Journal of Pathology Informatics最新文献

{"title":"CDK: A novel high-performance transfer feature technique for early detection of osteoarthritis","authors":"Mohammad Shariful Islam ,&nbsp;Mohammad Abu Tareq Rony","doi":"10.1016/j.jpi.2024.100382","DOIUrl":"10.1016/j.jpi.2024.100382","url":null,"abstract":"<div><p>Knee osteoarthritis (OA) is a prevalent condition causing significant disability, particularly among the elderly, necessitating advancements in diagnostic methodologies to facilitate early detection and treatment. Traditional OA diagnosis, relying on radiography and physical exams, faces limitations in accuracy and objectivity. This underscores the need for more advanced diagnostic methods, such as machine learning (ML) and deep learning (DL), to improve OA detection and classification. This research introduces a novel ensemble learning approach for image data feature extraction which ingeniously combines the strengths of 2 advanced (ML) models with a (DL) method to substantially improve the accuracy of OA detection from radiographic images. This innovative strategy aims to address the limitations of traditional diagnostic tools by leveraging the enhanced sensitivity and specificity of combined ML and DL models. The methodology deployed in this study encompasses the application of 10 ML models to a comprehensive publicly available Kaggle dataset with a total of 3615 samples of knee X-ray images. Through rigorous k-fold cross-validation and meticulous hyperparameter optimization, we also included evaluation metrics like accuracy, receiver operating characteristic, precision, recall, and F1-score to assess our models' performance effectively. The proposed novel CDK (convolutional neural network, decision tree, K-nearest classifier) ensemble approach for feature extraction is designed to synergize the predictive capabilities of individual models, thereby significantly improving the detection accuracy of OA indicators within radiographic images. We applied several ML and DL approaches to the newly created feature set to evaluate performance. The CDK ensemble model outperformed state-of-the-art studies with a high-performance score of 99.72% accuracy. This remarkable achievement underscores the model's exceptional capability in the early detection of OA, highlighting its superiority in comparison to existing methods.</p></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"15 ","pages":"Article 100382"},"PeriodicalIF":0.0,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S215335392400021X/pdfft?md5=7758136ea8b6ff5e5d21a4755ae40c6d&pid=1-s2.0-S215335392400021X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141028032","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 selective CutMix approach improves generalizability of deep learning-based grading and risk assessment of prostate cancer","authors":"Sushant Patkar ,&nbsp;Stephanie Harmon ,&nbsp;Isabell Sesterhenn ,&nbsp;Rosina Lis ,&nbsp;Maria Merino ,&nbsp;Denise Young ,&nbsp;G. Thomas Brown ,&nbsp;Kimberly M. Greenfield ,&nbsp;John D. McGeeney ,&nbsp;Sally Elsamanoudi ,&nbsp;Shyh-Han Tan ,&nbsp;Cara Schafer ,&nbsp;Jiji Jiang ,&nbsp;Gyorgy Petrovics ,&nbsp;Albert Dobi ,&nbsp;Francisco J. Rentas ,&nbsp;Peter A. Pinto ,&nbsp;Gregory T. Chesnut ,&nbsp;Peter Choyke ,&nbsp;Baris Turkbey ,&nbsp;Joel T. Moncur","doi":"10.1016/j.jpi.2024.100381","DOIUrl":"10.1016/j.jpi.2024.100381","url":null,"abstract":"<div><p>The Gleason score is an important predictor of prognosis in prostate cancer. However, its subjective nature can result in over- or under-grading. Our objective was to train an artificial intelligence (AI)-based algorithm to grade prostate cancer in specimens from patients who underwent radical prostatectomy (RP) and to assess the correlation of AI-estimated proportions of different Gleason patterns with biochemical recurrence-free survival (RFS), metastasis-free survival (MFS), and overall survival (OS). Training and validation of algorithms for cancer detection and grading were completed with three large datasets containing a total of 580 whole-mount prostate slides from 191 RP patients at two centers and 6218 annotated needle biopsy slides from the publicly available Prostate Cancer Grading Assessment dataset. A cancer detection model was trained using MobileNetV3 on 0.5 mm × 0.5 mm cancer areas (tiles) captured at 10× magnification. For cancer grading, a Gleason pattern detector was trained on tiles using a ResNet50 convolutional neural network and a selective CutMix training strategy involving a mixture of real and artificial examples. This strategy resulted in improved model generalizability in the test set compared with three different control experiments when evaluated on both needle biopsy slides and whole-mount prostate slides from different centers. In an additional test cohort of RP patients who were clinically followed over 30 years, quantitative Gleason pattern AI estimates achieved concordance indexes of 0.69, 0.72, and 0.64 for predicting RFS, MFS, and OS times, outperforming the control experiments and International Society of Urological Pathology system (ISUP) grading by pathologists. Finally, unsupervised clustering of test RP patient specimens into low-, medium-, and high-risk groups based on AI-estimated proportions of each Gleason pattern resulted in significantly improved RFS and MFS stratification compared with ISUP grading. In summary, deep learning-based quantitative Gleason scoring using a selective CutMix training strategy may improve prognostication after prostate cancer surgery.