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SEOUL, South Korea, Nov. 27, 2023 /PRNewswire/ -- Lunit (KRX:328130.KQ), a leading provider of AI-powered solutions for cancer diagnostics and therapeutics, published a new study in npj Precision Oncology (Nature Partner Journal), using Lunit SCOPE IO to predict prognosis in stage II–III colon cancer via AI-powered spatial analysis of tumor-infiltrating lymphocytes (TIL). This study showed that Lunit SCOPE IO and the power of AI for spatial TIL analysis can provide clinicians with a practical and efficient tool to enhance prognosis prediction.
Lunit's AI-powered TIL analyzer, Lunit SCOPE IO
Traditionally recognized as a biomarker that can be related to cancer prognosis, TIL densities in the tumor microenvironment (TME) offer valuable insights into the patient's immune response to the tumor. However, the manual evaluation of TIL densities has been laborious and time-consuming, even requiring additional steps in tissue preparation, such as special lymphocyte staining. Moreover, manual evaluation is prone to variability between observers.
The study utilized Lunit SCOPE IO, Lunit's AI-powered hematoxylin and eosin (H&E) whole-slide image (WSI) analyzer, to evaluate intratumoral TIL (iTIL) and tumor-related stromal TIL (sTIL) densities from WSIs of 289 patients with stage II–III colon cancer treated with surgery and adjuvant therapy. Patients with confirmed recurrences showed significantly lower sTIL densities compared to those with no recurrence (mean sTIL density 630.2/mm2 in cases with confirmed recurrence vs. 1021.3/mm2 in no recurrence).
The study also identified distinct risk groups: patients with the lowest iTIL and sTIL were grouped as high-risk, patients with higher sTIL than the median as low-risk, and intermediate-risk group. These groups were predictive of recurrence-free survival time and were independently associated with clinical outcomes after adjusting for other clinical factors. This result suggests that AI-powered TIL analysis can provide practical and reliable prognostic information for stage II–III colon cancer.
"Despite progress in colon cancer treatments, we still see a relapse in about 20–30% of stage II–III patients. This underscores the need for more precise prognostic biomarkers," said Brandon Suh, CEO of Lunit. "With the integration of AI, Lunit's innovative approach not only simplifies the evaluation process but also holds the potential to guide treatment decisions and improve patient outcomes with a tailored approach."
About Lunit
Founded in 2013, Lunit is a deep learning-based medical AI company on a mission to conquer cancer. We are committed to harnessing AI to ensure accurate diagnosis and optimal treatment for each cancer patient using AI-powered medical image analytics and AI biomarkers.
As a medical AI company grounded on clinical evidence, our findings are presented in major peer-reviewed journals, such as the Journal of Clinical Oncology and the Lancet Digital Health, and global conferences, including ASCO and RSNA.
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. 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.
