Chen Wen, Wang Xu, Duan Huihong, Zhang Xiaobing, Dong Ting, Nie Shengdong
Institute of Medical Imaging, University of Shanghai for Science and Technology, Shanghai 200093, P.R.China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2020 Oct 25;37(5):918-929. doi: 10.7507/1001-5515.201909066.
In recent years, deep learning has provided a new method for cancer prognosis analysis. The literatures related to the application of deep learning in the prognosis of cancer are summarized and their advantages and disadvantages are analyzed, which can be provided for in-depth research. Based on this, this paper systematically reviewed the latest research progress of deep learning in the construction of cancer prognosis model, and made an analysis on the strengths and weaknesses of relevant methods. Firstly, the construction idea and performance evaluation index of deep learning cancer prognosis model were clarified. Secondly, the basic network structure was introduced, and the data type, data amount, and specific network structures and their merits and demerits were discussed. Then, the mainstream method of establishing deep learning cancer prognosis model was verified and the experimental results were analyzed. Finally, the challenges and future research directions in this field were summarized and expected. Compared with the previous models, the deep learning cancer prognosis model can better improve the prognosis prediction ability of cancer patients. In the future, we should continue to explore the research of deep learning in cancer recurrence rate, cancer treatment program and drug efficacy evaluation, and fully explore the application value and potential of deep learning in cancer prognosis model, so as to establish an efficient and accurate cancer prognosis model and realize the goal of precision medicine.
近年来，深度学习为癌症预后分析提供了一种新方法。对深度学习在癌症预后应用方面的相关文献进行了总结，并分析了其优缺点，可为深入研究提供参考。在此基础上，本文系统综述了深度学习在癌症预后模型构建方面的最新研究进展，并对相关方法的优缺点进行了分析。首先，阐明了深度学习癌症预后模型的构建思路和性能评价指标。其次，介绍了基本网络结构，并讨论了数据类型、数据量以及具体的网络结构及其优缺点。然后，对建立深度学习癌症预后模型的主流方法进行了验证并分析了实验结果。最后，总结并展望了该领域面临的挑战和未来研究方向。与以往模型相比，深度学习癌症预后模型能够更好地提高癌症患者的预后预测能力。未来，应继续探索深度学习在癌症复发率、癌症治疗方案及药物疗效评估等方面的研究，充分挖掘深度学习在癌症预后模型中的应用价值和潜力，从而建立高效、准确的癌症预后模型，实现精准医疗的目标。
