Journal of Pathology Informatics最新文献

{"title":"Quantitative digital pathology enables automated and quantitative assessment of inflammatory activity in patients with autoimmune hepatitis","authors":"Piotr Socha ,&nbsp;Elizabeth Shumbayawonda ,&nbsp;Abhishek Roy ,&nbsp;Caitlin Langford ,&nbsp;Paul Aljabar ,&nbsp;Malgorzata Wozniak ,&nbsp;Sylwia Chełstowska ,&nbsp;Elzbieta Jurkiewicz ,&nbsp;Rajarshi Banerjee ,&nbsp;Ken Fleming ,&nbsp;Maciej Pronicki ,&nbsp;Kamil Janowski ,&nbsp;Wieslawa Grajkowska","doi":"10.1016/j.jpi.2024.100372","DOIUrl":"https://doi.org/10.1016/j.jpi.2024.100372","url":null,"abstract":"<div><h3>Background</h3><p>Chronic liver disease diagnoses depend on liver biopsy histopathological assessment. However, due to the limitations associated with biopsy, there is growing interest in the use of quantitative digital pathology to support pathologists. We evaluated the performance of computational algorithms in the assessment of hepatic inflammation in an autoimmune hepatitis in which inflammation is a major component.</p></div><div><h3>Methods</h3><p>Whole-slide digital image analysis was used to quantitatively characterize the area of tissue covered by inflammation [Inflammation Density (ID)] and number of inflammatory foci per unit area [Focal Density (FD)] on tissue obtained from 50 patients with autoimmune hepatitis undergoing routine liver biopsy. Correlations between digital pathology outputs and traditional categorical histology scores, biochemical, and imaging markers were assessed. The ability of ID and FD to stratify between low-moderate (both portal and lobular inflammation ≤1) and moderate-severe disease activity was estimated using the area under the receiver operating characteristic curve (AUC).</p></div><div><h3>Results</h3><p>ID and FD scores increased significantly and linearly with both portal and lobular inflammation grading. Both ID and FD correlated moderately-to-strongly and significantly with histology (portal and lobular inflammation; 0.36≤R≤0.69) and biochemical markers (ALT, AST, GGT, IgG, and gamma globulins; 0.43≤R≤0.57). ID (AUC: 0.85) and FD (AUC: 0.79) had good performance for stratifying between low-moderate and moderate-severe inflammation.</p></div><div><h3>Conclusion</h3><p>Quantitative assessment of liver biopsy using quantitative digital pathology metrics correlates well with traditional pathology scores and key biochemical markers. Whole-slide quantification of disease can support stratification and identification of patients with more advanced inflammatory disease activity.</p></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"15 ","pages":"Article 100372"},"PeriodicalIF":0.0,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2153353924000117/pdfft?md5=506527e4685e9ff0ebf86f565be2119c&pid=1-s2.0-S2153353924000117-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140138142","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":"Crossing the Andes: Challenges and opportunities for digital pathology in Latin America","authors":"Renata A. Coudry ,&nbsp;Emilio A.C.P. Assis ,&nbsp;Fernando Pereira Frassetto ,&nbsp;Angela Marie Jansen ,&nbsp;Leonard Medeiros da Silva ,&nbsp;Rafael Parra-Medina ,&nbsp;Mauro Saieg","doi":"10.1016/j.jpi.2024.100369","DOIUrl":"10.1016/j.jpi.2024.100369","url":null,"abstract":"<div><p>The most widely accepted and used type of digital pathology (DP) is whole-slide imaging (WSI). The USFDA granted two WSI system approvals for primary diagnosis, the first in 2017. In Latin America, DP has the potential to reshape healthcare by enhancing diagnostic capabilities through artificial intelligence (AI) and standardizing pathology reports. Yet, we must tackle regulatory hurdles, training, resource availability, and unique challenges to the region. Collectively addressing these hurdles can enable the region to harness DP’s advantages—enhancing disease diagnosis, medical research, and healthcare accessibility for its population. Americas Health Foundation assembled a panel of Latin American pathologists who are experts in DP to assess the hurdles to implementing it into pathologists’ workflows in the region and provide recommendations for overcoming them. Some key steps recommended include creating a Latin American Society of Digital Pathology to provide continuing education, developing AI models trained on the Latin American population, establishing national regulatory frameworks for protecting the data, and standardizing formats for DP images to ensure that pathologists can collaborate and validate specimens across the various DP platforms.