Purpose: There has been much research interest in automated head-and-neck (HN) planning with the goal of reducing planning time and inter-planner variability while improving plan quality. However, clinical uses are still limited and institution-dependent due to the plan complexity. This work aims to investigate whether the use of a novel semi-automated two-step optimization method (TSP) can improve the quality and efficiency of planning while providing a simple framework that other institutions can follow.
Methods And Materials: Forty patients (two and three prescription isodose levels) were retrospectively studied. Plans were generated by TSP which incorporates a knowledge-based planning solution. Comparisons were performed for plan conformity and selected dose-volume indices between clinical plan (CP) and TSP. Blind reviews were carried out by 15 clinicians to determine preference between the CP and TSP, as well as clinical suitability.
Results: For majority of patients studied, TSP had similar or slightly better conformity for the high-dose PTV, and better conformity for the low-dose PTV and 45 Gy isodose lines compared to CP. The only statistically significant difference observed for the serial organs was a reduction of the spinal cord maximum dose with TSP. Except for left parotid gland (D and V for both 2R× and 3R× groups) and oral cavity (D for 3R× group), TSP had significant dose reductions for all parallel organs compared to CP. Blind reviewers either showed preference/no preference for 57.2%/21.7% (2R×) and 57.5%/27.8% (3R×) of TSP compared with CP. Excluding no preference votes, 60% of TSP were preferred. TSP was selected majority of the time when looking at the vote distribution for each patient individually.
Conclusion: Our TSP allows plans to be created within 90-min time frame while offering improvements in plan quality and less inter-planner variability as compared to traditional planning techniques.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10243331 | PMC |
http://dx.doi.org/10.1002/acm2.13939 | DOI Listing |
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!