Multi-channel multi-task deep learning for predicting EGFR and KRAS mutations of non-small cell lung cancer on CT images.

BACKGROUND: Predicting the mutation statuses of 2 essential pathogenic genes [epidermal growth factor receptor (EGFR) and Kirsten rat sarcoma (KRAS)] in non-small cell lung cancer (NSCLC) based on CT is valuable for targeted therapy because it is a non-invasive and less costly method. Although deep learning technology has realized substantial computer vision achievements, CT imaging being used to predict gene mutations remains challenging due to small dataset limitations. METHODS: We propose a multi-channel and multi-task deep learning (MMDL) model for the simultaneous prediction of EGFR and KRAS mutation statuses based on CT images. First, we decomposed each 3D lung nodule into 9 views. Then, we used the pre-trained inception-attention-resnet model for each view to learn the features of the nodules. By combining 9 inception-attention-resnet models to predict the types of gene mutations in lung nodules, the models were adaptively weighted, and the proposed MMDL model could be trained end-to-end. The MMDL model utilized multiple channels to characterize the nodule more comprehensively and integrate patient personal information into our learning process. RESULTS: We trained the proposed MMDL model using a dataset of 363 patients collected by our partner hospital and conducted a multi-center validation on 162 patients in The Cancer Imaging Archive (TCIA) public dataset. The accuracies for the prediction of EGFR and KRAS mutations were, respectively, 79.43% and 72.25% in the training dataset and 75.06% and 69.64% in the validation dataset. CONCLUSIONS: The experimental results demonstrated that the proposed MMDL model outperformed the latest methods in predicting EGFR and KRAS mutations in NSCLC.

Knowledge-guided synthetic medical image adversarial augmentation for ultrasonography thyroid nodule classification.

BACKGROUND AND OBJECTIVE: Image classification is an important task in many medical applications. Methods based on deep learning have made great achievements in the computer vision domain. However, they typically rely on large-scale datasets which are annotated. How to obtain such great datasets is still a serious problem in medical domain. METHODS: In this paper, we propose a knowledge-guided adversarial augmentation method for synthesizing medical images. First, we design Term and Image Encoders to extract domain knowledge from radiologists, then we use domain knowledge as novel condition to constrain the Auxiliary Classifier Generative Adversarial Network (ACGAN) framework for the synthesis of high-quality thyroid nodule images. Finally, we demonstrate our method on the task of classifying ultrasonography thyroid nodule. Our method can make effective use of the high-quality diagnostic experience of advanced radiologists. In addition, we creatively choose to extract domain knowledge from standardized terms rather than ultrasound images. RESULTS: Our novel method is demonstrated on a limited dataset of 1937 clinical thyroid ultrasound images and corresponding standardized terms. The accuracy of the proposed model for thyroid nodules is 91.46%, the sensitivity is 90.63%, the specificity is 92.65%, and the AUC is 95.32%, which is better than the current classification methods for thyroid nodules. The experimental results show the model has better generalization and robustness. CONCLUSIONS: We believe that the proposed method can alleviate the problem of insufficient data in the medical domain, and other medical problems can benefit from using synthetic augmentation.

MLW-gcForest: a multi-weighted gcForest model towards the staging of lung adenocarcinoma based on multi-modal genetic data.

BACKGROUND: Lung cancer is one of the most common types of cancer, among which lung adenocarcinoma accounts for the largest proportion. Currently, accurate staging is a prerequisite for effective diagnosis and treatment of lung adenocarcinoma. Previous research has used mainly single-modal data, such as gene expression data, for classification and prediction. Integrating multi-modal genetic data (gene expression RNA-seq, methylation data and copy number variation) from the same patient provides the possibility of using multi-modal genetic data for cancer prediction. A new machine learning method called gcForest has recently been proposed. This method has been proven to be suitable for classification in some fields. However, the model may face challenges when applied to small samples and high-dimensional genetic data. RESULTS: In this paper, we propose a multi-weighted gcForest algorithm (MLW-gcForest) to construct a lung adenocarcinoma staging model using multi-modal genetic data. The new algorithm is based on the standard gcForest algorithm. First, different weights are assigned to different random forests according to the classification performance of these forests in the standard gcForest model. Second, because the feature vectors generated under different scanning granularities have a diverse influence on the final classification result, the feature vectors are given weights according to the proposed sorting optimization algorithm. Then, we train three MLW-gcForest models based on three single-modal datasets (gene expression RNA-seq, methylation data, and copy number variation) and then perform decision fusion to stage lung adenocarcinoma. Experimental results suggest that the MLW-gcForest model is superior to the standard gcForest model in constructing a staging model of lung adenocarcinoma and is better than the traditional classification methods. The accuracy, precision, recall, and AUC reached 0.908, 0.896, 0.882, and 0.96, respectively. CONCLUSIONS: The MLW-gcForest model has great potential in lung adenocarcinoma staging, which is helpful for the diagnosis and personalized treatment of lung adenocarcinoma. The results suggest that the MLW-gcForest algorithm is effective on multi-modal genetic data, which consist of small samples and are high dimensional.