Imaging Based EGFR Mutation Subtype Classification Using EfficientNet
Authors: Daniel L Franklin, Tara Pattilachan, Anthony Magliocco
Journal: Cancer Research | Volume 82
Publisher: The American Association for Cancer Research
This study aimed to determine whether EfficientNet-B0 was able to classify EGFR mutation subtypes with H&E stained whole slide images of lung and lymph node tissue.
Background: Non-small cell lung cancer (NSCLC) accounts for the majority of all lung adenocarcinomas, with estimates that up to a third of such cases have a mutation in their epidermal growth factor receptor (EGFR). EGFR mutations can occur in various subtypes, such as Exon19 deletion, and L858R substitution, which are important for early therapy decisions. Here, we propose a deep learning approach for detecting and classifying EGFR mutation subtypes, which will greatly reduce the cost of determining mutation status, allowing for testing in a low resource setting.
Methods: An EfficientNet-B0 model was trained with whole slide images of lung tissue or metastatic lymph nodes with known EGFR mutation subtype (wild type, exon19 deletion or …