Label-Free Prediction of EGFR Mutation Status Using Fluorescence Lifetime Imaging and Deep Learning in Lung Adenocarcinoma.

Abstract excerpt

Accurate prediction of epidermal growth factor receptor (EGFR) mutations is essential for guiding targeted therapy in non-small cell lung cancer (NSCLC) and is routinely performed in clinical pathology samples. Current diagnostic practices rely on molecular techniques such as PCR-based assays and next-generation sequencing, which are often costly, time-consuming, and destructive to tissue samples. Furthermore, in...

Topics

• Humans
• Deep Learning
• ErbB Receptors
• Mutation
• Lung Neoplasms
• Adenocarcinoma of Lung
• Optical Imaging