Lunit: Lunit to Present AI Study on EGFR Mutation Prediction in NSCLC at AACR 2025 in Collaboration with AstraZeneca

Lunit will present a deep learning study on Epidermal Growth Factor Receptor (EGFR) mutation prediction in patients with non-small cell lung cancer (NSCLC) 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&E) -stained tissue samples in NSCLC patients, will be presented in collaboration with AstraZeneca. EFR 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's collaborative study leveraged the large and most diverse training dataset to date, consisting of more than 12,000 pathology slides (>4,500 EGFR-mutated and >7,500 wild-type) from NSCLC patients across multiple countries, including the US, China, and South Korea, to further develop and validate Lunit's model. The AI model's 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. The collaboration focuses on the development of the Lunit SCOPEGenotype Predictor, anAI-driven tool designed to rapidly and cost-effectively predict NSCLC driver mutations from H&E-stained tissue samples. Lunit will present a deep learning study on Epidermal Growth Factor Receptor (EGFR) mutation prediction in patients with non-small cell lung cancer (NSCLC) 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&E) -stained tissue samples in NSCLC patients, will be presented in collaboration with AstraZeneca. EFR 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's collaborative study leveraged the large and most diverse training dataset to date, consisting of more than 12,000 pathology slides (>4,500 EGFR-mutated and >7,500 wild-type) from NSCLC patients across multiple countries, including the US, China, and South Korea, to further develop and validate Lunit's model. The AI model's 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. The collaboration focuses on the development of the Lunit SCOPEGenotype Predictor, anAI-driven tool designed to rapidly and cost-effectively predict NSCLC driver mutations from H&E-stained tissue samples.