</p></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"15 ","pages":"Article 100381"},"PeriodicalIF":0.0,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2153353924000208/pdfft?md5=3e256673a8f52212fb05001cc4e50f5b&pid=1-s2.0-S2153353924000208-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141042139","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":"Validation and three years of clinical experience in using an artificial intelligence algorithm as a second read system for prostate cancer diagnosis—real-world experience","authors":"Juan Carlos Santa-Rosario,&nbsp;Erik A. Gustafson,&nbsp;Dario E. Sanabria Bellassai,&nbsp;Phillip E. Gustafson,&nbsp;Mariano de Socarraz","doi":"10.1016/j.jpi.2024.100378","DOIUrl":"https://doi.org/10.1016/j.jpi.2024.100378","url":null,"abstract":"<div><h3>Background</h3><p>Prostate cancer ranks as the most frequently diagnosed cancer in men in the USA, with significant mortality rates. Early detection is pivotal for optimal patient outcomes, providing increased treatment options and potentially less invasive interventions. There remain significant challenges in prostate cancer histopathology, including the potential for missed diagnoses due to pathologist variability and subjective interpretations.</p></div><div><h3>Methods</h3><p>To address these challenges, this study investigates the ability of artificial intelligence (AI) to enhance diagnostic accuracy. The Galen™ Prostate AI algorithm was validated on a cohort of Puerto Rican men to demonstrate its efficacy in cancer detection and Gleason grading. Subsequently, the AI algorithm was integrated into routine clinical practice during a 3-year period at a CLIA certified precision pathology laboratory.</p></div><div><h3>Results</h3><p>The Galen™ Prostate AI algorithm showed a 96.7% (95% CI 95.6–97.8) specificity and a 96.6% (95% CI 93.3–98.8) sensitivity for prostate cancer detection and 82.1% specificity (95% CI 73.9–88.5) and 81.1% sensitivity (95% CI 73.7–87.2) for distinction of Gleason Grade Group 1 from Grade Group 2+. The subsequent AI integration into routine clinical use examined prostate cancer diagnoses on &gt;122,000 slides and 9200 cases over 3 years and had an overall AI Impact ™ factor of 1.8%.</p></div><div><h3>Conclusions</h3><p>The potential of AI to be a powerful, reliable, and effective diagnostic tool for pathologists is highlighted, while the AI Impact™ in a real-world setting demonstrates the ability of AI to standardize prostate cancer diagnosis at a high level of performance across pathologists.</p></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"15 ","pages":"Article 100378"},"PeriodicalIF":0.0,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2153353924000178/pdfft?md5=9c3ac8cee3a0de7c2dbd11239bf2bccb&pid=1-s2.0-S2153353924000178-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141164206","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":"Pathologists light level preferences using the microscope—study to guide digital pathology display use","authors":"Charlotte Jennings ,&nbsp;Darren Treanor ,&nbsp;David Brettle","doi":"10.1016/j.jpi.2024.100379","DOIUrl":"https://doi.org/10.1016/j.jpi.2024.100379","url":null,"abstract":"<div><h3>Background</h3><p>Currently, there is a paucity of guidelines relating to displays used for digital pathology making procurement decisions, and optimal display configuration, challenging.</p><p>Experience suggests pathologists have personal preferences for brightness when using a conventional microscope which we hypothesized could be used as a predictor for display setup.</p></div><div><h3>Methods</h3><p>We conducted an online survey across six NHS hospitals, totalling 108 practicing pathologists, to capture brightness adjustment habits on both microscopes and displays.</p><p>A convenience subsample of respondents was then invited to take part in a practical task to determine microscope brightness and display luminance preferences in the normal working environment. A novel adaptation for a lightmeter was developed to directly measure the light output from the microscope eyepiece.