Predicting and Visualizing STK11 Mutation in Lung Adenocarcinoma Histopathology Slides Using Deep Learning

Abstract

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.

When blockchain meets artificial intelligence: An application to cancer histopathology

“…In the past few years, a number of studies reported that deep-learning-based computer vision models successfully predict molecular characteristics, such as gene mutation status, molecular subtypes, and microsatellite instability (MSI), with H&E-stained whole-slide images (WSIs) across various cancer types and even at pan-cancer level. 1 , 2 , 3 , 4 , 5 Moreover, many of these studies also claimed that their models are generalizable on external H&E image datasets, further illustrating the robustness and potential clinical applicability.…”

Deep Learning and Its Applications in Computational Pathology

“…The visualization techniques also reveal results that often match pathologists' expectations and many models are generalizable to independent real-world clinical images. For example, Inception and InceptionResNet architectural models demonstrate high accuracy and other statistical metrics in predicting subtypes and key biomarker mutations, such as STK11 and EGFR, in non-small-cell lung cancer histopathology slides [14,57,58]. With the integration of other critical clinical variables and images, immune response, G-CIMP, and telomere length can be predicted in glioblastoma patients [59].…”

Deep Learning and Its Applications in Computational Pathology

“…The visualization techniques also reveal results that often match the pathologist's expectation and many models are generalizable to independent real-world clinical images. For example, Inception and In-ceptionResnet architected models show high accuracy and other statistical metrics in predicting subtypes and key biomarker mutations, such as STK11 and EGFR, in nonesmall-cell lung cancer histopathology slides [13,56,57]. With the integration of other critical clinical variables and images, immune response, G-CIMP, and telomere length are able to be predicted in glioblastoma patients [58].…”