Mar 11, 2020

The Application of Deep Learning in Cancer Prognosis Prediction

Cancers
Wan ZhuXiangqian Guo

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 ca...Continue Reading

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Citations

Mentioned in this Paper

Classification
Tissue Engineering
Recurrent Malignant Neoplasm
Learning
Malignant Neoplasms
Disease Progression
Diagnostic Imaging
Cancer Prognosis
The Cancer Genome Atlas
Hospital Admission

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