RT Journal Article
SR Electronic
T1 Predicting and Visualizing STK11 Mutation in Lung Adenocarcinoma Histopathology Slides Using Deep Learning
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.02.20.956557
DO 10.1101/2020.02.20.956557
A1 Runyu Hong
A1 Wenke Liu
A1 David Fenyö
YR 2021
UL http://biorxiv.org/content/early/2021/12/15/2020.02.20.956557.abstract
AB Studies have shown that STK11 mutation plays a critical role in affecting the lung adenocarcinoma (LUAD) tumor immune environment. By training an Inception-Resnet-v2 deep convolutional neural network model, we were able to classify STK11-mutated and wild type LUAD tumor histopathology images with a promising accuracy (per slide AUROC=0.795). Dimensional reduction of the activation maps before the output layer of the test set images revealed that fewer immune cells were accumulated around cancer cells in STK11-mutation cases. Our study demonstrated that deep convolutional network model can automatically identify STK11 mutations based on histopathology slides and confirmed that the immune cell density was the main feature used by the model to distinguish STK11-mutated cases.Competing Interest StatementThe authors have declared no competing interest.