Multimodal Non-Small Cell Lung Cancer Classification Using Convolutional Neural Networks

Abstract

Lung cancer is the leading cause of death worldwide. Early detection of lung cancer is a hard mission. New Small Cell Lung Cancer (NSCLC) is the most prevalent sub-type of lung cancer. Differentiating between several NSCLC subtypes is important for making the right decision of treatment plan for the patient. Despite the focus of recent researchers on single modality approach, multi-omics modalities have many underlying influences and discoveries in the cancer detection and classification area. Through this research multi-omics modalities are used. Previous efforts have been focused either on multimodality using traditional machine learning classifiers or single modality using deep learning. Also, for the molecular sources (RNA-seq and miRNA-Seq) traditional machine learning approaches are usually used. For this work, deep learning using Convolutional Neural Networks (CNNs) is used and applied on the above-mentioned multimodalities. The classification accuracy results obtained for RNA-Seq, miRNA-Seq, WSIs are 96.79%, 98.59%, 89.73% respectively. The F1 scores obtained for RNA-Seq, miRNA-Seq, WSIs are 95.238%,99.67%,89.76% respectively. Moreover, the Area Under Curve obtained for RNA-Seq, miRNA-Seq, WSIs are 100%, 99.41%,97,54% respectively. These results improves the results obtained by other related works as will be explained. According to these improvements in the results, the lung cancer classification could be better and the disease would be discovered at early stages which is the goal for the research field efforts.

Keywords

• Lung cancer
• convolutional neural networks
• multimodality
• molecular data
• whole slide images
• deep learning