Journal of Pathology Informatics最新文献

{"title":"Deep learning-based classification of breast cancer molecular subtypes from H&E whole-slide images","authors":"Masoud Tafavvoghi ,&nbsp;Anders Sildnes ,&nbsp;Mehrdad Rakaee ,&nbsp;Nikita Shvetsov ,&nbsp;Lars Ailo Bongo ,&nbsp;Lill-Tove Rasmussen Busund ,&nbsp;Kajsa Møllersen","doi":"10.1016/j.jpi.2024.100410","DOIUrl":"10.1016/j.jpi.2024.100410","url":null,"abstract":"<div><div>Classifying breast cancer molecular subtypes is crucial for tailoring treatment strategies. While immunohistochemistry (IHC) and gene expression profiling are standard methods for molecular subtyping, IHC can be subjective, and gene profiling is costly and not widely accessible in many regions. Previous approaches have highlighted the potential application of deep learning models on hematoxylin and eosin (H&amp;E)-stained whole-slide images (WSIs) for molecular subtyping, but these efforts vary in their methods, datasets, and reported performance. In this work, we investigated whether H&amp;E-stained WSIs could be solely leveraged to predict breast cancer molecular subtypes (luminal A, B, HER2-enriched, and Basal). We used 1433 WSIs of breast cancer in a two-step pipeline: first, classifying tumor and non-tumor tiles to use only the tumor regions for molecular subtyping; and second, employing a One-vs-Rest (OvR) strategy to train four binary OvR classifiers and aggregating their results using an eXtreme Gradient Boosting model. The pipeline was tested on 221 hold-out WSIs, achieving an F1 score of 0.95 for tumor vs non-tumor classification and a macro F1 score of 0.73 for molecular subtyping. Our findings suggest that, with further validation, supervised deep learning models could serve as supportive tools for molecular subtyping in breast cancer. Our codes are made available to facilitate ongoing research and development.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"16 ","pages":"Article 100410"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11667687/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142886203","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":"Economic evaluation: Impact on costs, time, and productivity of the incorporation of integrative digital pathology (IDP) in the anatomopathological analysis of breast cancer in a national reference public provider in Chile","authors":"Rony Lenz-Alcayaga ,&nbsp;Daniela Paredes-Fernández ,&nbsp;Fancy Gaete Verdejo ,&nbsp;Luciano Páez-Pizarro ,&nbsp;Karla Hernández-Sánchez","doi":"10.1016/j.jpi.2024.100417","DOIUrl":"10.1016/j.jpi.2024.100417","url":null,"abstract":"<div><h3>Introduction</h3><div>The incidence of breast cancer has risen in Chile, along with the complexity of diagnosis. For accurate diagnosis, it is necessary to complement the morphology assessed with hematoxylin and eosin with additional techniques to evaluate specific tumor markers. Evaluating the impact on costs, time, and productivity of automated techniques integrated with digital pathology solutions is crucial.</div></div><div><h3>Objectives</h3><div>To estimate the impact on costs, time, and productivity of incorporating the automation of the HER2 in situ hybridization technique combined with integrative digital pathology (IDP) in breast cancer diagnosis in a Chilean public provider versus a manual technique.</div></div><div><h3>Methods</h3><div>This economic evaluation adopted a health economics multi-method approach. A decision model was developed to represent the current manual fluorescence in situ hybridization (FISH) scenario versus an automated dual in situ hybridization (DISH) plus IDP in breast cancer diagnosis. Business process management (BPM) methodology was applied for capturing working time and latencies, in combination with a time-driven activity-based costing (TDABC) methodology for estimating direct, total, and average cost (2023 USD) for both scenarios for the following vectors: Human resources, supplies, and equipment, sorted by pre-analytical, analytical, and post-analytical phases. Indirect costs (2023 USD) were also retrieved. Both BPM and TDABC served to estimate labor productivity.</div></div><div><h3>Results</h3><div>In the baseline scenario based on manual FISH, the turnaround time (TAT) was estimated at 1259 min, at an average total cost of $265.67, considering direct and indirect costs for all phases. An average of 20.5 FISH reports were submitted per pathologist monthly during the baseline. The automated DISH plus IDP scenario consumed 203 min per biopsy, at an average total cost of $231.08, considering direct and indirect costs for all phases; it also showed an average of 22.8 submitted reports per pathologist monthly. This represents a decrease of 13.02% in average total costs, an 83.86% decrease in TAT, and an average labor productivity increase of 11.29%.</div></div><div><h3>Conclusions</h3><div>The incorporation of automated DISH plus IDP in the pathology department of this public provider has resulted in reductions in the time required to perform the in situ hybridization technique, a decrease in total costs, and increased productivity. Particular attention should be given to adopting new technologies to accelerate processing times and workflow.