New Study Demonstrates Lunit SCOPE IO's Effectiveness to Predict Biliary Tract Cancer Therapy Outcomes - Findings to be Presented at the ESMO 2023

- Nine abstracts, featuring the effectiveness of Lunit's AI-powered TIL/WSI analyzer and biomarker platform, accepted by the ESMO 2023 Congress

SEOUL, South Korea, Oct. 16, 2023 /PRNewswire/ -- Lunit (KRX:328130.KQ), a leading provider of AI-powered solutions for cancer diagnostics and therapeutics, today announced the presentation of 9 studies at the upcoming European Society for Medical Oncology (ESMO) 2023 Congress, to be held in Madrid, Spain, from October 20-24.

During this year's congress, Lunit plans to highlight the predictive value and analytical power of its Lunit SCOPE suite, offering valuable insights for understanding the tumor microenvironment, predicting treatment responses, and accurately assessing HER2 scores, in various types of cancer such as biliary tract cancer, head and neck squamous cell carcinoma, and non-small cell lung cancer.

A collaborative study indicated that AI-based immune phenotyping can predict therapy outcomes in advanced biliary tract cancer (BTC) patients who are planning to be treated with anti-PD-1 therapy. A total of 337 H&E-stained whole slide images (WSI) were acquired for assessment. In the study, the research team defined the immune phenotype of the WSI samples via Lunit SCOPE IO, Lunit's AI TIL (tumor-infiltrating lymphocytes) analyzer. Among the three immune phenotypes (inflamed; immune-excluded; immune-desert), the inflamed group showed enhanced overall survival (12.5 vs. 5.1 months), progression-free survival (5.0 vs. 2.0 months), and objective response rates (27.5% vs. 7.7%), compared to the non-inflamed group.

In another study, it was found that TIL density in tumor microenvironment is highly correlated with favorable treatment response to immune checkpoint inhibitor (ICI) in head and neck squamous cell carcinoma (HNSCC). Assessed by Lunit SCOPE IO, patients with high-TIL showed a higher objective response rate (21.6% vs 5.7%) and more favorable median progression-free survival (3.2 vs 1.6 months).

Lunit also plans to present the results of three trials during this year's congress. A joint trial with the Mayo Clinic unveils that epithelial TIL demonstrated the highest ability to distinguish between MMR-D (Mismatch repair deficiency) and MMR-P (Mismatch repair proficiency) tumors in colon cancer. The post-hoc exploratory analysis results of three clinical trials utilizing Lunit SCOPE IO in Italy and France are also set to be showcased.

In another study, HER2 (human epidermal growth factor receptor-2) scoring in biliary tract cancer was evaluated using Lunit SCOPE HER2. The analysis showed a substantial concordance of 75.3% between AI and human pathologists' assessments.

Another study aimed to predict multiple druggable mutations in non-small cell lung cancer (NSCLC) based on AI analysis of H&E images. More than 3,000 NSCLC samples were used as training data to develop an AI-powered predictive model. In validation in an independent dataset, the model showed robust performance in predicting six types of mutations (EGFR-mt, KRAS-mt, ALK-tr, ROS1-tr, RET-tr, MET-ex). Notably, for MET exon skipping mutations, the model achieved a high positive predictive value (PPV), showing that test-positive patients were three times more likely to have true-MET-ex positive mutations compared to the overall patient population. Moreover, specificity and PPV for identifying patients without mutations (All-WT) were 99.2% and 95.2% respectively, which means with AI assistance unnecessary tests can be avoided. Following the results, it is expected that the newly developed AI genotype predictor, available for multiple genotypes in non-small cell lung cancer, holds immense potential for widespread adoption by clinicians and pharmaceutical industry leaders globally.

"We are thrilled to be at this year's ESMO with nine groundbreaking study results that prove the effectiveness of the Lunit SCOPE AI WSI analyzer and biomarker platform," said Brandon Suh, CEO of Lunit. "The study results emphasize the substantial progress made with Lunit SCOPE IO, building compelling evidence of the critical role of immune phenotyping in understanding cancer biology and optimizing treatment strategies. We remain committed to advancing this transformative technology through further research and development."

For inquiries or to schedule a meeting with the Lunit team, please contact oncology@lunit.io.

Lunit's Abstracts at ESMO 2023

About Lunit

Lunit is a deep learning-based medical AI company on a mission to conquer cancer. Our focus is on developing AI solutions for precision diagnostics and therapeutics, ensuring the right diagnosis, and treatment, at the right cost for each patient. Lunit is devoted to developing advanced medical image analytics and AI-based biomarkers via cutting-edge technology.

Founded in 2013, Lunit has been acknowledged around the world for its advanced, state-of-the-art technology and its application in medical images. As a medical AI company grounded on clinical evidence, the company's findings are presented in major peer-reviewed journals, such as the Journal of Clinical Oncology and JAMA Network Open, and global conferences, including ASCO and AACR.

After receiving FDA clearance and the CE Mark, our flagship Lunit INSIGHT suite is clinically used in approximately 2,000+ hospitals and medical institutions across 40+ countries. Lunit is headquartered in Seoul, South Korea, with offices and representatives worldwide. For more information, please visit lunit.io

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.