Journal of Pathology Informatics最新文献

{"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":"186 3","pages":""},"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":"187 2","pages":""},"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":"28 22","pages":""},"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}

{"title":"Publicly available datasets of breast histopathology H&E whole-slide images: A scoping review","authors":"Masoud Tafavvoghi ,&nbsp;Lars Ailo Bongo ,&nbsp;Nikita Shvetsov ,&nbsp;Lill-Tove Rasmussen Busund ,&nbsp;Kajsa Møllersen","doi":"10.1016/j.jpi.2024.100363","DOIUrl":"10.1016/j.jpi.2024.100363","url":null,"abstract":"<div><p>Advancements in digital pathology and computing resources have made a significant impact in the field of computational pathology for breast cancer diagnosis and treatment. However, access to high-quality labeled histopathological images of breast cancer is a big challenge that limits the development of accurate and robust deep learning models. In this scoping review, we identified the publicly available datasets of breast H&amp;E-stained whole-slide images (WSIs) that can be used to develop deep learning algorithms. We systematically searched 9 scientific literature databases and 9 research data repositories and found 17 publicly available datasets containing 10 385 H&amp;E WSIs of breast cancer. Moreover, we reported image metadata and characteristics for each dataset to assist researchers in selecting proper datasets for specific tasks in breast cancer computational pathology. In addition, we compiled 2 lists of breast H&amp;E patches and private datasets as supplementary resources for researchers. Notably, only 28% of the included articles utilized multiple datasets, and only 14% used an external validation set, suggesting that the performance of other developed models may be susceptible to overestimation. The TCGA-BRCA was used in 52% of the selected studies. This dataset has a considerable selection bias that can impact the robustness and generalizability of the trained algorithms. There is also a lack of consistent metadata reporting of breast WSI datasets that can be an issue in developing accurate deep learning models, indicating the necessity of establishing explicit guidelines for documenting breast WSI dataset characteristics and metadata.</p></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"15 ","pages":"Article 100363"},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2153353924000026/pdfft?md5=e1d6b199f5ede66427075250c84de4c0&pid=1-s2.0-S2153353924000026-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139824478","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 pathology: A survey review and the way forward","authors":"Mahdi S. Hosseini ,&nbsp;Babak Ehteshami Bejnordi ,&nbsp;Vincent Quoc-Huy Trinh ,&nbsp;Lyndon Chan ,&nbsp;Danial Hasan ,&nbsp;Xingwen Li ,&nbsp;Stephen Yang ,&nbsp;Taehyo Kim ,&nbsp;Haochen Zhang ,&nbsp;Theodore Wu ,&nbsp;Kajanan Chinniah ,&nbsp;Sina Maghsoudlou ,&nbsp;Ryan Zhang ,&nbsp;Jiadai Zhu ,&nbsp;Samir Khaki ,&nbsp;Andrei Buin ,&nbsp;Fatemeh Chaji ,&nbsp;Ala Salehi ,&nbsp;Bich Ngoc Nguyen ,&nbsp;Dimitris Samaras ,&nbsp;Konstantinos N. Plataniotis","doi":"10.1016/j.jpi.2023.100357","DOIUrl":"10.1016/j.jpi.2023.100357","url":null,"abstract":"<div><p>Computational Pathology (CPath) is an interdisciplinary science that augments developments of computational approaches to analyze and model medical histopathology images. The main objective for CPath is to develop infrastructure and workflows of digital diagnostics as an assistive CAD system for clinical pathology, facilitating transformational changes in the diagnosis and treatment of cancer that are mainly address by CPath tools. With evergrowing developments in deep learning and computer vision algorithms, and the ease of the data flow from digital pathology, currently CPath is witnessing a paradigm shift. Despite the sheer volume of engineering and scientific works being introduced for cancer image analysis, there is still a considerable gap of adopting and integrating these algorithms in clinical practice. This raises a significant question regarding the direction and trends that are undertaken in CPath. In this article we provide a comprehensive review of more than 800 papers to address the challenges faced in problem design all-the-way to the application and implementation viewpoints. We have catalogued each paper into a model-card by examining the key works and challenges faced to layout the current landscape in CPath. We hope this helps the community to locate relevant works and facilitate understanding of the field’s future directions. In a nutshell, we oversee the CPath developments in cycle of stages which are required to be cohesively linked together to address the challenges associated with such multidisciplinary science. We overview this cycle from different perspectives of data-centric, model-centric, and application-centric problems. We finally sketch remaining challenges and provide directions for future technical developments and clinical integration of CPath. For updated information on this survey review paper and accessing to the original model cards repository, please refer to <span>GitHub</span><svg><path></path></svg>. Updated version of this draft can also be found from <span>arXiv</span><svg><path></path></svg>.