2020/09/15 by Shadab Momin, Yabo Fu, Momin, Shadab +13 · 8 citations
Medicine · Physics and Astronomy · #Advanced Radiotherapy Techniques #Artificial Intelligence in Healthcare and Education #Artificial intelligence #Artificial neural network #Computer science #Data science #Deep learning #Deep neural networks #FOS: Physical sciences #Key (lock) #Machine learning #Medical Physics (physics.med-ph) #Radiomics and Machine Learning in Medical Imaging #physics.med-ph
paper · pdf · doi:10.48550/arxiv.2009.07388
published in arXiv (Cornell University) (Cornell University) · 5 figures
openalex publication_date 2020/09/15 · arxiv created 2020/09/18 · openalex created_date 2020/09/21 · arxiv updated 2020/09/22 · openalex updated_date 2026/08/08
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.