<ul><li>Lung cancer is the primary cause of cancer death globally, with non-small cell lung cancer (NSCLC) being the most common subtype.</li><li>A new study has developed a multimodal machine learning model to predict patient resistance to osimertinib, a third-generation EGFR-tyrosine kinase inhibitor, in late-stage NSCLC patients with activating EGFR mutations.</li><li>The model achieved a c-index of 0.82 on a multi-institutional dataset by integrating various data types such as histology images, next-generation sequencing (NGS) data, demographics data, and clinical records.</li><li>The multimodal model demonstrated superior performance over single modality models, highlighting the importance of combining multiple data types for accurate patient outcome prediction.</li></ul>

Predicting Targeted Therapy Resistance in Non-Small Cell Lung Cancer Using Multimodal Machine Learning

Discover more