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"16 ","pages":"Article 100417"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143092062","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":"Iris: A Next Generation Digital Pathology Rendering Engine","authors":"Ryan Erik Landvater,&nbsp;Ulysses Balis","doi":"10.1016/j.jpi.2024.100414","DOIUrl":"10.1016/j.jpi.2024.100414","url":null,"abstract":"<div><div>Digital pathology is a tool of rapidly evolving importance within the discipline of pathology. Whole slide imaging promises numerous advantages; however, adoption is limited by challenges in ease of use and speed of high-quality image rendering relative to the simplicity and visual quality of glass slides. Herein, we introduce Iris, a new high-performance digital pathology rendering system. Specifically, we outline and detail the performance metrics of Iris Core, the core rendering engine technology. Iris Core comprises machine code modules written from the ground up in C++ and using Vulkan, a low-level and low-overhead cross-platform graphical processing unit application program interface, and our novel rapid tile buffering algorithms. We provide a detailed explanation of Iris Core's system architecture, including the stateless isolation of core processes, interprocess communication paradigms, and explicit synchronization paradigms that provide powerful control over the graphical processing unit. Iris Core achieves slide rendering at the sustained maximum frame rate on all tested platforms (120 FPS) and buffers an entire new slide field of view, without overlapping pixels, in 10 ms with enhanced detail in 30 ms. Further, it is able to buffer and compute high-fidelity reduction-enhancements for viewing low-power cytology with increased visual quality at a rate of 100–160 μs per slide tile, and with a cumulative median buffering rate of 1.36 GB of decompressed image data per second. This buffering rate allows for an entirely new field of view to be fully buffered and rendered in less than a single monitor refresh on a standard display, and high detail features within 2–3 monitor refresh frames. These metrics far exceed previously published specifications, beyond an order of magnitude in some contexts. The system shows no slowing with high use loads, but rather increases performance due to graphical processing unit cache control mechanisms and is “future-proof” due to near unlimited parallel scalability.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"16 ","pages":"Article 100414"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11742306/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143013435","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":"A standards-based application for improving platelet transfusion workflow","authors":"William Gordon ,&nbsp;Maria Aguad ,&nbsp;Layne Ainsworth ,&nbsp;Samuel Aronson ,&nbsp;Jane Baronas ,&nbsp;Edward Comeau ,&nbsp;Rory De La Paz ,&nbsp;Justin B.L. Halls ,&nbsp;Vincent T. Ho ,&nbsp;Michael Oates ,&nbsp;Adam Landman ,&nbsp;Wen Lu ,&nbsp;Shawn N. Murphy ,&nbsp;Fei Wang ,&nbsp;Indira Guleria ,&nbsp;Sean R. Stowell ,&nbsp;Melissa Y. Yeung ,&nbsp;Edgar L. Milford ,&nbsp;Richard M. Kaufman ,&nbsp;William J. Lane","doi":"10.1016/j.jpi.2024.100412","DOIUrl":"10.1016/j.jpi.2024.100412","url":null,"abstract":"<div><h3>Objective</h3><div>Thrombocytopenia is a common complication of hematopoietic stem-cell transplantation (HSCT), though many patients will become immune refractory to platelet transfusions over time. We built and evaluated an electronic health record (EHR)-integrated, standards-based application that enables blood-bank clinicians to match platelet inventory with patients using data previously not available at the point-of-care, like human leukocyte antigen (HLA) data for donors and recipients.</div></div><div><h3>Materials and methods</h3><div>The web-based application launches as an EHR-embedded application or as a standalone application. The application coalesces disparate data streams into a unified view, including platelet count, HLA data, demographics, and real-time inventory. We looked at application usage over time and developed a multivariable logistic regression model to compute odds ratios that a patient undergoing HSCT would have a complicated thrombocytopenia course, with several model covariates including pre-/post-application deployment.</div></div><div><h3>Results</h3><div>Usage of the application has been consistent since launch, with a slight dip during the first COVID wave. Our model, which included 376 patients in the final analysis, did not demonstrate a significantly decreased odds that a patient would have a complicated thrombocytopenia course after application deployment as compared to before application deployment.</div></div><div><h3>Discussion</h3><div>We built an EHR-integrated application to improve platelet transfusion processes. Whereas our model did not demonstrate decreased odds of a patient having a complicated thrombocytopenia course, there are other workflow and clinical benefits that will benefit from future evaluation.