Journal of Pathology Informatics最新文献

{"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}

{"title":"Artificial intelligence (AI) for tumor microenvironment (TME) and tumor budding (TB) identification in colorectal cancer (CRC) patients: A systematic review","authors":"Olga Andreevna Lobanova ,&nbsp;Anastasia Olegovna Kolesnikova ,&nbsp;Valeria Aleksandrovna Ponomareva ,&nbsp;Ksenia Andreevna Vekhova ,&nbsp;Anaida Lusparonovna Shaginyan ,&nbsp;Alisa Borisovna Semenova ,&nbsp;Dmitry Petrovich Nekhoroshkov ,&nbsp;Svetlana Evgenievna Kochetkova ,&nbsp;Natalia Valeryevna Kretova ,&nbsp;Alexander Sergeevich Zanozin ,&nbsp;Maria Alekseevna Peshkova ,&nbsp;Natalia Borisovna Serezhnikova ,&nbsp;Nikolay Vladimirovich Zharkov ,&nbsp;Evgeniya Altarovna Kogan ,&nbsp;Alexander Alekseevich Biryukov ,&nbsp;Ekaterina Evgenievna Rudenko ,&nbsp;Tatiana Alexandrovna Demura","doi":"10.1016/j.jpi.2023.100353","DOIUrl":"https://doi.org/10.1016/j.jpi.2023.100353","url":null,"abstract":"<div><p>Evaluation of the parameters such as tumor microenvironment (TME) and tumor budding (TB) is one of the most important steps in colorectal cancer (CRC) diagnosis and cancer development prognosis. In recent years, artificial intelligence (AI) has been successfully used to solve such problems. In this paper, we summarize the latest data on the use of artificial intelligence to predict tumor microenvironment and tumor budding in histological scans of patients with colorectal cancer. We performed a systematic literature search using 2 databases (Medline and Scopus) with the following search terms: (\"tumor microenvironment\" OR \"tumor budding\") AND (\"colorectal cancer\" OR CRC) AND (\"artificial intelligence\" OR \"machine learning \" OR \"deep learning\"). During the analysis, we gathered from the articles performance scores such as sensitivity, specificity, and accuracy of identifying TME and TB using artificial intelligence. The systematic review showed that machine learning and deep learning successfully cope with the prediction of these parameters. The highest accuracy values in TB and TME prediction were 97.7% and 97.3%, respectively. This review led us to the conclusion that AI platforms can already be used as diagnostic aids, which will greatly facilitate the work of pathologists in detection and estimation of TB and TME as instruments and second-opinion services. A key limitation in writing this systematic review was the heterogeneous use of performance metrics for machine learning models by different authors, as well as relatively small datasets used in some studies.</p></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"15 ","pages":"Article 100353"},"PeriodicalIF":0.0,"publicationDate":"2023-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2153353923001670/pdfft?md5=02bdc3221d561cd21e9de3f4dfe5954d&pid=1-s2.0-S2153353923001670-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139038548","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 deep learning model to predict Ki-67 positivity in oral squamous cell carcinoma","authors":"Francesco Martino ,&nbsp;Gennaro Ilardi ,&nbsp;Silvia Varricchio ,&nbsp;Daniela Russo ,&nbsp;Rosa Maria Di Crescenzo ,&nbsp;Stefania Staibano ,&nbsp;Francesco Merolla","doi":"10.1016/j.jpi.2023.100354","DOIUrl":"https://doi.org/10.1016/j.jpi.2023.100354","url":null,"abstract":"<div><p>Anatomical pathology is undergoing its third revolution, transitioning from analogical to digital pathology and incorporating new artificial intelligence technologies into clinical practice. Aside from classification, detection, and segmentation models, predictive models are gaining traction since they can impact diagnostic processes and laboratory activity, lowering consumable usage and turnaround time. Our research aimed to create a deep-learning model to generate synthetic Ki-67 immunohistochemistry from Haematoxylin and Eosin (H&amp;E) stained images. We used 175 oral squamous cell carcinoma (OSCC) from the University Federico II’s Pathology Unit’s archives to train our model to generate 4 Tissue Micro Arrays (TMAs). We sectioned one slide from each TMA, first stained with H&amp;E and then re-stained with anti-Ki-67 immunohistochemistry (IHC). In digitised slides, cores were disarrayed, and the matching cores of the 2 stained were aligned to construct a dataset to train a Pix2Pix algorithm to convert H&amp;E images to IHC. Pathologists could recognise the synthetic images in only half of the cases in a specially designed likelihood test. Hence, our model produced realistic synthetic images. We next used QuPath to quantify IHC positivity, achieving remarkable levels of agreement between genuine and synthetic IHC.</p><p>Furthermore, a categorical analysis employing 3 Ki-67 positivity cut-offs (5%, 10%, and 15%) revealed high positive-predictive values. Our model is a promising tool for collecting Ki-67 positivity information directly on H&amp;E slides, reducing laboratory demand and improving patient management. It is also a valuable option for smaller laboratories to easily and quickly screen bioptic samples and prioritise them in a digital pathology workflow.