</p></div><div><h3>Results</h3><p>The survey (response rate 59% <em>n</em>=64) indicates 81% of respondents adjust the brightness on their microscope. In comparison, only 11% report adjusting their digital display. Display adjustments were more likely to be for visual comfort and ambient light compensation rather than for tissue factors, common for microscope adjustments. Part of this discrepancy relates to lack of knowledge of how to adjust displays and lack of guidance on whether this is safe; But, 66% felt that the ability to adjust the light on the display was important.</p><p>Twenty consultants took part in the practical brightness assessment. Light preferences on the microscope showed no correlation with display preferences, except where a pathologist has a markedly brighter microscope light preference. All of the preferences in this cohort were for a display luminance of &lt;500 cd/m², with 90% preferring 350 cd/m² or less. There was no correlation between these preferences and the ambient lighting in the room.</p></div><div><h3>Conclusions</h3><p>We conclude that microscope preferences can only be used to predict display luminance requirements where the microscope is being used at very high brightness levels. A display capable of a brightness of 500 cd/m² should be suitable for almost all pathologists with 300 cd/m² suitable for the majority. Although display luminance is not frequently changed by users, the ability to do so was felt to be important by the majority of respondents.</p><p>Further work needs to be undertaken to establish the relationship between diagnostic performance, luminance preferences, and ambient lighting levels.</p></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"15 ","pages":"Article 100379"},"PeriodicalIF":0.0,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S215335392400018X/pdfft?md5=0134b221667c45b419ce808d463b9b22&pid=1-s2.0-S215335392400018X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141164205","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":"AI-algorithm training and validation for identification of endometrial CD138+ cells in infertility-associated conditions; polycystic ovary syndrome (PCOS) and recurrent implantation failure (RIF)","authors":"Seungbaek Lee ,&nbsp;Riikka K. Arffman ,&nbsp;Elina K. Komsi ,&nbsp;Outi Lindgren ,&nbsp;Janette A. Kemppainen ,&nbsp;Hanna Metsola ,&nbsp;Henna-Riikka Rossi ,&nbsp;Anne Ahtikoski ,&nbsp;Keiu Kask ,&nbsp;Merli Saare ,&nbsp;Andres Salumets ,&nbsp;Terhi T. Piltonen","doi":"10.1016/j.jpi.2024.100380","DOIUrl":"https://doi.org/10.1016/j.jpi.2024.100380","url":null,"abstract":"<div><h3>Background</h3><p>Endometrial CD138+ plasma cells serve as a diagnostic biomarker for endometrial inflammation, and their elevated occurrence correlates positively with adverse pregnancy outcomes. Infertility-related conditions like polycystic ovary syndrome (PCOS) and recurrent implantation failure (RIF) are closely associated with systemic and local chronic inflammatory status, wherein endometrial CD138+ plasma cell accumulation could also contribute to endometrial pathology. Current methods for quantifying CD138+ cells typically involve laborious and time-consuming microscopic assessments of only a few random areas from a slide. These methods have limitations in accurately representing the entire slide and are susceptible to significant biases arising from intra- and interobserver variations. Implementing artificial intelligence (AI) for CD138+ cell identification could enhance the accuracy, reproducibility, and reliability of analysis.</p></div><div><h3>Methods</h3><p>Here, an AI algorithm was developed to identify CD138+ plasma cells within endometrial tissue. The AI model comprised two layers of convolutional neural networks (CNNs). CNN1 was trained to segment epithelium and stroma across 28,363 mm² (2.56 mm² of epithelium and 24.87 mm² of stroma), while CNN2 was trained to distinguish stromal cells based on CD138 staining, encompassing 7345 cells in the object layers (6942 CD138− cells and 403 CD138+ cells). The training and performance of the AI model were validated by three experienced pathologists. We collected 193 endometrial tissues from healthy controls (<em>n</em> = 73), women with PCOS (<em>n</em> = 91), and RIF patients (<em>n</em> = 29) and compared the CD138+ cell percentages based on cycle phases, ovulation status, and endometrial receptivity utilizing the AI model.</p></div><div><h3>Results</h3><p>The AI algorithm consistently and reliably distinguished CD138− and CD138+ cells, with total error rates of 6.32% and 3.23%, respectively. During the training validation, there was a complete agreement between the decisions made by the pathologists and the AI algorithm, while the performance validation demonstrated excellent accuracy between the AI and human evaluation methods (intraclass correlation; 0.76, 95% confidence intervals; 0.36–0.93, <em>p</em> = 0.002) and a positive correlation (Spearman's rank correlation coefficient: 0.79, <em>p</em> &lt; 0.01). In the AI analysis, the AI model revealed higher CD138+ cell percentages in the proliferative phase (PE) endometrium compared to the secretory phase or anovulatory PCOS endometrium, irrespective of PCOS diagnosis. Interestingly, CD138+ percentages differed according to PCOS phenotype in the PE (<em>p</em> = 0.03). On the other hand, the receptivity status had no impact on the cell percentages in RIF samples.