{"id":986,"date":"2025-03-26T00:00:00","date_gmt":"2025-03-25T15:00:00","guid":{"rendered":"https:\/\/www.lunit.io\/media-hub\/lunit-to-present-ai-study-on-egfr-mutation-prediction-in-nsclc-at-aacr-2025-in-collaboration-with-astrazeneca\/"},"modified":"2026-01-08T14:49:36","modified_gmt":"2026-01-08T05:49:36","slug":"lunit-to-present-ai-study-on-egfr-mutation-prediction-in-nsclc-at-aacr-2025-in-collaboration-with-astrazeneca","status":"publish","type":"media-hub","link":"https:\/\/lunit.supremeclients.com\/en\/media-hub\/lunit-to-present-ai-study-on-egfr-mutation-prediction-in-nsclc-at-aacr-2025-in-collaboration-with-astrazeneca\/","title":{"rendered":"Lunit to Present AI Study on EGFR Mutation Prediction in NSCLC at AACR 2025 in Collaboration with AstraZeneca"},"content":{"rendered":"<p><strong><em>AI-powered model predicts EGFR mutations from H&amp;E-stained slides, demonstrating robust performance across diverse clinical settings and helping overcome barriers to molecular testing in NSCLC<\/em><\/strong><\/p>\n<p>&nbsp;<\/p>\n<p><strong>SEOUL, South Korea, March 26, 2025<\/strong> \u2014 Lunit (KRX:328130.KQ), a leading provider of AI-powered solutions for cancer diagnostics and therapeutics, will present a <a href=\"https:\/\/www.abstractsonline.com\/pp8\/#!\/20273\/presentation\/5602\" target=\"_blank\" rel=\"noopener\">deep learning study on Epidermal Growth Factor Receptor (EGFR) mutation prediction in patients with non-small cell lung cancer (NSCLC)<\/a> at the upcoming American Association for Cancer Research (AACR) Annual Meeting 2025, held from April 25 to 30 in Chicago, Illinois. The study, which highlights the development and validation of the Lunit SCOPE Genotype Predictor, an AI-powered deep learning model capable of predicting EGFR mutations directly from hematoxylin and eosin (H&amp;E)-stained tissue samples in NSCLC patients, will be presented in collaboration with AstraZeneca (LSE\/STO\/Nasdaq: AZN).<\/p>\n<p>EGFR mutation testing plays a crucial role in determining the best course of treatment for patients with NSCLC, yet many patients remain untested due to logistical and resource constraints, despite guideline recommendations. Existing AI models designed to predict mutations from pathology images have faced limitations in real-world clinical applications due to limited training data and lack of validation. Lunit and AstraZeneca\u2019s collaborative study leveraged the largest and most diverse training dataset to date, consisting of more than 12,000 pathology slides (&gt;4,500 EGFR-mutated and &gt;7,500 wild-type) from NSCLC patients across multiple countries, including the US, China, and South Korea, to further develop and validate Lunit\u2019s model.<\/p>\n<p>The AI model\u2019s performance remained consistent across key clinical variables including specimen types, EGFR mutation subtypes, slide scanners, and scan magnifications, reinforcing its potential for real-world deployment in diverse clinical environments.<\/p>\n<p>The collaboration focuses on the development of the Lunit SCOPE Genotype Predictor, an AI-driven tool designed to rapidly and cost-effectively predict NSCLC driver mutations from H&amp;E-stained tissue samples.<\/p>\n<p>\u201cThis study is a testament to the real-world potential of AI in precision oncology,\u201d said Brandon Suh, CEO of Lunit. \u201cBy leveraging Lunit AI, we have demonstrated that routine pathology slides can serve as a powerful tool to predict EGFR mutations with high accuracy. This could help clinicians prioritize molecular testing for NSCLC patients, ensuring that patients receive targeted therapy without unnecessary delays. We are excited to showcase this breakthrough at AACR 2025, reinforcing our commitment to advancing AI-driven precision oncology in collaboration with AstraZeneca.\u201d<\/p>\n<p><strong>Join Us at AACR 2025<\/strong><\/p>\n<p>Visit Lunit at AACR 2025 <strong>Booth #2843<\/strong> to learn more about this study and how AI is advancing precision oncology and improving patient outcomes.<\/p>\n<p>&nbsp;<\/p>\n<p><strong>About Lunit<\/strong><\/p>\n<p>Founded in 2013, Lunit (KRX:328130.KQ) is a medical AI company on a mission to conquer cancer. Lunit harnesses AI-powered medical image analytics and biomarker analysis to ensure accurate diagnosis and optimal treatment for each cancer patient. The FDA-cleared Lunit INSIGHT suite for cancer screening serves over 4,800 hospitals and medical institutions across 55+ countries. Lunit clinical studies have been published in top journals, including the <em>Journal of Clinical Oncology<\/em> and the <em>Lancet Digital Health<\/em>, and presented at global conferences such as the ASCO and RSNA. Headquartered in Seoul, South Korea, with a network of offices worldwide, Lunit leads the global fight against cancer. Discover more at <a href=\"lunit.io\/en\" target=\"_blank\" rel=\"noopener\">lunit.io<\/a>.<\/p>\n","protected":false},"author":3,"featured_media":0,"parent":0,"template":"","media-hub-type":[35],"class_list":["post-986","media-hub","type-media-hub","status-publish","hentry","media-hub-type-precision-oncology"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.6 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Lunit to Present AI Study on EGFR Mutation Prediction in NSCLC at AACR 2025 in Collaboration with AstraZeneca - 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:\/\/lunit.supremeclients.com\/en\/media-hub\/lunit-to-present-ai-study-on-egfr-mutation-prediction-in-nsclc-at-aacr-2025-in-collaboration-with-astrazeneca\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Lunit to Present AI Study on EGFR Mutation Prediction in NSCLC at AACR 2025 in Collaboration with AstraZeneca - Lunit\" \/>\n<meta property=\"og:description\" content=\"AI-powered model predicts EGFR mutations from H&amp;E-stained slides, demonstrating robust performance across diverse clinical settings and helping overcome barriers to molecular testing in NSCLC &nbsp; 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