A methodology for automatic intensity-modulated radiation treatment planning for lung cancer

Abstract

In intensity-modulated radiotherapy (IMRT), the quality of the treatment plan, which is highly dependent upon the treatment planner's level of experience, greatly affects the potential benefits of the radiotherapy (RT). Furthermore, the planning process is complicated and requires a great deal of iteration, and is often the most time-consuming aspect of the RT process. In this paper, we describe a methodology to automate the IMRT planning process in lung cancer cases, the goal being to improve the quality and consistency of treatment planning. This methodology (1) automatically sets beam angles based on a beam angle automation algorithm, (2) judiciously designs the planning structures, which were shown to be effective for all the lung cancer cases we studied, and (3) automatically adjusts the objectives of the objective function based on a parameter automation algorithm. We compared treatment plans created in this system (mdaccAutoPlan) based on the overall methodology with plans from a clinical trial of IMRT for lung cancer run at our institution. The 'autoplans' were consistently better, or no worse, than the plans produced by experienced medical dosimetrists in terms of tumor coverage and normal tissue sparing. We conclude that the mdaccAutoPlan system can potentially improve the quality and consistency of treatment planning for lung cancer.

Intensity-modulated proton therapy and osteoradionecrosis in oropharyngeal cancer

“…Our results suggest that the lower rate of osteoradionecrosis with IMPT resulted from better mandibular sparing, which in turn suggests that stricter constraints for the mandible should be applied in IMRT planning. Better mandibular sparing without compromising target coverage could be done by using better planning techniques for photon therapy[31]. …”

Validation of Fully Automated VMAT Plan Generation for Library-Based Plan-of-the-Day Cervical Cancer Radiotherapy

“…The initial objectives in their work were defined on the basis of previous planning experience. Their ‘autoplans’ were consistently better, or no worse, than the manual plans in terms of tumor coverage and normal tissue sparing [22]. In another study Wu et al [23] developed a method using overlap volume histograms and IMRT data to guide and automate head and neck VMAT planning.…”

A DVH-guided IMRT optimization algorithm for automatic treatment planning and adaptive radiotherapy replanning

“…[4][5][6][7] The second technique is to employ a library of clinically approved and delivered plans of previously treated patients with similar medical characteristics in order to find a set of parameters for a new patient that produce a clinically desirable plan. [8][9][10] In this work, we propose an algorithm to automatically adjust the optimization model parameters to replicate a reference plan for a new patient's geometry with the reference plan being selected on patient similarity from a library of preciously delivered plans.…”