Abstract
Treatment planning plays an important role in the process of radiotherapy (RT). The quality of the treatment plan directly and significantly affects patient treatment outcomes. In the past decades, technological advances in computer and software have promoted the development of RT treatment planning systems with sophisticated dose calculation and optimization algorithms. Treatment planners now have greater flexibility in designing highly complex RT treatment plans in order to mitigate the damage to healthy tissues better while maximizing radiation dose to tumor targets. Nevertheless, treatment planning is still largely a time-inefficient and labor-intensive process in current clinical practice. Artificial intelligence, including machine learning (ML) and deep learning (DL), has been recently used to automate RT treatment planning and has gained enormous attention in the RT community due to its great promises in improving treatment planning quality and efficiency. In this article, we reviewed the historical advancement, strengths, and weaknesses of various DL-based automated RT treatment planning techniques. We have also discussed the challenges, issues, and potential research directions of DL-based automated RT treatment planning techniques.
Highlights
As one of the cancer treatment modalities, radiotherapy plays an important role in the treatment of numerous types of malignant tumors
We reviewed the historical advancement, strengths, and weaknesses of various deep learning (DL)-based automated RT treatment planning techniques
Treatment planning is an important process of radiotherapy
Summary
As one of the cancer treatment modalities, radiotherapy plays an important role in the treatment of numerous types of malignant tumors. Treatment planning is an important process of radiotherapy. Radiation beam parameters, including aperture shapes at each gantry angle and dose deposition for each aperture, are determined during the treatment planning process. The beam parameters are subsequently transferred to radiotherapy machines to enable radiation delivery so that the prescribed dose distribution can be delivered as planned to achieve satisfactory tumor control while preserving normal tissue function [1]. The current practice of treatment planning is largely a manual process, which is time-consuming and labor-intensive, typically taking hours, or days to complete one case. The plan optimization parameters need to be manually adjusted and determined by planners. The plan quality heavily depends upon the planner’s experience. It is a trial-and-error process through multiple
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