Journal of Pathology Informatics最新文献

{"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":null,"pages":null},"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":null,"pages":null},"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":"Pathology Visions 2023 Overview","authors":"","doi":"10.1016/j.jpi.2024.100362","DOIUrl":"https://doi.org/10.1016/j.jpi.2024.100362","url":null,"abstract":"","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","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":"","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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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}

{"title":"Publicly available datasets of breast histopathology H&E whole-slide images: A scoping review","authors":"M. Tafavvoghi, L. A. Bongo, N. Shvetsov, Lill-ToveRasmussen Busund, Kajsa Møllersen","doi":"10.1016/j.jpi.2024.100363","DOIUrl":"https://doi.org/10.1016/j.jpi.2024.100363","url":null,"abstract":"","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139884260","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":"Pathology Visions 2023 Overview","authors":"","doi":"10.1016/j.jpi.2024.100362","DOIUrl":"https://doi.org/10.1016/j.jpi.2024.100362","url":null,"abstract":"","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139892074","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, R. Arffman, E. Komsi, Outi Lindgren, J. Kemppainen, K. Kask, M. Saare, Andres Salumets, T. Piltonen","doi":"10.1016/j.jpi.2024.100364","DOIUrl":"https://doi.org/10.1016/j.jpi.2024.100364","url":null,"abstract":"","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139816298","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}