</div></div><div><h3>Conclusion</h3><div>A web-based, EHR-integrated application was built and integrated into our EHR system and is now part of the standard operating procedures of our blood bank.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"16 ","pages":"Article 100412"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11721207/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142972501","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":"Enhancing human phenotype ontology term extraction through synthetic case reports and embedding-based retrieval: A novel approach for improved biomedical data annotation","authors":"Abdulkadir Albayrak ,&nbsp;Yao Xiao ,&nbsp;Piyush Mukherjee ,&nbsp;Sarah S. Barnett ,&nbsp;Cherisse A. Marcou ,&nbsp;Steven N. Hart","doi":"10.1016/j.jpi.2024.100409","DOIUrl":"10.1016/j.jpi.2024.100409","url":null,"abstract":"<div><div>With the increasing utilization of exome and genome sequencing in clinical and research genetics, accurate and automated extraction of human phenotype ontology (HPO) terms from clinical texts has become imperative. Traditional methods for HPO term extraction, such as PhenoTagger, often face limitations in coverage and precision. In this study, we propose a novel approach that leverages large language models (LLMs) to generate synthetic sentences with clinical context, which were semantically encoded into vector embeddings. These embeddings are linked to HPO terms, creating a robust knowledgebase that facilitates precise information retrieval. Our method circumvents the known issue of LLM hallucinations by storing and querying these embeddings within a true database, ensuring accurate context matching without the need for a predictive model. We evaluated the performance of three different embedding models, all of which demonstrated substantial improvements over PhenoTagger. Top recall (sensitivity), precision (positive-predictive value, PPV), and F1 are 0.64, 0.64, and 0.64, respectively, which were 31%, 10%, and 21% better than PhenoTagger. Furthermore, optimal performance was achieved when we combined the best performing embedding model with PhenoTagger (a.k.a. Fused model), resulting in recall (sensitivity), precision (PPV), and F1 values of 0.7, 0.7, and 0.7, respectively, which are 10%, 10%, and 10% better than the best embedding models. Our findings underscore the potential of this integrated approach to enhance the precision and reliability of HPO term extraction, offering a scalable and effective solution for biomedical data annotation.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"16 ","pages":"Article 100409"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11667693/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142886205","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 Regarding Previously Published Articles","authors":"","doi":"10.1016/j.jpi.2024.100365","DOIUrl":"10.1016/j.jpi.2024.100365","url":null,"abstract":"","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"15 ","pages":"Article 100365"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11662270/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142878057","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":"Pathology Visions 2023 Overview","authors":"","doi":"10.1016/j.jpi.2024.100362","DOIUrl":"10.1016/j.jpi.2024.100362","url":null,"abstract":"","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"15 ","pages":"Article 100362"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139832128","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":"Learning to predict prostate cancer recurrence from tissue images","authors":"Mahtab Farrokh ,&nbsp;Neeraj Kumar ,&nbsp;Peter H. Gann ,&nbsp;Russell Greiner","doi":"10.1016/j.jpi.2023.100344","DOIUrl":"10.1016/j.jpi.2023.100344","url":null,"abstract":"<div><div>Roughly 30% of men with prostate cancer who undergo radical prostatectomy will suffer biochemical cancer recurrence (BCR). Accurately predicting which patients will experience BCR could identify who would benefit from increased surveillance or adjuvant therapy. Unfortunately, no current method can effectively predict this. We develop and evaluate PathCLR, a novel semi-supervised method that learns a model that can use hematoxylin and eosin (H&amp;E)-stained tissue microarrays (TMAs) to predict prostate cancer recurrence within 5 years after diagnosis. The learning process involves 2 sequential steps: PathCLR (a) first employs self-supervised learning to generate effective feature representations of the input images, then (b) feeds these learned features into a fully supervised neural network classifier to learn a model for predicting BCR. We conducted training and evaluation using 2 large prostate cancer datasets: (1) the Cooperative Prostate Cancer Tissue Resource (CPCTR) with 374 patients, including 189 who experienced BCR, and (2) the Johns Hopkins University (JHU) prostate cancer dataset of 646 patients, with 451 patients having BCR. PathCLR’s (10-fold cross-validation) F1 score was 0.61 for CPCTR and 0.85 for JHU. This was statistically superior (paired t-test with <em>P &lt;</em> <em>.