</p></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"15 ","pages":"Article 100357"},"PeriodicalIF":0.0,"publicationDate":"2024-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2153353923001712/pdfft?md5=cc2f8380838ba30f7db624e4fbf72b6e&pid=1-s2.0-S2153353923001712-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139538505","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":"Use of n-grams and K-means clustering to classify data from free text bone marrow reports","authors":"Richard F. Xiang","doi":"10.1016/j.jpi.2023.100358","DOIUrl":"10.1016/j.jpi.2023.100358","url":null,"abstract":"<div><p>Natural language processing (NLP) has been used to extract information from and summarize medical reports. Currently, the most advanced NLP models require large training datasets of accurately labeled medical text. An approach to creating these large datasets is to use low resource intensive classical NLP algorithms. In this manuscript, we examined how an automated classical NLP algorithm was able to classify portions of bone marrow report text into their appropriate sections. A total of 1480 bone marrow reports were extracted from the laboratory information system of a tertiary healthcare network. The free text of these bone marrow reports were preprocessed by separating the reports into text blocks and then removing the section headers. A natural language processing algorithm involving n-grams and K-means clustering was used to classify the text blocks into their appropriate bone marrow sections. The impact of token replacement of numerical values, accession numbers, and clusters of differentiation, varying the number of centroids (1–19) and n-grams (1–5), and utilizing an ensemble algorithm were assessed. The optimal NLP model was found to employ an ensemble algorithm that incorporated token replacement, utilized 1-gram or bag of words, and 10 centroids for K-means clustering. This optimal model was able to classify text blocks with an accuracy of 89%, suggesting that classical NLP models can accurately classify portions of marrow report text.</p></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"15 ","pages":"Article 100358"},"PeriodicalIF":0.0,"publicationDate":"2024-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2153353923001724/pdfft?md5=b7e487105251a8e09617df3f8efc1607&pid=1-s2.0-S2153353923001724-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139395227","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":"Artificial intelligence for human gunshot wound classification","authors":"Jerome Cheng ,&nbsp;Carl Schmidt ,&nbsp;Allecia Wilson ,&nbsp;Zixi Wang ,&nbsp;Wei Hao ,&nbsp;Joshua Pantanowitz ,&nbsp;Catherine Morris ,&nbsp;Randy Tashjian ,&nbsp;Liron Pantanowitz","doi":"10.1016/j.jpi.2023.100361","DOIUrl":"10.1016/j.jpi.2023.100361","url":null,"abstract":"<div><p>Certain features are helpful in the identification of gunshot entrance and exit wounds, such as the presence of muzzle imprints, peripheral tears, stippling, bone beveling, and wound border irregularity. Some cases are less straightforward and wounds can thus pose challenges to an emergency room doctor or forensic pathologist. In recent years, deep learning has shown promise in various automated medical image classification tasks.</p><p>This study explores the feasibility of using a deep learning model to classify entry and exit gunshot wounds in digital color images. A collection of 2418 images of entrance and exit gunshot wounds were procured. Of these, 2028 entrance and 1314 exit wounds were cropped, focusing on the area around each gunshot wound. A ConvNext Tiny deep learning model was trained using the Fastai deep learning library, with a train/validation split ratio of 70/30, until a maximum validation accuracy of 92.6% was achieved. An additional 415 entrance and 293 exit wound images were collected for the test (holdout) set. The model achieved an accuracy of 87.99%, precision of 83.99%, recall of 87.71%, and F1-score 85.81% on the holdout set. Correctly classified were 88.19% of entrance wounds and 87.71% of exit wounds. The results are comparable to what a forensic pathologist can achieve without other morphologic cues. This study represents one of the first applications of artificial intelligence to the field of forensic pathology. This work demonstrates that deep learning models can discern entrance and exit gunshot wounds in digital images with high accuracy.</p></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"15 ","pages":"Article 100361"},"PeriodicalIF":0.0,"publicationDate":"2023-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S215335392300175X/pdfft?md5=e69bbf6eb449bdb360e3bbcbd84c9a3a&pid=1-s2.0-S215335392300175X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139190131","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":"Multimodal Gated Mixture of Experts Using Whole Slide Image and Flow Cytometry for Multiple Instance Learning Classification of Lymphoma","authors":"Noriaki Hashimoto ,&nbsp;Hiroyuki Hanada ,&nbsp;Hiroaki Miyoshi ,&nbsp;Miharu Nagaishi ,&nbsp;Kensaku Sato ,&nbsp;Hidekata Hontani ,&nbsp;Koichi Ohshima ,&nbsp;Ichiro Takeuchi","doi":"10.1016/j.jpi.2023.100359","DOIUrl":"10.1016/j.jpi.2023.100359","url":null,"abstract":"<div><p>In this study, we present a deep-learning-based multimodal classification method for lymphoma diagnosis in digital pathology, which utilizes a whole slide image (WSI) as the primary image data and flow cytometry (FCM) data as auxiliary information. In pathological diagnosis of malignant lymphoma, FCM serves as valuable auxiliary information during the diagnosis process, offering useful insights into predicting the major class (superclass) of subtypes. By incorporating both images and FCM data into the classification process, we can develop a method that mimics the diagnostic process of pathologists, enhancing the explainability. In order to incorporate the hierarchical structure between superclasses and their subclasses, the proposed method utilizes a network structure that effectively combines the mixture of experts (MoE) and multiple instance learning (MIL) techniques, where MIL is widely recognized for its effectiveness in handling WSIs in digital pathology. The