</p></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"15 ","pages":"Article 100369"},"PeriodicalIF":0.0,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2153353924000087/pdfft?md5=2e2463cde6a90ee91b313f6ab548c3e4&pid=1-s2.0-S2153353924000087-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140463674","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":"ML-CKDP: Machine learning-based chronic kidney disease prediction with smart web application","authors":"Rajib Kumar Halder ,&nbsp;Mohammed Nasir Uddin ,&nbsp;Md. Ashraf Uddin ,&nbsp;Sunil Aryal ,&nbsp;Sajeeb Saha ,&nbsp;Rakib Hossen ,&nbsp;Sabbir Ahmed ,&nbsp;Mohammad Abu Tareq Rony ,&nbsp;Mosammat Farida Akter","doi":"10.1016/j.jpi.2024.100371","DOIUrl":"https://doi.org/10.1016/j.jpi.2024.100371","url":null,"abstract":"<div><p>Chronic kidney diseases (CKDs) are a significant public health issue with potential for severe complications such as hypertension, anemia, and renal failure. Timely diagnosis is crucial for effective management. Leveraging machine learning within healthcare offers promising advancements in predictive diagnostics. In this paper, we developed a machine learning-based kidney diseases prediction (ML‐CKDP) model with dual objectives: to enhance dataset preprocessing for CKD classification and to develop a web-based application for CKD prediction. The proposed model involves a comprehensive data preprocessing protocol, converting categorical variables to numerical values, imputing missing data, and normalizing via Min-Max scaling. Feature selection is executed using a variety of techniques including Correlation, Chi-Square, Variance Threshold, Recursive Feature Elimination, Sequential Forward Selection, Lasso Regression, and Ridge Regression to refine the datasets. The model employs seven classifiers: Random Forest (RF), AdaBoost (AdaB), Gradient Boosting (GB), XgBoost (XgB), Naive Bayes (NB), Support Vector Machine (SVM), and Decision Tree (DT), to predict CKDs. The effectiveness of the models is assessed by measuring their accuracy, analyzing confusion matrix statistics, and calculating the Area Under the Curve (AUC) specifically for the classification of positive cases. Random Forest (RF) and AdaBoost (AdaB) achieve a 100% accuracy rate, evident across various validation methods including data splits of 70:30, 80:20, and K-Fold set to 10 and 15. RF and AdaB consistently reach perfect AUC scores of 100% across multiple datasets, under different splitting ratios. Moreover, Naive Bayes (NB) stands out for its efficiency, recording the lowest training and testing times across all datasets and split ratios. Additionally, we present a real-time web-based application to operationalize the model, enhancing accessibility for healthcare practitioners and stakeholders.</p><p>Web app link: <span>https://rajib-research-kedney-diseases-prediction.onrender.com/</span><svg><path></path></svg></p></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"15 ","pages":"Article 100371"},"PeriodicalIF":0.0,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2153353924000105/pdfft?md5=ed85d934a771241ed567b9de62993e5f&pid=1-s2.0-S2153353924000105-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140112920","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":"BBDash: An Electron-based tool for analyzing blood product utilization","authors":"Jacob Spector ,&nbsp;Adrienne Kennedy ,&nbsp;Elena Nedelcu","doi":"10.1016/j.jpi.2024.100370","DOIUrl":"https://doi.org/10.1016/j.jpi.2024.100370","url":null,"abstract":"<div><p>Blood transfusions can be associated with side effects ranging from occasional febrile reactions to extremely rare fatal reactions. Monitoring blood product orders and ensuring appropriate utilization is therefore an important strategy to ensure patient safety. However, data extracted from laboratory information systems can be difficult to interpret. We created BBDash, an Electron-based tool that reads Sunquest reports to create easy-to-interpret graphs related to blood product utilization.