Machine learning identifies a core gene set predictive of acquired resistance to EGFR tyrosine kinase inhibitor

摘要

Acquired resistance (AR) to epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) is a major issue worldwide, for both patients and healthcare providers. However, precise prediction is currently infeasible due to the lack of an appropriate model. This study was conducted to develop and validate an individualized prediction model for automated detection of acquired EGFR-TKI resistance.