Journal of Pathology Informatics最新文献

{"title":"A novel rotation and scale-invariant deep learning framework leveraging conical transformers for precise differentiation between meningioma and solitary fibrous tumor","authors":"Mohamed T. Azam ,&nbsp;Hossam Magdy Balaha ,&nbsp;Akshitkumar Mistry ,&nbsp;Khadiga M. Ali ,&nbsp;Bret C. Mobley ,&nbsp;Nalin Leelatian ,&nbsp;Sanjay Bhatia ,&nbsp;Murat Gokden ,&nbsp;Norman Lehman ,&nbsp;Mohammed Ghazal ,&nbsp;Ayman El-Baz ,&nbsp;Dibson D. Gondim","doi":"10.1016/j.jpi.2025.100422","DOIUrl":"10.1016/j.jpi.2025.100422","url":null,"abstract":"<div><div>Meningiomas, the most prevalent tumors of the central nervous system, can have overlapping histopathological features with solitary fibrous tumors (SFT), presenting a significant diagnostic challenge. Accurate differentiation between these two diagnoses is crucial for optimal medical management. Currently, immunohistochemistry and molecular techniques are the methods of choice for distinguishing between them; however, these techniques are expensive and not universally available. In this article, we propose a rotational and scale-invariant deep learning framework to enable accurate discrimination between these two tumor types. The proposed framework employs a novel architecture of conical transformers to capture both global and local imaging markers from whole-slide images, accommodating variations across different magnification scales. A weighted majority voting schema is utilized to combine individual scale decisions, ultimately producing a complementary and more accurate diagnostic outcome. A dataset comprising 92 patients (46 with meningioma and 46 with SFT) was used for evaluation. The experimental results demonstrate robust performance across different validation methods. In train-test evaluation, the model achieved 92.27% accuracy, 87.77% sensitivity, 97.55% specificity, and 92.46% F1-score. Performance further improved in 4-fold cross-validation, achieving 94.68% accuracy, 96.05% sensitivity, 93.11% specificity, and 95.07% F1-score. These findings highlight the potential of AI-based diagnostic approaches for precise differentiation between meningioma and SFT, paving the way for innovative diagnostic tools in pathology.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"17 ","pages":"Article 100422"},"PeriodicalIF":0.0,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143519352","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":"Unlocking the potential of digital pathology: Novel baselines for compression","authors":"Maximilian Fischer ,&nbsp;Peter Neher ,&nbsp;Peter Schüffler ,&nbsp;Sebastian Ziegler ,&nbsp;Shuhan Xiao ,&nbsp;Robin Peretzke ,&nbsp;David Clunie ,&nbsp;Constantin Ulrich ,&nbsp;Michael Baumgartner ,&nbsp;Alexander Muckenhuber ,&nbsp;Silvia Dias Almeida ,&nbsp;Michael Gőtz ,&nbsp;Jens Kleesiek ,&nbsp;Marco Nolden ,&nbsp;Rickmer Braren ,&nbsp;Klaus Maier-Hein","doi":"10.1016/j.jpi.2025.100421","DOIUrl":"10.1016/j.jpi.2025.100421","url":null,"abstract":"<div><div>Digital pathology offers a groundbreaking opportunity to transform clinical practice in histopathological image analysis, yet faces a significant hurdle: the substantial file sizes of pathological whole slide images (WSIs). Whereas current digital pathology solutions rely on lossy JPEG compression to address this issue, lossy compression can introduce color and texture disparities, potentially impacting clinical decision-making. Whereas prior research addresses perceptual image quality and downstream performance independently of each other, we jointly evaluate compression schemes for perceptual and downstream task quality on four different datasets. In addition, we collect an initially uncompressed dataset for an unbiased perceptual evaluation of compression schemes. Our results show that deep learning models fine-tuned for perceptual quality outperform conventional compression schemes like JPEG-XL or WebP for further compression of WSI. However, they exhibit a significant bias towards the compression artifacts present in the training data and struggle to generalize across various compression schemes. We introduce a novel evaluation metric based on feature similarity between original files and compressed files that aligns very well with the actual downstream performance on the compressed WSI. Our metric allows for a general and standardized evaluation of lossy compression schemes and mitigates the requirement to independently assess different downstream tasks. Our study provides novel insights for the assessment of lossy compression schemes for WSI and encourages a unified evaluation of lossy compression schemes to accelerate the clinical uptake of digital pathology.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"17 ","pages":"Article 100421"},"PeriodicalIF":0.0,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143444736","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":"Attention induction based on pathologist annotations for improving whole slide pathology image classifier","authors":"Ryoichi Koga ,&nbsp;Tatsuya Yokota ,&nbsp;Koji Arihiro ,&nbsp;Hidekata Hontani","doi":"10.1016/j.jpi.2024.100413","DOIUrl":"10.1016/j.jpi.2024.100413","url":null,"abstract":"<div><div>We propose a method of <em>attention induction</em> to improve an attention mechanism in a whole slide image (WSI) classifier. Generally, only some regions in a WSI are useful for lesion classification, and the WSI classifier is required to find and focus on such regions for the classification. Multiple instance learning and hierarchical representation learning are widely employed for WSI processing and both use attention mechanisms to automatically find the useful regions and then conduct the class prediction. Here, it is impractical to collect a large number of WSIs, and when the attention mechanism is trained with a small number of training WSIs, the resultant attention often fails to focus on the useful regions. To improve the attention mechanism without increasing the number of training WSIs, we propose a method of attention induction for a hierarchical representation of WSI that guides attention to focus on the regions useful for lesion classification based on pathologist's coarse annotations. Our experimental results demonstrate that the proposed method improves the attention mechanism, thereby enhancing the performance of WSI classification.