The study demonstrates the ability of Lunit SCOPE IO, to enable quantitative immune phenotyping from H&E stained slides as a broadly accessible biomarker for immunotherapy.
The study, conducted on a real-world multicenter cohort of 1,806 Immune Checkpoint Inhibitor (ICI)-treated patients across 27 tumor types, showcases a correlation between the Inflamed Immune Phenotype and positive ICI treatment responses. There is an unmet need for improved immunotherapy biomarkers, and this study highlights the importance of Lunit SCOPE IO's ability to quantify immune phenotype (IP) as Inflamed, Excluded, or Desert, purely from H&E whole slide images (WSIs).
Utilizing advanced machine learning (ML) models, Lunit SCOPE IO segments tissue into cancer area (CA) and cancer stroma (CS) within WSIs. The model also detects Tumor-Infiltrating Lymphocytes (TILs) using a cell detection model trained on over 17,000 WSIs spanning multiple solid tumor types.
Based on TIL density, the model classifies the tumor into one of three immune phenotypes: Inflamed (IIP; high TIL density within CA), Immune Excluded (IEP; TILs within CS but excluded from CA), and Immune Desert (IDP; low TIL density within both CA and CS).
In an independent real-world dataset of ICI-treated patients, Lunit SCOPE IO demonstrated predictive power for clinical outcomes, including objective response rates (ORR), progression-free survival (PFS), and overall survival (OS). In the study, IIP patients showed significantly favorable clinical outcomes post-ICI treatment. More favorable ORRs (26.3% vs 15.8%), prolonged PFS (5.3 vs. 3.1 months) and OS (25.3 vs. 13.6 months) were observed in IIP patients, irrespective of ICI regimen or programmed death-ligand 1 (PD-L1) expression. The dataset reflected global diversity, with data coming from
This study paves the way for more precise patient selection with a time-efficient and labor-efficient analysis at scale in immunotherapy.
'This study marks a major step towards better biomarkers for immunotherapy driven by AI, analyzing the tumor microenvironment to determine immune phenotype quantitatively and predict patient responses to ICI therapy,' said
Published in the JITC, the official journal of the
About
Founded in 2013,
As a medical AI company grounded on clinical evidence, our findings are presented in major peer-reviewed journals, such as the
After receiving FDA clearance and the CE Mark, our flagship Lunit INSIGHT suite is clinically used in approximately 3,000+ hospitals and medical institutions across 40+ countries.
About Lunit SCOPE
Lunit SCOPE is a suite of AI-powered software that analyzes tissue slide images for digital pathology and AI biomarker development, aiming to optimize workflow and facilitate more accurate and predictive clinical data for clinicians and researchers.
Lunit SCOPE platform offers multiple AI-powered tissue analysis products and assays that can streamline digital pathology workflow and diagnostics and enhance the drug development process.
Lunit SCOPE IO analyzes the tumor microenvironment (TME) based on H&E analysis and provides AI-based predictive clinical outcome information. In addition, AI-driven Immunohistochemistry (IHC) slide analysis services are offered through products such as Lunit SCOPE PD-L1, Lunit SCOPE HER2, Lunit SCOPE ER/PR, and others.
Contact:
Email: jaewhan.lee@lunit.io
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