</p></div><div><h3>Conclusion</h3><p>Our findings emphasize the potential and accuracy of the AI algorithm in detecting endometrial ","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"15 ","pages":"Article 100380"},"PeriodicalIF":0.0,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2153353924000191/pdfft?md5=e87bcb34b2122d5de74031fb378126b2&pid=1-s2.0-S2153353924000191-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141084391","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":"Understanding the financial aspects of digital pathology: A dynamic customizable return on investment calculator for informed decision-making","authors":"Orly Ardon ,&nbsp;Sylvia L. Asa ,&nbsp;Mark C. Lloyd ,&nbsp;Giovanni Lujan ,&nbsp;Anil Parwani ,&nbsp;Juan C. Santa-Rosario ,&nbsp;Bryan Van Meter ,&nbsp;Jennifer Samboy ,&nbsp;Danielle Pirain ,&nbsp;Scott Blakely ,&nbsp;Matthew G. Hanna","doi":"10.1016/j.jpi.2024.100376","DOIUrl":"https://doi.org/10.1016/j.jpi.2024.100376","url":null,"abstract":"<div><h3>Background</h3><p>The adoption of digital pathology has transformed the field of pathology, however, the economic impact and cost analysis of implementing digital pathology solutions remain a critical consideration for institutions to justify. Digital pathology implementation requires a thorough evaluation of associated costs and should identify and optimize resource allocation to facilitate informed decision-making. A dynamic cost calculator to estimate the financial implications of deploying digital pathology systems was needed to estimate the financial effects on transitioning to a digital workflow.</p></div><div><h3>Methods</h3><p>A systematic approach was used to comprehensively assess the various components involved in implementing and maintaining a digital pathology system. This consisted of: (1) identification of key cost categories associated with digital pathology implementation; (2) data collection and analysis of cost estimation; (3) cost categorization and quantification of direct and indirect costs associated with different use cases, allowing customization of each factor based on specific intended uses and market rates, industry standards, and regional variations; (4) opportunities for savings realized by digitization of glass slides and (5) integration of the cost calculator into a unified framework for a holistic view of the financial implications associated with digital pathology implementation. The online tool enables the user to test various scenarios specific to their institution and provides adjustable parameters to assure organization specific relatability.</p></div><div><h3>Results</h3><p>The Digital Pathology Association has developed a web-based calculator as a companion tool to provide an exhaustive list of the necessary concepts needed when assessing the financial implications of transitioning to a digital pathology system. The dynamic return on investment (ROI) calculator successfully integrated relevant cost and cost-saving components associated with digital pathology implementation and maintenance. Considerations include factors such as digital pathology infrastructure, clinical operations, staffing, hardware and software, information technology, archive and retrieval, medical–legal, and potential reimbursements. The ROI calculator developed for digital pathology workflows offers a comprehensive, customizable tool for institutions to assess their anticipated upfront and ongoing annual costs as they start or expand their digital pathology journey. It also offers cost-savings analysis based on specific user case volume, institutional geographic considerations, and actual costs. In addition, the calculator also serves as a tool to estimate number of required whole slide scanners, scanner throughput, and data storage (TB). This tool is intended to estimate the potential costs and cost savings resulting from the transition to digital pathology for business plan justifications and return on investment calculation","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"15 ","pages":"Article 100376"},"PeriodicalIF":0.