Deep Convolutional Neural Networks Detect Tumor Genotype from Pathological Tissue Images in Gastrointestinal Stromal Tumors

SIMPLE SUMMARY: In this study, we established four convolutional neural network (DCNN) models (AlexNet, ResNet101, DenseNet201, and InceptionV3) to predict drug-sensitive mutations from images of tissues with gastrointestinal stromal tumors. The treatment of these tumors depends on the mutational subtype of the KIT/PDGFRA genes. Previous studies rarely focused on mesenchymal tumors and mutational subtypes. More than 5000 images of 365 GISTs from three independent laboratories were used to generate the model. DenseNet201 achieved an accuracy of 87% while the accuracies of AlexNet, InceptionV3, and ResNet101 were 75%, 81%, and 86%, respectively. Cross-institutional inconsistency and the contributions of image color and subcellular components were studied and analyzed. ABSTRACT: Gastrointestinal stromal tumors (GIST) are common mesenchymal tumors, and their effective treatment depends upon the mutational subtype of the KIT/PDGFRA genes. We established deep convolutional neural network (DCNN) models to rapidly predict drug-sensitive mutation subtypes from images of pathological tissue. A total of 5153 pathological images of 365 different GISTs from three different laboratories were collected and divided into training and validation sets. A transfer learning mechanism based on DCNN was used with four different network architectures, to identify cases with drug-sensitive mutations. The accuracy ranged from 87% to 75%. Cross-institutional inconsistency, however, was observed. Using gray-scale images resulted in a 7% drop in accuracy (accuracy 80%, sensitivity 87%, specificity 73%). Using images containing only nuclei (accuracy 81%, sensitivity 87%, specificity 73%) or cytoplasm (accuracy 79%, sensitivity 88%, specificity 67%) produced 6% and 8% drops in accuracy rate, respectively, suggesting buffering effects across subcellular components in DCNN interpretation. The proposed DCNN model successfully inferred cases with drug-sensitive mutations with high accuracy. The contribution of image color and subcellular components was also revealed. These results will help to generate a cheaper and quicker screening method for tumor gene testing.