PathAI Introduces AI-Powered Tool to Enhance Lung Cancer Assessments

PathAI, a leader in artificial intelligence and digital pathology solutions, has unveiled MET Predict, a new AI-powered algorithm integrated into their AISight Image management system. This innovative tool is designed to aid pathologists in identifying non-small cell lung cancer (NSCLC) tumors that may have MET exon 14 skipping (METex14) or MET amplification, using only H&E whole slide images.

NSCLC represents approximately 85% of lung cancer cases, with 3-4% involving METex14 mutations. Currently, targeted therapies are available for NSCLC patients with METex14 mutations. Additionally, MET amplification occurs in 1-6% of NSCLC cases and is emerging as a potential biomarker in lung cancer research.

Traditional molecular testing for MET alterations faces several challenges, including high costs, time-consuming procedures, and the need for specialized equipment and trained personnel. These factors have limited the accessibility and scalability of such testing, creating a need for more efficient and cost-effective assessment methods.

MET Predict addresses these challenges by providing rapid biomarker insights directly from H&E images. The algorithm has demonstrated the ability to identify over 90% of MET altered tumors while potentially preserving tissue in 30% of cases where the likelihood of MET alteration is low. This capability could significantly improve the efficiency of biomarker evaluation and molecular testing paradigms in pathology labs.

Dr. Andy Beck, co-founder and CEO of PathAI, emphasized the significance of this development, stating, "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."

The introduction of MET Predict expands AISight's toolset, providing pathologists with advanced technology for lung cancer assessments. This innovation addresses a critical need in pathology labs for accessible, rapid, and comprehensive molecular tools that can provide actionable insights from biopsy samples.

The potential impact of MET Predict on lung cancer diagnostics is substantial. By streamlining the identification of tumors with potential MET alterations, the tool could lead to more timely and targeted treatment decisions for NSCLC patients. This could be particularly beneficial given the availability of approved targeted therapies for patients with METex14 mutations.

Furthermore, the ability to potentially preserve tissue in cases where MET alteration is less likely could have significant implications for sample management and further testing. This feature addresses a common challenge in oncology diagnostics, where tissue preservation is crucial for comprehensive molecular profiling.

While MET Predict and AISight are currently designated for research use only, their development signals a promising direction in the integration of AI technologies in cancer diagnostics. As these tools continue to evolve and undergo further validation, they may play an increasingly important role in enhancing the accuracy and efficiency of lung cancer assessments in clinical settings.

The launch of MET Predict represents a significant step forward in the application of AI to oncology diagnostics. By providing pathologists with advanced tools to quickly identify potential genetic alterations in NSCLC tumors, PathAI is contributing to the broader effort to improve cancer diagnosis and treatment strategies. As the field of AI in healthcare continues to advance, innovations like MET Predict may become increasingly crucial in supporting more personalized and effective approaches to cancer care.