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2153353924000154/pdfft?md5=06d1c9d9fe955e29390f2e8c6402cf66&pid=1-s2.0-S2153353924000154-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140825437","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":"Evaluation of the precision and accuracy in the classification of breast histopathology images using the MobileNetV3 model","authors":"Kenneth DeVoe ,&nbsp;Gary Takahashi ,&nbsp;Ebrahim Tarshizi ,&nbsp;Allan Sacker","doi":"10.1016/j.jpi.2024.100377","DOIUrl":"https://doi.org/10.1016/j.jpi.2024.100377","url":null,"abstract":"<div><p>Accurate surgical pathological assessment of breast biopsies is essential to the proper management of breast lesions. Identifying histological features, such as nuclear pleomorphism, increased mitotic activity, cellular atypia, patterns of architectural disruption, as well as invasion through basement membranes into surrounding stroma and normal structures, including invasion of vascular and lymphatic spaces, help to classify lesions as malignant. This visual assessment is repeated on numerous slides taken at various sections through the resected tumor, each at different magnifications. Computer vision models have been proposed to assist human pathologists in classification tasks such as these. Using MobileNetV3, a convolutional architecture designed to achieve high accuracy with a compact parameter footprint, we attempted to classify breast cancer images in the BreakHis_v1 breast pathology dataset to determine the performance of this model out-of-the-box. Using transfer learning to take advantage of ImageNet embeddings without special feature extraction, we were able to correctly classify histopathology images broadly as benign or malignant with 0.98 precision, 0.97 recall, and an F1 score of 0.98. The ability to classify into histological subcategories was varied, with the greatest success being with classifying ductal carcinoma (accuracy 0.95), and the lowest success being with lobular carcinoma (accuracy 0.59). Multiclass ROC assessment of performance as a multiclass classifier yielded AUC values ≥0.97 in both benign and malignant subsets. In comparison with previous efforts, using older and larger convolutional network architectures with feature extraction pre-processing, our work highlights that modern, resource-efficient architectures can classify histopathological images with accuracy that at least matches that of previous efforts, without the need for labor-intensive feature extraction protocols. Suggestions to further refine the model are discussed.</p></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"15 ","pages":"Article 100377"},"PeriodicalIF":0.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2153353924000166/pdfft?md5=3267cff7f293c28129100035b0de0ddd&pid=1-s2.0-S2153353924000166-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140646007","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":"On image search in histopathology","authors":"H.R. Tizhoosh ,&nbsp;Liron Pantanowitz","doi":"10.1016/j.jpi.2024.100375","DOIUrl":"https://doi.org/10.1016/j.jpi.2024.100375","url":null,"abstract":"<div><p>Pathology images of histopathology can be acquired from camera-mounted microscopes or whole-slide scanners. Utilizing similarity calculations to match patients based on these images holds significant potential in research and clinical contexts. Recent advancements in search technologies allow for implicit quantification of tissue morphology across diverse primary sites, facilitating comparisons, and enabling inferences about diagnosis, and potentially prognosis, and predictions for new patients when compared against a curated database of diagnosed and treated cases. In this article, we comprehensively review the latest developments in image search technologies for histopathology, offering a concise overview tailored for computational pathology researchers seeking effective, fast, and efficient image search methods in their work.</p></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"15 ","pages":"Article 100375"},"PeriodicalIF":0.0,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2153353924000142/pdfft?md5=77ba5ac2a9061caa47f0ffdca4b5d013&pid=1-s2.0-S2153353924000142-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140554425","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":"Number of intraepithelial lymphocytes and presence of a subepithelial band in normal colonic mucosa differs according to stainings and evaluation method","authors":"Anne-Marie Kanstrup Fiehn ,&nbsp;Peter Johan Heiberg Engel ,&nbsp;Ulla Engel ,&nbsp;Dea Natalie Munch Jepsen ,&nbsp;Thomas Blixt ,&nbsp;Julie Rasmussen ,&nbsp;Signe Wildt ,&nbsp;Wojciech Cebula ,&nbsp;Andreea-Raluca Diac ,&nbsp;Lars Kristian Munck","doi":"10.1016/j.jpi.2024.100374","DOIUrl":"https://doi.org/10.1016/j.jpi.2024.100374","url":null,"abstract":"<div><p>Chronic watery diarrhea is a frequent symptom. In approximately 10% of the patients, a diagnosis of microscopic colitis (MC) is established. The diagnosis relies on specific, but sometimes subtle, histopathological findings. As the histology of normal intestinal mucosa vary, discriminating subtle features of MC from normal tissue can be challenging and therefore auxiliary stainings are increasingly used. The aim of this study was to determine the variance in number of intraepithelial lymphocytes (IELs) and presence of a subepithelial band in normal ileum and colonic mucosa, according to different stains and digital assessment. Sixty-one patients without diarrhea