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.
Highlights
Laboratory, Bioinformatics center, School of Basic Medical Sciences, Henan University, Kaifeng 475004, Department of Anesthesia, Stanford University, 300 Pasteur Drive, Stanford, CA 94305, USA
Known as deep neural network (DNN), is a branch of machine learning that has made some major breakthrough in recent years due to the increase of computation power, the improvement in model architecture [27] and the exponential growth of data captured by cellular and other devices
Based on the types of Neural network (NN) and whether feature extraction has been used, the publications that we reviewed could be grouped into three classes: (1) NN models with no feature extraction, (2) Feature extraction from multi-omics data to build fully connected NNs, and (3) convolutional NN (CNN) based models
Summary
In the United States, approximately 1 in 10 adults have been diagnosed with cancer [1]. The Genotype-Tissue Expression (GTEx) database contains whole-genome sequencing and RNA-sequencing profiles from ~960 postmortem adult donors of many tissue samples that have tissue images stored in an image library for public access [25,26]. These public data provide unprecedented opportunities to better illustrate the molecular mechanism of cancers and normal tissues, and become the major resources to apply novel methods, build models and perform predictions in cancer prognosis
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