{"id":1828,"date":"2023-08-31T13:46:21","date_gmt":"2023-08-31T13:46:21","guid":{"rendered":"https:\/\/www.lunit.io\/publication\/an-automated-clinical-workflow-integrating-an-ai-driven-pd-l1-22c3-scoring-algorithm-into-a-browser-based-digital-pathology-solution\/"},"modified":"2025-11-01T05:24:49","modified_gmt":"2025-11-01T05:24:49","slug":"an-automated-clinical-workflow-integrating-an-ai-driven-pd-l1-22c3-scoring-algorithm-into-a-browser-based-digital-pathology-solution","status":"publish","type":"publication","link":"https:\/\/www.lunit.io\/en\/publication\/an-automated-clinical-workflow-integrating-an-ai-driven-pd-l1-22c3-scoring-algorithm-into-a-browser-based-digital-pathology-solution\/","title":{"rendered":"An Automated Clinical Workflow Integrating an AI-Driven PD-L1 22C3 Scoring Algorithm Into a Browser-Based Digital Pathology Solution"},"content":{"rendered":"<h3>An Automated Clinical Workflow Integrating an AI-Driven PD-L1 22C3 Scoring Algorithm Into a Browser-Based Digital Pathology Solution<\/h3>\n<p>Michael J. Cascio, Wei Song, Lauren Lawrence, Wei-Zhong He, Kyunghyun Paeng, Jeongseok Kang, Jongseok Choi, Sooick Cho, Dylan Cairns, Hallie Rane, Eric Runde<\/p>\n<p><strong>CAP, 2023<\/strong><\/p>\n<p><strong>Context:<\/strong> Quantitative image analysis of histopathologic tissue sections provides a promising approach for advancing precision medicine in clinical practice. However, there is limited literature on integrating digital pathology\u2013based artificial intelligence algorithms and workflows in clinical practice. Here, we demonstrate a real-world integration for Lunit SCOPE PD-L1 (Lunit, Inc, Seoul, South Korea) for non-small cell lung carcinoma (NSCLC) currently deployed for clinical use at Guardant Health.<\/p>\n<p><strong>Design:<\/strong> A 3-way integration was built by implementing Lunit SCOPE PD-L1, an AI-powered PD-L1 scoring algorithm, in HALO AP, a browser-based image management system (IMS) (Indica Labs, Albuquerque, New Mexico), which was integrated with the Guardant Health (Redwood City, California) laboratory information management system (LIMS).<\/p>\n<p><strong>Results:<\/strong> PD-L1 22C3 immunohistochemistry is performed on NSCLC tissue sections and whole slide image (WSI) files are generated using a Leica Aperio GT450 whole slide scanner (Leica Biosystems). After WSIs are received by the LIMS and available in the IMS, WSIs are automatically transferred to a cloud instance for AI analysis by Lunit SCOPE PD-L1. Within minutes, QC information, heat maps, and tumor proportion scores are returned to the IMS, and a scoring report is prefilled. Pathologists perform review of the NSCLC WSIs and algorithm results within the IMS, and either approve the AI-generated results or reflex to manual scoring. After result approval, tumor proportion scores and heat maps are automatically returned to the LIMS for clinical report generation and release.<\/p>\n<p><strong>Conclusions:<\/strong> Automated clinical workflows integrating AI algorithms with IMS and LIMS can be used in clinical practice to simplify and strengthen pathology workflows.<\/p>\n<p style=\"text-align: center;\"><a href=\"https:\/\/meridian.allenpress.com\/aplm\/article\/147\/9\/e2\/495512\/Abstracts-and-Case-Studies-From-the-College-of\"><strong>View abstract<\/strong><\/a><\/p>\n","protected":false},"featured_media":0,"template":"","publication-oncology":[95,78,135,70,77,93],"publication-region":[],"publication-type":[],"radiology":[],"class_list":["post-1828","publication","type-publication","status-publish","hentry","publication-oncology-conference-posters","publication-oncology-lung-cancer","publication-oncology-lunit-scope-pd-l1","publication-oncology-product","publication-oncology-tumor-type","publication-oncology-type-of-evidence"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.7 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>An Automated Clinical Workflow Integrating an AI-Driven PD-L1 22C3 Scoring Algorithm Into a Browser-Based Digital Pathology Solution - Lunit<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.lunit.io\/en\/publication\/an-automated-clinical-workflow-integrating-an-ai-driven-pd-l1-22c3-scoring-algorithm-into-a-browser-based-digital-pathology-solution\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"An Automated Clinical Workflow Integrating an AI-Driven PD-L1 22C3 Scoring Algorithm Into a Browser-Based Digital Pathology Solution - Lunit\" \/>\n<meta property=\"og:description\" content=\"An Automated Clinical Workflow Integrating an AI-Driven PD-L1 22C3 Scoring Algorithm Into a Browser-Based Digital Pathology Solution Michael J. Cascio, Wei Song, Lauren Lawrence, Wei-Zhong He, Kyunghyun Paeng, Jeongseok Kang, Jongseok Choi, Sooick Cho, Dylan Cairns, Hallie Rane, Eric Runde CAP, 2023 Context: Quantitative image analysis of histopathologic tissue sections provides a promising approach for [&hellip;]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.lunit.io\/en\/publication\/an-automated-clinical-workflow-integrating-an-ai-driven-pd-l1-22c3-scoring-algorithm-into-a-browser-based-digital-pathology-solution\/\" \/>\n<meta property=\"og:site_name\" content=\"Lunit\" \/>\n<meta property=\"article:modified_time\" content=\"2025-11-01T05:24:49+00:00\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:site\" content=\"@lunit_ai\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"2 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/www.lunit.io\\\/en\\\/publication\\\/an-automated-clinical-workflow-integrating-an-ai-driven-pd-l1-22c3-scoring-algorithm-into-a-browser-based-digital-pathology-solution\\\/\",\"url\":\"https:\\\/\\\/www.lunit.io\\\/en\\\/publication\\\/an-automated-clinical-workflow-integrating-an-ai-driven-pd-l1-22c3-scoring-algorithm-into-a-browser-based-digital-pathology-solution\\\/\",\"name\":\"An Automated Clinical Workflow Integrating an AI-Driven PD-L1 22C3 Scoring Algorithm Into a Browser-Based Digital Pathology Solution - 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