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"16 ","pages":"Article 100413"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11750489/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143025178","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":"Advancements in pathology: Digital transformation, precision medicine, and beyond","authors":"Sana Ahuja,&nbsp;Sufian Zaheer","doi":"10.1016/j.jpi.2024.100408","DOIUrl":"10.1016/j.jpi.2024.100408","url":null,"abstract":"<div><div>Pathology, a cornerstone of medical diagnostics and research, is undergoing a revolutionary transformation fueled by digital technology, molecular biology advancements, and big data analytics. Digital pathology converts conventional glass slides into high-resolution digital images, enhancing collaboration and efficiency among pathologists worldwide. Integrating artificial intelligence (AI) and machine learning (ML) algorithms with digital pathology improves diagnostic accuracy, particularly in complex diseases like cancer. Molecular pathology, facilitated by next-generation sequencing (NGS), provides comprehensive genomic, transcriptomic, and proteomic insights into disease mechanisms, guiding personalized therapies. Immunohistochemistry (IHC) plays a pivotal role in biomarker discovery, refining disease classification and prognostication. Precision medicine integrates pathology's molecular findings with individual genetic, environmental, and lifestyle factors to customize treatment strategies, optimizing patient outcomes. Telepathology extends diagnostic services to underserved areas through remote digital pathology. Pathomics leverages big data analytics to extract meaningful insights from pathology images, advancing our understanding of disease pathology and therapeutic targets. Virtual autopsies employ non-invasive imaging technologies to revolutionize forensic pathology. These innovations promise earlier diagnoses, tailored treatments, and enhanced patient care. Collaboration across disciplines is essential to fully realize the transformative potential of these advancements in medical practice and research.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"16 ","pages":"Article 100408"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143092063","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":"Prioritizing cases from a multi-institutional cohort for a dataset of pathologist annotations","authors":"Victor Garcia ,&nbsp;Emma Gardecki ,&nbsp;Stephanie Jou ,&nbsp;Xiaoxian Li ,&nbsp;Kenneth R. Shroyer ,&nbsp;Joel Saltz ,&nbsp;Balazs Acs ,&nbsp;Katherine Elfer ,&nbsp;Jochen Lennerz ,&nbsp;Roberto Salgado ,&nbsp;Brandon D. Gallas","doi":"10.1016/j.jpi.2024.100411","DOIUrl":"10.1016/j.jpi.2024.100411","url":null,"abstract":"<div><h3>Objective</h3><div>With the increasing energy surrounding the development of artificial intelligence and machine learning (AI/ML) models, the use of the same external validation dataset by various developers allows for a direct comparison of model performance. Through our High Throughput Truthing project, we are creating a validation dataset for AI/ML models trained in the assessment of stromal tumor-infiltrating lymphocytes (sTILs) in triple negative breast cancer (TNBC).</div></div><div><h3>Materials and methods</h3><div>We obtained clinical metadata for hematoxylin and eosin-stained glass slides and corresponding scanned whole slide images (WSIs) of TNBC core biopsies from two US academic medical centers. We selected regions of interest (ROIs) from the WSIs to target regions with various tissue morphologies and sTILs densities. Given the selected ROIs, we implemented a hierarchical rank-sort method for case prioritization.</div></div><div><h3>Results</h3><div>We received 122 glass slides and clinical metadata on 105 unique patients with TNBC. All received cases were female, and the mean age was 63.44 years. 60% of all cases were White patients, and 38.1% were Black or African American. After case prioritization, the skewness of the sTILs density distribution improved from 0.60 to 0.46 with a corresponding increase in the entropy of the sTILs density bins from 1.20 to 1.24. We retained cases with less prevalent metadata elements.</div></div><div><h3>Conclusion</h3><div>This method allows us to prioritize underrepresented subgroups based on important clinical factors. In this manuscript, we discuss how we sourced the clinical metadata, selected ROIs, and developed our approach to prioritizing cases for inclusion in our pivotal study.