</p></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"15 ","pages":"Article 100354"},"PeriodicalIF":0.0,"publicationDate":"2023-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2153353923001682/pdfft?md5=1e50c2def78451a443dc9e3c3d022cbc&pid=1-s2.0-S2153353923001682-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138558826","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":"Digital analysis of the prostate tumor microenvironment with high-order chromogenic multiplexing","authors":"Rahul Rajendran,&nbsp;Rachel C. Beck,&nbsp;Morteza M. Waskasi,&nbsp;Brian D. Kelly,&nbsp;Daniel R. Bauer","doi":"10.1016/j.jpi.2023.100352","DOIUrl":"https://doi.org/10.1016/j.jpi.2023.100352","url":null,"abstract":"<div><p>As our understanding of the tumor microenvironment grows, the pathology field is increasingly utilizing multianalyte diagnostic assays to understand important characteristics of tumor growth. In clinical settings, brightfield chromogenic assays represent the gold-standard and have developed significant trust as the first-line diagnostic method. However, conventional brightfield tests have been limited to low-order assays that are visually interrogated. We have developed a hybrid method of brightfield chromogenic multiplexing that overcomes these limitations and enables high-order multiplex assays. However, how compatible high-order brightfield multiplexed images are with advanced analytical algorithms has not been extensively evaluated. In the present study, we address this gap by developing a novel 6-marker prostate cancer assay that targets diverse aspects of the tumor microenvironment such as prostate-specific biomarkers (PSMA and p504s), immune biomarkers (CD8 and PD-L1), a prognostic biomarker (Ki-67), as well as an adjunctive diagnostic biomarker (basal cell cocktail) and apply the assay to 143 differentially graded adenocarcinoma prostate tissues. The tissues were then imaged on our spectroscopic multiplexing imaging platform and mined for proteomic and spatial features that were correlated with cancer presence and disease grade. Extracted features were used to train a UMAP model that differentiated healthy from cancerous tissue with an accuracy of 89% and identified clusters of cells based on cancer grade. For spatial analysis, cell-to-cell distances were calculated for all biomarkers and differences between healthy and adenocarcinoma tissues were studied. We report that p504s positive cells were at least 2× closer to cells expressing PD-L1, CD8, Ki-67, and basal cell in adenocarcinoma tissues relative to the healthy control tissues. These findings offer a powerful insight to understand the fingerprint of the prostate tumor microenvironment and indicate that high-order chromogenic multiplexing is compatible with digital analysis. Thus, the presented chromogenic multiplexing system combines the clinical applicability of brightfield assays with the emerging diagnostic power of high-order multiplexing in a digital pathology friendly format that is well-suited for translational studies to better understand mechanisms of tumor development and growth.</p></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"15 ","pages":"Article 100352"},"PeriodicalIF":0.0,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2153353923001669/pdfft?md5=0e916a6f16983a6fd85779bc435e915c&pid=1-s2.0-S2153353923001669-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138633481","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":"Mathematical modelling and deep learning algorithms to automate assessment of single and digitally multiplexed immunohistochemical stains in tumoural stroma","authors":"Liam Burrows ,&nbsp;Declan Sculthorpe ,&nbsp;Hongrun Zhang ,&nbsp;Obaid Rehman ,&nbsp;Abhik Mukherjee ,&nbsp;Ke Chen","doi":"10.1016/j.jpi.2023.100351","DOIUrl":"https://doi.org/10.1016/j.jpi.2023.100351","url":null,"abstract":"<div><p>Whilst automated analysis of immunostains in pathology research has focused predominantly on the epithelial compartment, automated analysis of stains in the stromal compartment is challenging and therefore requires time-consuming pathological input and guidance to adjust to tissue morphometry as perceived by pathologists. This study aimed to develop a robust method to automate stromal stain analyses using 2 of the commonest stromal stains (SMA and desmin) employed in clinical pathology practice as examples. An effective computational method capable of automatically assessing and quantifying tumour-associated stromal stains was developed and applied on cores of colorectal cancer tissue microarrays. The methodology combines both mathematical models and deep learning techniques with the former requiring no training data and the latter as many inputs as possible. The novel mathematical model was used to produce a digital double marker overlay allowing for fast automated digital multiplex analysis of stromal stains. The results show that deep learning methodologies in combination with mathematical modelling allow for an accurate means of quantifying stromal stains whilst also opening up new possibilities of digital multiplex analyses.