Kourou Konstantina, Exarchos Konstantinos P, Papaloukas Costas, Sakaloglou Prodromos, Exarchos Themis, Fotiadis Dimitrios I
Unit of Medical Technology and Intelligent Information Systems, Dept. of Materials Science and Engineering, University of Ioannina, Ioannina, Greece.
Foundation for Research and Technology-Hellas, Institute of Molecular Biology and Biotechnology, Dept. of Biomedical Research, Ioannina GR45110, Greece.
Comput Struct Biotechnol J. 2021 Oct 6;19:5546-5555. doi: 10.1016/j.csbj.2021.10.006. eCollection 2021.
Artificial Intelligence (AI) has recently altered the landscape of cancer research and medical oncology using traditional Machine Learning (ML) algorithms and cutting-edge Deep Learning (DL) architectures. In this review article we focus on the ML aspect of AI applications in cancer research and present the most indicative studies with respect to the ML algorithms and data used. The PubMed and dblp databases were considered to obtain the most relevant research works of the last five years. Based on a comparison of the proposed studies and their research clinical outcomes concerning the medical ML application in cancer research, three main clinical scenarios were identified. We give an overview of the well-known DL and Reinforcement Learning (RL) methodologies, as well as their application in clinical practice, and we briefly discuss Systems Biology in cancer research. We also provide a thorough examination of the clinical scenarios with respect to disease diagnosis, patient classification and cancer prognosis and survival. The most relevant studies identified in the preceding year are presented along with their primary findings. Furthermore, we examine the effective implementation and the main points that need to be addressed in the direction of robustness, explainability and transparency of predictive models. Finally, we summarize the most recent advances in the field of AI/ML applications in cancer research and medical oncology, as well as some of the challenges and open issues that need to be addressed before data-driven models can be implemented in healthcare systems to assist physicians in their daily practice.
人工智能（AI）最近利用传统机器学习（ML）算法和前沿深度学习（DL）架构改变了癌症研究和医学肿瘤学的格局。在这篇综述文章中，我们聚焦于AI在癌症研究中的ML应用方面，并展示了关于ML算法和所用数据的最具代表性的研究。我们查阅了PubMed和dblp数据库，以获取过去五年中最相关的研究成果。基于对所提出的研究及其在癌症研究中医学ML应用的研究临床结果的比较，确定了三种主要临床场景。我们概述了著名的DL和强化学习（RL）方法及其在临床实践中的应用，并简要讨论了癌症研究中的系统生物学。我们还对疾病诊断、患者分类以及癌症预后和生存等临床场景进行了全面审视。介绍了上一年度确定的最相关研究及其主要发现。此外，我们研究了预测模型在稳健性、可解释性和透明度方面有效实施的情况以及需要解决的要点。最后，我们总结了AI/ML在癌症研究和医学肿瘤学领域的最新进展，以及在数据驱动模型能够在医疗系统中实施以协助医生日常实践之前需要解决的一些挑战和未决问题。