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"16 ","pages":"Article 100411"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11667696/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142886209","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":"Liver fibrosis classification on trichrome histology slides using weakly supervised learning in children and young adults","authors":"Mahdieh Shabanian ,&nbsp;Zachary Taylor ,&nbsp;Christopher Woods ,&nbsp;Anas Bernieh ,&nbsp;Jonathan Dillman ,&nbsp;Lili He ,&nbsp;Sarangarajan Ranganathan ,&nbsp;Jennifer Picarsic ,&nbsp;Elanchezhian Somasundaram","doi":"10.1016/j.jpi.2024.100416","DOIUrl":"10.1016/j.jpi.2024.100416","url":null,"abstract":"<div><h3>Background</h3><div>Traditional liver fibrosis staging via percutaneous biopsy suffers from sampling bias and variable inter-pathologist agreement, highlighting the need for more objective techniques. Deep learning models for disease staging from medical images have shown potential to decrease diagnostic variability, with recent weakly supervised learning strategies showing promising results even with limited manual annotation.</div></div><div><h3>Purpose</h3><div>To study the clustering-constrained attention multiple instance learning (CLAM) approach for staging liver fibrosis on trichrome whole slide images (WSIs) of children and young adults.</div></div><div><h3>Methods</h3><div>This is an ethics board approved retrospective study utilizing 217 trichrome WSI from pediatric liver biopsies for model development and testing. Two pediatric pathologists scored WSI using two liver fibrosis staging systems, METAVIR and Ishak. Cases were then secondarily categorized into either high- or low-stage liver fibrosis and used for model development. The CLAM pipeline was used to develop binary classification models for histological liver fibrosis. Model performance was evaluated using area under the curve (AUC), accuracy, sensitivity, specificity, and Cohen's Kappa.</div></div><div><h3>Results</h3><div>The CLAM models showed strong diagnostic performance, with sensitivities up to 0.76 and AUCs up to 0.92 for distinguishing low- and high-stage fibrosis. The agreement between model predictions and average pathologist scores was moderate to substantial (Kappa: 0.57–0.69), whereas pathologist agreement on the METAVIR and Ishak scoring systems was only fair (Kappa: 0.39–0.46).</div></div><div><h3>Conclusions</h3><div>CLAM pipeline showed promise in detecting features important for differentiating low- and high-stage fibrosis from trichrome WSI based on the results, offering a promising objective method for liver fibrosis detection in children and young adults.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"16 ","pages":"Article 100416"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11760786/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143048182","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 pre-trained machine learning models for islet quantification in type 1 diabetes","authors":"Sanghoon Kang ,&nbsp;Jesus D. Penaloza Aponte ,&nbsp;Omar Elashkar ,&nbsp;Juan Francisco Morales ,&nbsp;Nicholas Waddington ,&nbsp;Damon G. Lamb ,&nbsp;Huiwen Ju ,&nbsp;Martha Campbell-Thompson ,&nbsp;Sarah Kim","doi":"10.1016/j.jpi.2024.100406","DOIUrl":"10.1016/j.jpi.2024.100406","url":null,"abstract":"<div><div>Human islets display a high degree of heterogeneity in terms of size, number, architecture, and endocrine cell-type compositions. An ever-increasing number of immunohistochemistry-stained whole slide images (WSIs) are available through the online pathology database of the Network for Pancreatic Organ donors with Diabetes (nPOD) program at the University of Florida (UF). We aimed to develop an enhanced machine learning-assisted WSI analysis workflow to utilize the nPOD resource for analysis of endocrine cell heterogeneity in the natural history of type 1 diabetes (T1D) in comparison to donors without diabetes. To maximize usability, the user-friendly open-source software QuPath was selected for the main interface. The WSI data were analyzed with two pre-trained machine learning models (i.e., Segment Anything Model (SAM) and QuPath's pixel classifier), using the UF high-performance-computing cluster, HiPerGator. SAM was used to define precise endocrine cell and cell grouping boundaries (with an average quality score of 0.91 per slide), and the artificial neural network-based pixel classifier was applied to segment areas of insulin- or glucagon-stained cytoplasmic regions within each endocrine cell. An additional script was developed to automatically count CD3+ cells inside and within 20 μm of each islet perimeter to quantify the number of islets with inflammation (i.e., CD3+ T-cell infiltration). Proof-of-concept analysis was performed to test the developed workflow in 12 subjects using 24 slides. This open-source machine learning-assisted workflow enables rapid and high throughput determinations of endocrine cells, whether as single cells or within groups, across hundreds of slides. It is expected that the use of this workflow will accelerate our understanding of endocrine cell and islet heterogeneity in the context of T1D endotypes and pathogenesis.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"16 ","pages":"Article 100406"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11665367/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142886207","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 2024 Overview","authors":"","doi":"10.1016/j.jpi.2025.100419","DOIUrl":"10.1016/j.jpi.2025.100419","url":null,"abstract":"","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"16 ","pages":"Article 100419"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143478903","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":"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}