Using deep neural networks and interpretability methods to identify gene expression patterns that predict radiomic features and histology in non-small cell lung cancer

Abstract

Purpose: Integrative analysis combining diagnostic imaging and genomic information can uncover biological insights into lesions that are visible on radiologic images. We investigate techniques for interrogating a deep neural network trained to predict quantitative image (radiomic) features and histology from gene expression in non-small cell lung cancer (NSCLC). Approach: Using 262 training and 89 testing cases from two public datasets, deep feedforward neural networks were trained to predict the values of 101 computed tomography (CT) radiomic features and histology. A model interrogation method called gene masking was used to derive the learned associations between subsets of genes and a radiomic feature or histology class [adenocarcinoma (ADC), squamous cell, and other]. Results: Overall, neural networks outperformed other classifiers. In testing, neural networks classified histology with area under the receiver operating characteristic curves (AUCs) of 0.86 (ADC), 0.91 (squamous cell), and 0.71 (other). Classification performance of radiomics features ranged from 0.42 to 0.89 AUC. Gene masking analysis revealed new and previously reported associations. For example, hypoxia genes predicted histology ( $>0.90\mathrm{AUC}$ ). Previously published gene signatures for classifying histology were also predictive in our model ( $>0.80\mathrm{AUC}$ ). Gene sets related to the immune or cardiac systems and cell development processes were predictive ( $>0.70\mathrm{AUC}$ ) of several different radiomic features. AKT signaling, tumor necrosis factor, and Rho gene sets were each predictive of tumor textures. Conclusions: This work demonstrates neural networks' ability to map gene expressions to radiomic features and histology types in NSCLC and to interpret the models to identify predictive genes associated with each feature or type.

Keywords: deep learning; gene expression; model interpretability; non-small cell lung cancer; radiogenomics.

Fig. 1

An overview of this study’s approaches to (a) training and (b) interpretation radiogenomic neural networks.

Fig. 2

The architecture and hyperparameter tuning of a radiogenomic neural network.

Fig. 3

The ability of models to predict NSCLC histology and stage in (a) training and (b) testing. In training, models were evaluated using 10-fold CV, and models were compared using the mean AUC scores in CV. The top performing model was then retrained on the full training dataset and evaluated on the testing dataset. The testing performance scores are shown for each histology type and stage. (c)–(e) Gene masking of the histology neural network using gene sets from (c) published gene signatures for histology,^, (d) Hallmark (top five out of 50), and (e) Gene Ontology biological processes (GO.bp, top ten out of 7350). In (c)–(e), each column is a type of histology and each row is a gene set used to mask the trained model to inspect how well the model predicted a certain histology type. The color in a cell shows the model’s performance using a gene set to predict a histology type, where red denotes higher AUCs and purple denotes lower AUCs in the testing dataset. (b) The ability of the histology model to predict each histology type and the AUC score is based on using all genes in the gene expression profile. (c)–(e) The ability of a specific gene set in predicting a histology type. For more details on the gene sets used, see Sec. 2. Notation: lcc = gene signature for large cell carcinoma, scc = gene signature for squamous cell carcinoma, adc = gene signature for adenocarciomas, adc versus scc = gene signature for differentiating ADC from SCC.

Fig. 4

Radiogenomic modeling performance (a) between neural networks and other models in the training dataset. Neural network performance (b) in the training and testing datasets for the 13 radiomic features selected for further analysis. Train scores represent the averaged scores of the validation folds during 10-fold CV in the training dataset. The test scores are the model’s performance in the testing dataset after models were retrained on the full training dataset.

Fig. 5

Gene masking of the radiogenomics models with biological processes from GO. The top three gene sets ranked by test AUC for each radiomic feature are shown.