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.

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A two-step electrochemical surface treatment has been developed to modify the CP Ti surface on commercially pure titanium grade 2 (CP Ti): (1) anodic oxidation to form TiO 2 nanotube precoatings loaded with silver (Ag) and (2) microarc oxidation (MAO) to produce a porous Ca-P-Ag coating in an electrolyte containing Ag, Ca, and P. One-step MAO in the same electrolyte has also been used to produce porous Ca-P-Ag coatings without anodic oxidation and preloaded Ag as a control. Surface morphologies and alloying chemistry of the two coatings were characterized by SEM, EDS, and XPS. Biocompatibility and antimicrobial properties have been evaluated by the MTT method and co-culture of Staphylococcus aureus, respectively. It is demonstrated that porous coatings with high Ag content can be achieved on the CP Ti by the two-step treatment. The optimized MAO voltage for excellent comprehensive properties of the coating is 350 V, in which a suitable chemical equilibrium between Ag, Ca, and P contents and a Ca/P ratio of 1.67 similar to HA can be obtained, and the Ag particles are in the size of less than 100 nm and embedded into the underneath of the coating surface. After being contacted with S. aureus for 1 and 7 days, the average bactericidal rates were 99.53 and 89.27% and no cytotoxicity was detected. In comparison, the one-step MAO coatings contained less Ag, had a lower Ca/P ratio, and showed lower antimicrobial ability than the two-step treated samples.

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