referred to screening colonoscopy due to a positive feacal blood test and presenting with endoscopically normal mucosa were included. Basic histological features, number of IELs, and thickness of a subepithelial band was manually evaluated and a deep learning-based algorithm was developed to digitally determine the number of IELs in each of the two compartments; surface epithelium and cryptal epithelium, and the density of lymphocytes in the lamina propria compartment. The number of IELs was significantly higher on CD3-stained slides compared with slides stained with Hematoxylin-and-Eosin (HE) (<em>p</em>&lt;0.001), and even higher numbers were reached using digital analysis. No significant difference between right and left colon in IELs or density of CD3-positive lymphocytes in lamina propria was found. No subepithelial band was present in HE-stained slides while a thin band was visualized on special stains. Conclusively, in this cohort of prospectively collected ileum and colonic biopsies from asymptomatic patients, the range of IELs and detection of a subepithelial collagenous band varied depending on the stain and method used for assessment. As assessment of biopsies from patients with diarrhea constitute a considerable workload in the pathology departments digital image analysis is highly desired. Knowledge provided by the present study highlight important differences that should be considered before introducing this method in the clinic.</p></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"15 ","pages":"Article 100374"},"PeriodicalIF":0.0,"publicationDate":"2024-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2153353924000130/pdfft?md5=e89d0ed8c314678bb6d962000d68d253&pid=1-s2.0-S2153353924000130-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140341660","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":"Microcomputed tomography as a diagnostic tool for detection of lymph node metastasis in non-small cell lung cancer: A decision-support approach for pathological examination “A pilot study for method validation”","authors":"Ayten Kayı Cangır ,&nbsp;Süleyman Gökalp Güneş ,&nbsp;Kaan Orhan ,&nbsp;Hilal Özakıncı ,&nbsp;Yusuf Kahya ,&nbsp;Duru Karasoy ,&nbsp;Serpil Dizbay Sak","doi":"10.1016/j.jpi.2024.100373","DOIUrl":"10.1016/j.jpi.2024.100373","url":null,"abstract":"<div><h3>Background</h3><p>Non-small cell lung cancer (NSCLC) patients without lymph node (LN) metastases (pN0) may exhibit different survival rates, even when their T stage is similar. This divergence could be attributed to the current pathology practice, wherein LNs are examined solely in two-dimensional (2D). Unfortunately, adhering to the protocols of 2D pathological examination does not ensure the exhaustive sampling of all excised LNs, thereby leaving room for undetected metastatic foci in the unexplored depths of tissues. The employment of micro-computed tomography (micro-CT) facilitates a three-dimensional (3D) evaluation of all LNs without compromising sample integrity. In our study, we utilized quantitative micro-CT parameters to appraise the metastatic status of formalin-fixed paraffin-embedded (FFPE) LNs.</p></div><div><h3>Methods</h3><p>Micro-CT scans were conducted on 12 FFPEs obtained from 8 NSCLC patients with histologically confirmed mediastinal LN metastases. Simultaneously, whole-slide images from these FFPEs underwent scanning, and 47 regions of interest (ROIs) (17 metastatic foci, 11 normal lymphoid tissues, 10 adipose tissues, and 9 anthracofibrosis) were marked on scanned images. Quantitative structural variables obtained via micro-CT analysis from tumoral and non-tumoral ROIs, were analyzed.</p></div><div><h3>Result</h3><p>Significant distinctions were observed in linear density, connectivity, connectivity density, and closed porosity between tumoral and non-tumoral ROIs, as indicated by kappa coefficients of 1, 0.90, 1, and 1, respectively. Receiver operating characteristic analysis substantiated the differentiation between tumoral and non-tumoral ROIs based on thickness, linear density, connectivity, connectivity density, and the percentage of closed porosity.</p></div><div><h3>Conclusions</h3><p>Quantitative micro-CT parameters demonstrate the ability to distinguish between tumoral and non-tumoral regions of LNs in FFPEs. The discriminatory characteristics of these quantitative micro-CT parameters imply their potential usefulness in developing an artificial intelligence algorithm specifically designed for the 3D identification of LN metastases while preserving the FFPE tissue.</p></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"15 ","pages":"Article 100373"},"PeriodicalIF":0.0,"publicationDate":"2024-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2153353924000129/pdfft?md5=fda55927dc961cda1f292ed5e05fb3e2&pid=1-s2.0-S2153353924000129-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140406150","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}