</p></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"15 ","pages":"Article 100370"},"PeriodicalIF":0.0,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2153353924000099/pdfft?md5=262632943c1f336057625f96685997e0&pid=1-s2.0-S2153353924000099-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140138143","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":"Utility of artificial intelligence in a binary classification of soft tissue tumors","authors":"Jing Di ,&nbsp;Caylin Hickey ,&nbsp;Cody Bumgardner ,&nbsp;Mustafa Yousif ,&nbsp;Mauricio Zapata ,&nbsp;Therese Bocklage ,&nbsp;Bonnie Balzer ,&nbsp;Marilyn M. Bui ,&nbsp;Jerad M. Gardner ,&nbsp;Liron Pantanowitz ,&nbsp;Shadi A. Qasem","doi":"10.1016/j.jpi.2024.100368","DOIUrl":"10.1016/j.jpi.2024.100368","url":null,"abstract":"<div><p>Soft tissue tumors (STTs) pose diagnostic and therapeutic challenges due to their rarity, complexity, and morphological overlap. Accurate differentiation between benign and malignant STTs is important to set treatment directions, however, this task can be difficult. The integration of machine learning and artificial intelligence (AI) models can potentially be helpful in classifying these tumors. The aim of this study was to investigate AI and machine learning tools in the classification of STT into benign and malignant categories. This study consisted of three components: (1) Evaluation of whole-slide images (WSIs) to classify STT into benign and malignant entities. Five specialized soft tissue pathologists from different medical centers independently reviewed 100 WSIs, representing 100 different cases, with limited clinical information and no additional workup. The results showed an overall concordance rate of 70.4% compared to the reference diagnosis. (2) Identification of cell-specific parameters that can distinguish benign and malignant STT. Using an image analysis software (QuPath) and a cohort of 95 cases, several cell-specific parameters were found to be statistically significant, most notably cell count, nucleus/cell area ratio, nucleus hematoxylin density mean, and cell max caliper. (3) Evaluation of machine learning library (Scikit-learn) in differentiating benign and malignant STTs. A total of 195 STT cases (156 cases in the training group and 39 cases in the validation group) achieved approximately 70% sensitivity and specificity, and an AUC of 0.68. Our limited study suggests that the use of WSI and AI in soft tissue pathology has the potential to enhance diagnostic accuracy and identify parameters that can differentiate between benign and malignant STTs. We envision the integration of AI as a supportive tool to augment the pathologists' diagnostic capabilities.</p></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"15 ","pages":"Article 100368"},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2153353924000075/pdfft?md5=13c7bafd86f326dc7203d6c0381703ee&pid=1-s2.0-S2153353924000075-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139830649","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":"Computational methods for metastasis detection in lymph nodes and characterization of the metastasis-free lymph node microarchitecture: A systematic-narrative hybrid review","authors":"Elzbieta Budginaite ,&nbsp;Derek R. Magee ,&nbsp;Maximilian Kloft ,&nbsp;Henry C. Woodruff ,&nbsp;Heike I. Grabsch","doi":"10.1016/j.jpi.2024.100367","DOIUrl":"10.1016/j.jpi.2024.100367","url":null,"abstract":"<div><h3>Background</h3><p>Histological examination of tumor draining lymph nodes (LNs) plays a vital role in cancer staging and prognostication. However, as soon as a LN is classed as metastasis-free, no further investigation will be performed and thus, potentially clinically relevant information detectable in tumor-free LNs is currently not captured.</p></div><div><h3>Objective</h3><p>To systematically study and critically assess methods for the analysis of digitized histological LN images described in published research.