Mapping driver mutations to histopathological subtypes in papillary thyroid carcinoma: Applying a deep convolutional neural network

Papillary thyroid carcinoma (PTC) is the most common subtype of thyroid cancers and informative biomarkers are critical for risk stratification and treatment guidance. About half of PTCs harbor BRAFV600E and 10%–15% have RAS mutations. In the current study, we trained a deep learning convolutional neural network (CNN) model (Google Inception v3) on histopathology images obtained from The Cancer Genome Atlas (TCGA) to classify PTCs into BRAFV600E or RAS mutations. We aimed to answer whether CNNs can predict driver gene mutations using images as the only input. The performance of our method is comparable to that of recent publications of other cancer types using TCGA tumor slides with area under the curve (AUC) of 0.878–0.951. Our model was tested on separate tissue samples from the same cohort. On the independent testing subset, the accuracy rate using the cutoff of truth rate 0.8 was 95.2% for BRAF and RAS mutation class prediction. Moreover, we showed that the image-based classification correlates well with mRNA-derived expression pattern (Spearman correlation, rho = 0.63, p = 0.002 on validation data and rho = 0.79, p = 2 × 10−5 on final testing data). The current study demonstrates the potential of deep learning approaches for histopathologically classifying cancer based on driver mutations. This information could be of value assisting clinical decisions involving PTCs.