Application of Deep Learning in Predicting the Prognosis of Acute Myeloid Leukemia using Cytogenetics, Age, and Mutations

Abstract

Objective: We explored how Deep Learning can be utilized to predict the prognosis of acute myeloid leukemia. Methods: Out of The Cancer Genome Atlas database, 94 acute myeloid leukemia cases were used in this study. Input data included age, 10 most common cytogenetic and 23 most common mutation results; output was the prognosis (diagnosis to death). In our Deep Learning network, autoencoders were stacked to form a hierarchical Deep Learning model from which raw data were compressed and organized, and high-level features were extracted. The network was written in R language and was designed to predict the prognosis of acute myeloid leukemia for a given case (diagnosis to death of either more or less than 730 days). Results: The Deep Learning network achieved an excellent accuracy of 83% in predicting prognosis. Conclusion: As a proof-of-concept study, our preliminary results demonstrated a practical application of Deep Learning in the future practice of prognostic prediction using next-generation sequencing data.