Pham Tuan D
IEEE/ACM Trans Comput Biol Bioinform. 2023 Sep-Oct;20(5):3195-3204. doi: 10.1109/TCBB.2023.3274211. Epub 2023 Oct 9.
The ability to predict survival in cancer is clinically important because the finding can help patients and physicians make optimal treatment decisions. Artificial intelligence in the context of deep learning has been increasingly realized by the informatics-oriented medical community as a powerful machine-learning technology for cancer research, diagnosis, prediction, and treatment. This paper presents the combination of deep learning, data coding, and probabilistic modeling for predicting five-year survival in a cohort of patients with rectal cancer using images of RhoB expression on biopsies. Using about one-third of the patients' data for testing, the proposed approach achieved 90% prediction accuracy, which is much higher than the direct use of the best pretrained convolutional neural network (70%) and the best coupling of a pretrained model and support vector machines (70%).
预测癌症患者的生存情况在临床上具有重要意义，因为这一结果有助于患者和医生做出最佳治疗决策。深度学习背景下的人工智能已被以信息学为导向的医学界日益视为一种用于癌症研究、诊断、预测和治疗的强大机器学习技术。本文介绍了深度学习、数据编码和概率建模相结合的方法，用于利用活检组织中RhoB表达的图像预测一组直肠癌患者的五年生存率。使用约三分之一的患者数据进行测试，所提出的方法实现了90%的预测准确率，远高于直接使用最佳预训练卷积神经网络的准确率（70%）以及预训练模型与支持向量机的最佳组合的准确率（70%）。
