The Application of Deep Learning in Cancer Prognosis Prediction

Abstract

Deep learning has been applied to many areas in health care, including imaging diagnosis, digital pathology, prediction of hospital admission, drug design, classification of cancer and stromal cells, doctor assistance, etc. Cancer prognosis is to estimate the fate of cancer, probabilities of cancer recurrence and progression, and to provide survival estimation to the patients. The accuracy of cancer prognosis prediction will greatly benefit clinical management of cancer patients. The improvement of biomedical translational research and the application of advanced statistical analysis and machine learning methods are the driving forces to improve cancer prognosis prediction. Recent years, there is a significant increase of computational power and rapid advancement in the technology of artificial intelligence, particularly in deep learning. In addition, the cost reduction in large scale next-generation sequencing, and the availability of such data through open source databases (e.g., TCGA and GEO databases) offer us opportunities to possibly build more powerful and accurate models to predict cancer prognosis more accurately. In this review, we reviewed the most recent published works that used deep learning to build models for cancer prognosis prediction. Deep learning has been suggested to be a more generic model, requires less data engineering, and achieves more accurate prediction when working with large amounts of data. The application of deep learning in cancer prognosis has been shown to be equivalent or better than current approaches, such as Cox-PH. With the burst of multi-omics data, including genomics data, transcriptomics data and clinical information in cancer studies, we believe that deep learning would potentially improve cancer prognosis.

Keywords: cancer prognosis; deep learning; machine learning; multi-omics; prognosis prediction.

Figure 1

Workflow of building deep learning models for cancer prognosis prediction. The sources of input data include clinical data which could be text data and/or structured data (numeric and/or categorical data), clinical images which could be tissue slides in H&E staining or immune-histological staining. MRI, CT, etc, and genomic data which could be expression data (i.e., mRNA expression data, miRNA expression data), genomic sequence data (i.e., whole genome sequence, SNP data, CNA data, etc), epigenetic data (i.e., methylation data), etc. In the next step, researchers will examine the data to handle missing data and imbalanced data. Reduction of high dimensional genomic data is an optional step here. Features are then used to build a deep learning (neural network) model. The type of models to use depends on the input data. For example, fully connected NN is commonly used for structured datasets. Image data is used to build CNN models. Sequence data is often used to build RNN models. If multiple types of data exist, hybrid models can be built to accept different data types. After the model is built, the model will be tested in the holdout (or validation) datasets. It will also be important to test and compare the models using benchmark datasets. Finally, the model can be used in applications. Abbreviations: FPR: false positive rate; TPR: true positive rate.