp;Madelyn Lew","doi":"10.1016/j.jpi.2025.100429","DOIUrl":"10.1016/j.jpi.2025.100429","url":null,"abstract":"","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"17 ","pages":"Article 100429"},"PeriodicalIF":0.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143886680","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":"Fast TILs—A pipeline for efficient TILs estimation in non-small cell Lung cancer","authors":"Nikita Shvetsov ,&nbsp;Anders Sildnes ,&nbsp;Masoud Tafavvoghi ,&nbsp;Lill-Tove Rasmussen Busund ,&nbsp;Stig Manfred Dalen ,&nbsp;Kajsa Møllersen ,&nbsp;Lars Ailo Bongo ,&nbsp;Thomas Karsten Kilvær","doi":"10.1016/j.jpi.2025.100437","DOIUrl":"10.1016/j.jpi.2025.100437","url":null,"abstract":"<div><div>The prognostic relevance of tumor-infiltrating lymphocytes (TILs) in non-small cell Lung cancer (NSCLC) is well-established. However, manual TIL quantification in hematoxylin and eosin (H&amp;E) whole slide images (WSIs) is laborious and prone to variability. To address this, we aim to develop and validate an automated computational pipeline for the quantification of TILs in WSIs of NSCLC. Such a solution in computational pathology can accelerate TIL evaluation, thereby standardizing the prognostication process and facilitating personalized treatment strategies.</div><div>We develop an end-to-end automated pipeline for TIL estimation in Lung cancer WSIs by integrating a patch extraction approach based on hematoxylin component filtering with a machine learning-based patch classification and cell quantification method using the HoVer-Net model architecture. Additionally, we employ randomized patch sampling to further reduce the processed patch amount. We evaluate the effectiveness of the patch sampling procedure, the pipeline's ability to identify informative patches and computational efficiency, and the clinical value of produced scores using patient survival data.</div><div>Our pipeline demonstrates the ability to selectively process informative patches, achieving a balance between computational efficiency and prognostic integrity. The pipeline filtering excludes approximately 70% of all patch candidates. Further, only 5% of eligible patches are necessary to retain the pipeline's prognostic accuracy (c-index = 0.65), resulting in a linear reduction of the total computational time compared to the filtered patch subset analysis. The pipeline's TILs score has a strong association with patient survival and outperforms traditional CD8 immunohistochemical scoring (c-index = 0.59). Kaplan–Meier analysis further substantiates the TILs score's prognostic value.</div><div>This study introduces an automated pipeline for TIL evaluation in Lung cancer WSIs, providing a prognostic tool with potential to improve personalized treatment in NSCLC. The pipeline's computational advances, particularly in reducing processing time, and clinical relevance demonstrate a step forward in computational pathology.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"17 ","pages":"Article 100437"},"PeriodicalIF":0.0,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725776","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}