Wang Ching-Wei, Khalil Muhammad-Adil, Firdi Nabila Puspita
Graduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei 106335, Taiwan.
Graduate Institute of Applied Science and Technology, National Taiwan University of Science and Technology, Taipei 106335, Taiwan.
Diagnostics (Basel). 2022 Jun 17;12(6):1489. doi: 10.3390/diagnostics12061489.
Precision oncology, which ensures optimized cancer treatment tailored to the unique biology of a patient's disease, has rapidly developed and is of great clinical importance. Deep learning has become the main method for precision oncology. This paper summarizes the recent deep-learning approaches relevant to precision oncology and reviews over 150 articles within the last six years. First, we survey the deep-learning approaches categorized by various precision oncology tasks, including the estimation of dose distribution for treatment planning, survival analysis and risk estimation after treatment, prediction of treatment response, and patient selection for treatment planning. Secondly, we provide an overview of the studies per anatomical area, including the brain, bladder, breast, bone, cervix, esophagus, gastric, head and neck, kidneys, liver, lung, pancreas, pelvis, prostate, and rectum. Finally, we highlight the challenges and discuss potential solutions for future research directions.
精准肿瘤学能够确保根据患者疾病的独特生物学特性量身定制优化的癌症治疗方案，发展迅速且具有重大临床意义。深度学习已成为精准肿瘤学的主要方法。本文总结了近期与精准肿瘤学相关的深度学习方法，并回顾了过去六年内的150多篇文章。首先，我们调查了按各种精准肿瘤学任务分类的深度学习方法，包括治疗计划的剂量分布估计、治疗后的生存分析和风险估计、治疗反应预测以及治疗计划的患者选择。其次，我们概述了各解剖区域的研究，包括脑、膀胱、乳腺、骨、宫颈、食管、胃、头颈部、肾脏、肝脏、肺、胰腺、骨盆、前列腺和直肠。最后，我们强调了挑战并讨论了未来研究方向的潜在解决方案。
