Machine Learning in Radiation Oncology: Opportunities, Requirements, and Needs

By M. Feng, Gilmer Valdes, Nayha Dixit, Timothy D. Solberg. 2018

102 citations recorded by the source corpus.

Machine learning (ML) has the potential to revolutionize the field of radiation oncology, but there is much work to be done. In this article, we approach the radiotherapy process from a workflow perspective, identifying specific areas where a data-centric approach using ML could improve the quality and efficiency of patient care. We highlight areas where ML has already been used, and identify areas where we should invest additional resources. We believe that this article can serve as a guide for both clinicians and researchers to start discussing issues that must be addressed in a timely manner.

Subfields: Language and Linguistics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Chao Yu
2. Jiming Liu
3. Shamim Nemati
4. Guoqi Ren
5. Ruosong Wang
6. Pierre Humbert

Citations in the corpus

• Reinforcement Learning in Healthcare: A Survey (2019, background): “f an advanced generic cancer trial. Radiotherapy is another major option of treating cancer, and a number of studies have applied RL approaches for developing automated radiation adaptation protocols [106]. Jalalimanesh et al. [94] proposed an agent-based simulation model and Q-learning algorithm to”