</p></div><div><h3>Methods</h3><p>A systematic search was conducted in several public databases up to December 2023 using relevant search terms. Studies using brightfield light microscopy images of hematoxylin and eosin or immunohistochemically stained LN tissue sections aiming to detect and/or segment LNs, their compartments or metastatic tumor using artificial intelligence (AI) were included. Dataset, AI methodology, cancer type, and study objective were compared between articles.</p></div><div><h3>Results</h3><p>A total of 7201 articles were collected and 73 articles remained for detailed analyses after article screening. Of the remaining articles, 86% aimed at LN metastasis identification, 8% aimed at LN compartment segmentation, and remaining focused on LN contouring. Furthermore, 78% of articles used patch classification and 22% used pixel segmentation models for analyses. Five out of six studies (83%) of metastasis-free LNs were performed on publicly unavailable datasets, making quantitative article comparison impossible.</p></div><div><h3>Conclusions</h3><p>Multi-scale models mimicking multiple microscopy zooms show promise for computational LN analysis. Large-scale datasets are needed to establish the clinical relevance of analyzing metastasis-free LN in detail. Further research is needed to identify clinically interpretable metrics for LN compartment characterization.</p></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"15 ","pages":"Article 100367"},"PeriodicalIF":0.0,"publicationDate":"2024-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2153353924000063/pdfft?md5=d843e63269692c17cbcf96f14d687dad&pid=1-s2.0-S2153353924000063-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139872873","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":"External validation of a deep learning-based algorithm for detection of tall cells in papillary thyroid carcinoma: A multicenter study","authors":"Sebastian Stenman, Sylvain Bétrisey, Paula Vainio, Jutta Huvila, M. Lundin, N. Linder, Anja Schmitt, Aurel Perren, Matthias S. Dettmer, Caj Haglund, Johanna Arola, Johan Lundin","doi":"10.1016/j.jpi.2024.100366","DOIUrl":"https://doi.org/10.1016/j.jpi.2024.100366","url":null,"abstract":"","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"36 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139883234","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":"Utility of artificial intelligence in a binary classification of soft tissue tumors","authors":"Jing Di, Caylin Hickey, Cody Bumgardner, Mustafa Yousif, Mauricio Zapata, Therese Bocklage, Bonnie Balzer, Marilyn M. Bui, Jerad M. Gardner, Liron Pantanowitz, Shadi A. Qasem","doi":"10.1016/j.jpi.2024.100368","DOIUrl":"https://doi.org/10.1016/j.jpi.2024.100368","url":null,"abstract":"","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"34 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139890687","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":"Dynamic changes in AI-based analysis of endometrial cellular composition: Analysis of PCOS and RIF endometrium","authors":"Seungbaek Lee ,&nbsp;Riikka K. Arffman ,&nbsp;Elina K. Komsi ,&nbsp;Outi Lindgren ,&nbsp;Janette Kemppainen ,&nbsp;Keiu Kask ,&nbsp;Merli Saare ,&nbsp;Andres Salumets ,&nbsp;Terhi T. Piltonen","doi":"10.1016/j.jpi.2024.100364","DOIUrl":"10.1016/j.jpi.2024.100364","url":null,"abstract":"<div><h3>Background</h3><p>The human endometrium undergoes a monthly cycle of tissue growth and degeneration. During the mid-secretory phase, the endometrium establishes an optimal niche for embryo implantation by regulating cellular composition (e.g., epithelial and stromal cells) and differentiation. Impaired endometrial development observed in conditions such as polycystic ovary syndrome (PCOS) and recurrent implantation failure (RIF) contributes to infertility. Surprisingly, despite the importance of the endometrial lining properly developing prior to pregnancy, precise measures of endometrial cellular composition in these two infertility-associated conditions are entirely lacking. Additionally, current methods for measuring the epithelial and stromal area have limitations, including intra- and inter-observer variability and efficiency.