Machine intelligence for precision oncology  

作　　者：Nelson S Yee 

机构地区：[1]Department of Medicine,The Pennsylvania State University College of Medicine,Penn State Cancer Institute,Penn State Health Milton S.Hershey Medical Center,Hershey,PA 17033-0850,United States

出　　处：《World Journal of Translational Medicine》2021年第1期1-10,共10页世界转化医学杂志

摘　　要：Despite various advances in cancer research,the incidence and mortality rates of malignant diseases have remained high.Accurate risk assessment,prevention,detection,and treatment of cancer tailored to the individual are major challenges in clinical oncology.Artificial intelligence(AI),a field of applied computer science,has shown promising potential of accelerating evolution of healthcare towards precision oncology.This article focuses on highlights of the application of data-driven machine learning(ML)and deep learning(DL)in translational research for cancer diagnosis,prognosis,treatment,and clinical outcomes.MLbased algorithms in radiological and histological images have been demonstrated to improve detection and diagnosis of cancer.DL-based prediction models in molecular or multi-omics datasets of cancer for biomarkers and targets enable drug discovery and treatment.ML approaches combining radiomics with genomics and other omics data enhance the power of AI in improving diagnosis,prognostication,and treatment of cancer.Ethical and regulatory issues involving patient confidentiality and data security impose certain limitations on practical implementation of ML in clinical oncology.However,the ultimate goal of application of AI in cancer research is to develop and implement multi-modal machine intelligence for improving clinical decision on individualized management of patients.

关 键 词：Artificial intelligence Deep learning Machine learning Precision oncology Radiomics Radiogenomics 

分 类 号：R73[医药卫生—肿瘤]