Rapid and accurate assessment of EGFR mutations in lung adenocarcinoma is critical for the management of NSCLC patients.
PCR-based assays provide rapid results but with reduced accuracy compared with next-generation sequencing and can exhaust the increasingly valuable small amount of biopsy tissue available for predictive biomarkers. Computational biomarkers
Leveraging modern foundation models may offer alternative solutions.
In this episode of Precision Pathology Podcast (P3), the host discusses with Dr. Chad Vanderbilt from the Department of Pathology and Laboratory Medicine at MSKCC in NY, his team’s recent study, published in Nature Medicine, using a large international clinical dataset of digital lung adenocarcinoma slides to develop a computational EGFR biomarker. The artificial-intelligence-assisted workflow has the potential to reduce the number of rapid molecular tests needed by up to 43%.
Selected Podcast
Pathology Foundation Model for Lung Cancer Bio-Marker Detection
Featured Speaker:
Chad Vanderbilt, MD
Dr. Vanderbilt is a physician who specializes in the development and interpretation of DNA-based tests performed on solid tumors and hematologic malignancies. In his research, he focuses on using genomic and image-based data to better understand the tumor and the tumor microenvironment, with the goal of developing novel diagnostic tests. Dr. Vanderbilt is board certified in anatomic pathology, clinical pathology, and molecular genetic pathology.