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

Rhea-AI Summary

Lunit (KRX:328130.KQ) and AstraZeneca (AZN) will present a collaborative AI study at AACR 2025, showcasing the Lunit SCOPE Genotype Predictor. This deep learning model predicts EGFR mutations in non-small cell lung cancer (NSCLC) patients using H&E-stained tissue samples.

The study utilized the largest diverse training dataset to date, comprising over 12,000 pathology slides (>4,500 EGFR-mutated and >7,500 wild-type) from NSCLC patients across the US, China, and South Korea. The AI model demonstrated consistent performance across various clinical variables, including specimen types, EGFR mutation subtypes, slide scanners, and scan magnifications.

The technology aims to address current limitations in molecular testing accessibility for NSCLC patients, offering a rapid and cost-effective solution for predicting driver mutations. The presentation will take place at the AACR Annual Meeting from April 25-30, 2025, in Chicago.

Positive

• Partnership with major pharmaceutical company AstraZeneca enhances market credibility
• AI technology addresses significant market need in NSCLC molecular testing
• Large-scale validation using 12,000+ pathology slides demonstrates robust performance
• Cost-effective solution potential for healthcare providers

Negative

• Technology still pending regulatory approvals for clinical use
• Commercial success depends on healthcare provider adoption

AI-powered model predicts EGFR mutations from H&E-stained slides, demonstrating robust performance across diverse clinical settings and helping overcome barriers to molecular testing in NSCLC

SEOUL, South Korea, March 26, 2025 /PRNewswire/ -- Lunit (KRX:328130.KQ), a leading provider of AI-powered solutions for cancer diagnostics and therapeutics, 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 (LSE/STO/Nasdaq: AZN).

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's collaborative study leveraged the largest 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 SCOPE Genotype Predictor, an AI-driven tool designed to rapidly and cost-effectively predict NSCLC driver mutations from H&E-stained tissue samples.

"This study is a testament to the real-world potential of AI in precision oncology," said Brandon Suh, CEO of Lunit. "By 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."

Join Us at AACR 2025

Visit Lunit at AACR 2025 Booth #2843 to learn more about this study and how AI is advancing precision oncology and improving patient outcomes.

About Lunit

Founded in 2013, Lunit (KRX:328130.KQ) is a medical AI company on a mission to conquer cancer through AI. 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 medical institutions across 55+ countries. Lunit clinical studies have been published in top journals, including the Journal of Clinical Oncology and the Lancet Digital Health, 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 lunit.io.

View original content to download multimedia:https://www.prnewswire.com/news-releases/lunit-to-present-ai-study-on-egfr-mutation-prediction-in-nsclc-at-aacr-2025-in-collaboration-with-astrazeneca-302411752.html

SOURCE Lunit

FAQ

What is the purpose of Lunit and AstraZeneca's (AZN) AI collaboration for NSCLC patients?

The collaboration aims to predict EGFR mutations in NSCLC patients using AI analysis of H&E-stained tissue samples, making mutation testing more accessible and cost-effective.

How many pathology slides were used to train the Lunit SCOPE Genotype Predictor?

The AI model was trained on over 12,000 pathology slides, including >4,500 EGFR-mutated and >7,500 wild-type samples from multiple countries.

When and where will Lunit present their EGFR mutation prediction study with AstraZeneca (AZN)?

The study will be presented at the AACR Annual Meeting 2025, held from April 25 to 30 in Chicago, Illinois.

What clinical variables was the AI model tested across for NSCLC patients?

The model was tested across specimen types, EGFR mutation subtypes, slide scanners, and scan magnifications, showing consistent performance.