PathAI, a global leader in artificial intelligence (AI) and digital pathology solutions, has announced the launch of MET Predict on the AISight Image Management System (IMS). MET Predict is an AI-powered algorithm created to assist pathologists in identifying non-small cell lung cancer (NSCLC) tumors that may exhibit MET exon 14 skipping (METex14) or MET amplification directly from an H&E whole slide image.
NSCLC is responsible for approximately 85% of lung cancer cases, with 3-4% of these involving METex14 mutations. Currently, there are targeted therapies available for patients with NSCLC who harbor these mutations. Additionally, MET amplification, which occurs in 1-6% of NSCLC cases, is emerging as a significant biomarker in lung cancer treatment.
Traditional methods for detecting MET alterations, such as exon 14 skipping or amplification, often face challenges due to high costs, time-intensive procedures, and the need for specialized equipment and trained personnel. These limitations hinder accessibility and scalability, highlighting the need for more efficient and cost-effective assessment methods.
Andy Beck, MD, PhD, co-founder and CEO of PathAI, emphasized the importance of this development. “The integration of MET Predict into AISight marks a significant leap forward in utilizing AI to enhance the efficiency of NSCLC tumor assessments, particularly in identifying those with potential genetic alterations. By providing rapid and precise biomarker insights directly from H&E images, MET Predict equips pathologists with the essential information needed to drive timely and accurate NSCLC evaluations.”
The MET Predict algorithm has the potential to significantly improve the efficiency of biomarker evaluation and molecular testing paradigms. It identifies over 90% of MET-altered tumors and offers insights that may preserve tissue in 30% of cases where the likelihood of MET alteration is low.
With the addition of MET Predict, AISight expands its toolset with state-of-the-art technology to assist pathologists in lung cancer assessments. This innovation addresses a critical need in pathology labs for accessible, rapid, and comprehensive molecular tools that provide actionable insights from biopsy samples.
References:
1. American Cancer Society. Key Statistics for Lung Cancer. https://www.cancer.org/cancer/lung-cancer/about/what-is.html
2. Frampton, G. M., et al. Activation of MET via diverse exon 14 splicing alterations occurs in multiple tumor types and confers clinical sensitivity to MET inhibitors. Cancer discovery, 5(8), 850-859.
3. Wolf J., et al. Capmatinib in MET Exon 14-Mutated or MET-Amplified Non-Small-Cell Lung Cancer. N Engl J Med. 2020 Sep 3;383(10):944-957. doi: 10.1056/NEJMoa2002787. PMID: 32877583