MoE network in the proposed method consists of a gating network for superclass classification and multiple expert networks for (sub)class classification, specialized for each superclass. To evaluate the effectiveness of our method, we conducted experiments involving a six-class classification task using 600 lymphoma cases. The proposed method achieved a classification accuracy of 72.3%, surpassing the 69.5% obtained through the straightforward combination of FCM and images, as well as the 70.2% achieved by the method using only images. Moreover, the combination of multiple weights in the MoE and MIL allows for the visualization of specific cellular and tumor regions, resulting in a highly explanatory model that cannot be attained with conventional methods. It is anticipated that by targeting a larger number of classes and increasing the number of expert networks, the proposed method could be effectively applied to the real problem of lymphoma diagnosis.</p></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"15 ","pages":"Article 100359"},"PeriodicalIF":0.0,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2153353923001736/pdfft?md5=7da710e168eb41e1143a4b0663efcd4f&pid=1-s2.0-S2153353923001736-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139190722","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":"Combining a deep learning model with clinical data better predicts hepatocellular carcinoma behavior following surgery","authors":"Benoit Schmauch ,&nbsp;Sarah S. Elsoukkary ,&nbsp;Amika Moro ,&nbsp;Roma Raj ,&nbsp;Chase J. Wehrle ,&nbsp;Kazunari Sasaki ,&nbsp;Julien Calderaro ,&nbsp;Patrick Sin-Chan ,&nbsp;Federico Aucejo ,&nbsp;Daniel E. Roberts","doi":"10.1016/j.jpi.2023.100360","DOIUrl":"10.1016/j.jpi.2023.100360","url":null,"abstract":"<div><p>Hepatocellular carcinoma (HCC) is among the most common cancers worldwide, and tumor recurrence following liver resection or transplantation is one of the highest contributors to mortality in HCC patients after surgery. Using artificial intelligence (AI), we developed an interdisciplinary model to predict HCC recurrence and patient survival following surgery. We collected whole-slide H&amp;E images, clinical variables, and follow-up data from 300 patients with HCC who underwent transplant and 169 patients who underwent resection at the Cleveland Clinic. A deep learning model was trained to predict recurrence-free survival (RFS) and disease-specific survival (DSS) from the H&amp;E-stained slides. Repeated cross-validation splits were used to compute robust C-index estimates, and the results were compared to those obtained by fitting a Cox proportional hazard model using only clinical variables. While the deep learning model alone was predictive of recurrence and survival among patients in both cohorts, integrating the clinical and histologic models significantly increased the C-index in each cohort. In every subgroup analyzed, we found that a combined clinical and deep learning model better predicted post-surgical outcome in HCC patients compared to either approach independently.</p></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"15 ","pages":"Article 100360"},"PeriodicalIF":0.0,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2153353923001748/pdfft?md5=765c0e6b2719108fb46126309088e40a&pid=1-s2.0-S2153353923001748-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139191922","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":"SlideTiler: A dataset creator software for boosting deep learning on histological whole slide images","authors":"Leonardo Barcellona ,&nbsp;Lorenzo Nicolè ,&nbsp;Rocco Cappellesso ,&nbsp;Angelo Paolo Dei Tos ,&nbsp;Stefano Ghidoni","doi":"10.1016/j.jpi.2023.100356","DOIUrl":"10.1016/j.jpi.2023.100356","url":null,"abstract":"<div><p>The introduction of deep learning caused a significant breakthrough in digital pathology. Thanks to its capability of mining hidden data patterns in digitised histological slides to resolve diagnostic tasks and extract prognostic and predictive information. However, the high performance achieved in classification tasks depends on the availability of large datasets, whose collection and preprocessing are still time-consuming processes. Therefore, strategies to make these steps more efficient are worth investigation. This work introduces SlideTiler, an open-source software with a user-friendly graphical interface. SlideTiler can manage several image preprocessing phases through an intuitive workflow that does not require specific coding skills. The software was designed to provide direct access to virtual slides, allowing custom tiling of specific regions of interest drawn by the user, tile labelling, quality assessment, and direct export to dataset directories. To illustrate the functions and the scalability of SlideTiler, a deep learning-based classifier was implemented to classify 4 different tumour histotypes available in the TCGA repository. The results demonstrate the effectiveness of SlideTiler in facilitating data preprocessing and promoting accessibility to digitised pathology images for research purposes. Considering the increasing interest in deep learning applications of digital pathology, SlideTiler has a positive impact on this field. Moreover, SlideTiler has been conceived as a dynamic tool in constant evolution, and more updated and efficient versions will be released in the future.</p></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"15 ","pages":"Article 100356"},"PeriodicalIF":0.0,"publicationDate":"2023-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2153353923001700/pdfft?md5=8704f1d3116c95cedb709a6224e1022e&pid=1-s2.0-S2153353923001700-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138624638","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}