</p></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"15 ","pages":"Article 100351"},"PeriodicalIF":0.0,"publicationDate":"2023-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2153353923001657/pdfft?md5=482b41c3d6029ab0f2c8de25168258df&pid=1-s2.0-S2153353923001657-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138633482","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":"Whole slide images as non-fungible tokens: A decentralized approach to secure, scalable data storage and access","authors":"Arlen Brickman ,&nbsp;Yigit Baykara ,&nbsp;Miguel Carabaño ,&nbsp;Sean M. Hacking","doi":"10.1016/j.jpi.2023.100350","DOIUrl":"10.1016/j.jpi.2023.100350","url":null,"abstract":"<div><h3>Background</h3><p>Distributed ledger technology (DLT) enables the creation of tamper-resistant, decentralized, and secure digital ledgers. A non-fungible token (NFT) represents a record on-chain associated with a digital or physical asset, such as a whole-slide image (WSI). The InterPlanetary File System (IPFS) represents an off-chain network, hypermedia, and file sharing peer-to-peer protocol for storing and sharing data in a distributed file system. Today, we need cheaper, more efficient, highly scalable, and transparent solutions for WSI data storage and access of medical records and medical imaging data.</p></div><div><h3>Methods</h3><p>WSIs were created from non-human tissues and H&amp;E-stained sections were scanned on a Philips Ultrafast WSI scanner at 40× magnification objective lens (1 μm/pixel). TIFF images were stored on IPFS, while NFTs were minted on the Ethereum blockchain network in ERC-1155 standard. WSI-NFTs were stored on MetaMask and OpenSea was used to display the WSI-NFT collection. Filebase storage application programing interface (API) were used to create dedicated gateways and content delivery networks (CDN).</p></div><div><h3>Results</h3><p>A total of 10 WSI-NFTs were minted on the Ethereum blockchain network, found on our collection “Whole Slide Images as Non-fungible Tokens Project” on Open Sea: <span>https://opensea.io/collection/untitled-collection-126765644</span><svg><path></path></svg>. WSI TIFF files ranged in size from 1.6 to 2.2 GB and were stored on IPFS and pinned on 3 separate nodes. Under optimal conditions, and using a dedicated CDN, WSI reached retrieved at speeds of over 10 mb/s, however, download speeds and WSI retrieval times varied significantly depending on the file and gateway used. Overall, the public IPFS gateway resulted in variably poorer WSI download retrieval performance compared to gateways provided by Filebase storage API.</p></div><div><h3>Conclusion</h3><p>Whole-slide images, as the most complex and substantial data files in healthcare, demand innovative solutions. In this technical report, we identify pitfalls in IPFS, and demonstrate proof-of-concept using a 3-layer architecture for scalable, decentralized storage, and access. Optimized through dedicated gateways and CDNs, which can be effectively applied to all medical data and imaging modalities across the healthcare sector. DLT and off-chain network solutions present numerous opportunities for advancements in clinical care, education, and research. Such approaches uphold the principles of equitable healthcare data ownership, security, and democratization, and are poised to drive significant innovation.</p></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"15 ","pages":"Article 100350"},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2153353923001645/pdfft?md5=4164e105de7f34e0cc4b4035d1797cb6&pid=1-s2.0-S2153353923001645-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135565552","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":"Cognitive factors impacting patient understanding of laboratory test information","authors":"Edward C. Klatt","doi":"10.1016/j.jpi.2023.100349","DOIUrl":"10.1016/j.jpi.2023.100349","url":null,"abstract":"<div><p>Laboratory testing can provide information useful to promote patient health literacy and ultimately patient well-being. The human state of mind involves not only cognition but also emotion and motivation factors when receiving, processing, and acting upon information. The cognitive load for patients acquiring and processing new information is high. Modes of distribution can affect both attention to and receipt of information. Implicit unconscious biases can affect whom and what patients believe. Positive wording and framing of information with salience for patients can evoke positive emotions. Providing patients with the gist, or essential meaning, of information can positively influence decision-making. What laboratorians provide as information helps to combat mis- and disinformation. Laboratorians can actively participate in measures to improve the patient experience in health care by developing and contributing to high-quality information to enable timely, meaningful communication and interpretation of test results.</p></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"15 ","pages":"Article 100349"},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2153353923001633/pdfft?md5=00b861355861322a8aaa479615ff15fe&pid=1-s2.0-S2153353923001633-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135515811","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}