</p></div><div><h3>Methods</h3><p>We utilized a deep-learning artificial intelligence (AI) model, created on a cloud-based platform and developed in our previous study. The AI model underwent training to segment both areas populated by epithelial and stromal endometrial cells. During the training step, a total of 28.36 mm2 areas were annotated, comprising 2.56 mm2 of epithelium and 24.87 mm2 of stroma. Two experienced pathologists validated the performance of the AI model. 73 endometrial samples from healthy control women were included in the sample set to establish cycle phase-dependent dynamics of the endometrial epithelial-to-stroma ratio from the proliferative (PE) to secretory (SE) phases. In addition, 91 samples from PCOS cases, accounting for the presence or absence of ovulation and representing all menstrual cycle phases, and 29 samples from RIF patients on day 5 after progesterone administration in the hormone replacement treatment cycle were also included and analyzed in terms of cellular composition.</p></div><div><h3>Results</h3><p>Our AI model exhibited reliable and reproducible performance in delineating epithelial and stromal compartments, achieving an accuracy of 92.40% and 99.23%, respectively. Moreover, the performance of the AI model was comparable to the pathologists’ assessment, with F1 scores exceeding 82% for the epithelium and &gt;96% for the stroma. Next, we compared the endometrial epithelial-to-stromal ratio during the menstrual cycle in women with PCOS and in relation to endometrial receptivity status in RIF patients. The ovulatory PCOS endometrium exhibited epithelial cell proportions similar to those of control and healthy women’s samples in every cycle phase, from the PE to the late SE, correlating with progesterone levels (control SE, r2 = 0.64, FDR &lt; 0.001; PCOS SE, r2 = 0.52, FDR &lt; 0.001). The mid-SE endometrium showed the highest epithelial percentage compared to both the early and late SE endometrium in both healthy women and PCOS patients. Anovulatory PCOS cases showed epithelial cellular fractions comparable to those of PCOS cases in the PE (Anovulatory, 14.54%; PCOS ","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"15 ","pages":"Article 100364"},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2153353924000038/pdfft?md5=2faed9504ba60ae597600f7fbdfcc1dc&pid=1-s2.0-S2153353924000038-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139875736","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":"External validation of a deep learning-based algorithm for detection of tall cells in papillary thyroid carcinoma: A multicenter study","authors":"Sebastian Stenman ,&nbsp;Sylvain Bétrisey ,&nbsp;Paula Vainio ,&nbsp;Jutta Huvila ,&nbsp;Mikael Lundin ,&nbsp;Nina Linder ,&nbsp;Anja Schmitt ,&nbsp;Aurel Perren ,&nbsp;Matthias S. Dettmer ,&nbsp;Caj Haglund ,&nbsp;Johanna Arola ,&nbsp;Johan Lundin","doi":"10.1016/j.jpi.2024.100366","DOIUrl":"10.1016/j.jpi.2024.100366","url":null,"abstract":"<div><p>The tall cell subtype (TC-PTC) is an aggressive subtype of papillary thyroid carcinoma (PTC). The TC-PTC is defined as a PTC comprising at least 30% epithelial cells that are three times as tall as they are wide. In practice, this definition is difficult to adhere to, resulting in high inter-observer variability. In this multicenter study, we validated a previously trained deep learning (DL)-based algorithm for detection of tall cells on 160 externally collected hematoxylin and eosin (HE)-stained PTC whole-slide images. In a test set of 360 manual annotations of regions of interest from 18 separate tissue sections in the external dataset, the DL-based algorithm detected TCs with a sensitivity of 90.6% and a specificity of 88.5%. The DL algorithm detected non-TC areas with a sensitivity of 81.6% and a specificity of 92.9%. In the validation datasets, 20% and 30% TC thresholds correlated with a significantly shorter relapse-free survival. In conclusion, the DL algorithm detected TCs in unseen, external scanned HE tissue slides with high sensitivity and specificity without any retraining.</p></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"15 ","pages":"Article 100366"},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2153353924000051/pdfft?md5=6dbf0e9a0907b0d17dbe4092a431a1f0&pid=1-s2.0-S2153353924000051-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139823622","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}