论文标题
基于知识的辐射处理计划：数据驱动方法调查
Knowledge-based Radiation Treatment Planning: A Data-driven Method Survey
论文作者
论文摘要
本文调查了过去十年中针对基于知识的计划（KBP）引入的数据驱动剂量预测方法。这些方法根据使用以前的知识的方法和技术分为两个主要类别：传统的KBP方法和基于深度学习的方法。先前需要几何或解剖学特征才能找到先前交付的治疗计划存储库或构建预测模型的最佳匹配案例的研究包括在传统方法类别中，而基于深度学习的方法包括培训神经网络以进行剂量预测的研究。对每个类别进行了全面的审查，从多年来的剂量预测上突出了关键参数，方法及其前景。我们根据每个类别的框架和癌症场所将引用的作品分开。最后，我们简要讨论了传统的KBP方法和基于深度学习的方法的性能，以及两种数据驱动的KBP方法的未来趋势。
This paper surveys the data-driven dose prediction approaches introduced for knowledge-based planning (KBP) in the last decade. These methods were classified into two major categories according to their methods and techniques of utilizing previous knowledge: traditional KBP methods and deep-learning-based methods. Previous studies that required geometric or anatomical features to either find the best matched case(s) from repository of previously delivered treatment plans or build prediction models were included in traditional methods category, whereas deep-learning-based methods included studies that trained neural networks to make dose prediction. A comprehensive review of each category is presented, highlighting key parameters, methods, and their outlooks in terms of dose prediction over the years. We separated the cited works according to the framework and cancer site in each category. Finally, we briefly discuss the performance of both traditional KBP methods and deep-learning-based methods, and future trends of both data-driven KBP approaches.