</em>05) to the best-learned model that relied solely on clinicopathological features, including PSA level, primary and secondary Gleason Grade, etc. We attribute the improvement of PathCLR over models using only clinicopathological features to its utilization of both learned latent representations of tissue core images and clinicopathological features. This finding suggests that there is essential predictive information in tissue images at the time of surgery that goes beyond the knowledge obtained from reported clinicopathological features, helping predict the patient’s 5-year outcome.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"15 ","pages":"Article 100344"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135455776","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":"Leveraging deep learning for identification and segmentation of “CAF-1/p60-positive” nuclei in oral squamous cell carcinoma tissue samples","authors":"Silvia Varricchio ,&nbsp;Gennaro Ilardi ,&nbsp;Daniela Russo ,&nbsp;Rosa Maria Di Crescenzo ,&nbsp;Angela Crispino ,&nbsp;Stefania Staibano ,&nbsp;Francesco Merolla","doi":"10.1016/j.jpi.2024.100407","DOIUrl":"10.1016/j.jpi.2024.100407","url":null,"abstract":"<div><div>In the current study, we introduced a unique method for identifying and segmenting oral squamous cell carcinoma (OSCC) nuclei, concentrating on those predicted to have significant CAF-1/p60 protein expression. Our suggested model uses the StarDist architecture, a deep-learning framework designed for biomedical image segmentation tasks. The training dataset comprises painstakingly annotated masks created from tissue sections previously stained with hematoxylin and eosin (H&amp;E) and then restained with immunohistochemistry (IHC) for p60 protein. Our algorithm uses subtle morphological and colorimetric H&amp;E cellular characteristics to predict CAF-1/p60 IHC expression in OSCC nuclei. The StarDist-based architecture performs exceptionally well in localizing and segmenting H&amp;E nuclei, previously identified by IHC-based ground truth. In summary, our innovative approach harnesses deep learning and multimodal information to advance the automated analysis of OSCC nuclei exhibiting specific protein expression patterns. This methodology holds promise for expediting accurate pathological assessment and gaining deeper insights into the role of CAF-1/p60 protein within the context of oral cancer progression.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"15 ","pages":"Article 100407"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11653155/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142855784","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":"Improving the generalizability of white blood cell classification with few-shot domain adaptation","authors":"Manon Chossegros ,&nbsp;François Delhommeau ,&nbsp;Daniel Stockholm ,&nbsp;Xavier Tannier","doi":"10.1016/j.jpi.2024.100405","DOIUrl":"10.1016/j.jpi.2024.100405","url":null,"abstract":"<div><div>The morphological classification of nucleated blood cells is fundamental for the diagnosis of hematological diseases. Many Deep Learning algorithms have been implemented to automatize this classification task, but most of the time they fail to classify images coming from different sources. This is known as “domain shift”. Whereas some research has been conducted in this area, domain adaptation techniques are often computationally expensive and can introduce significant modifications to initial cell images. In this article, we propose an easy-to-implement workflow where we trained a model to classify images from two datasets, and tested it on images coming from eight other datasets. An EfficientNet model was trained on a source dataset comprising images from two different datasets. It was afterwards fine-tuned on each of the eight target datasets by using 100 or less-annotated images from these datasets. Images from both the source and the target dataset underwent a color transform to put them into a standardized color style. The importance of color transform and fine-tuning was evaluated through an ablation study and visually assessed with scatter plots, and an extensive error analysis was carried out. The model achieved an accuracy higher than 80% for every dataset and exceeded 90% for more than half of the datasets. The presented workflow yielded promising results in terms of generalizability, significantly improving performance on target datasets, whereas keeping low computational cost and maintaining consistent color transformations. Source code is available at: <span><span>https://github.com/mc2295/WBC_Generalization</span><svg><path></path></svg></span></div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"15 ","pages":"Article 100405"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142745793","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}