Knowledge-based planning for fully automated radiation therapy treatment planning of 10 different cancer sites

Abstract

PURPOSE: Radiation treatment planning is highly complex and can have significant inter- and intra-planner inconsistency, as well as variability in planning time and plan quality. Knowledge-based planning (KBP) is a tool that can be used to efficiently produce high-quality, consistent, clinically acceptable plans, independent of planner skills and experience. In this study, we created and validated multiple clinically acceptable and fully automatable KBP models, with the goal of creating VMAT plans without user intervention. METHODS: Ten KBP models were configured using high quality clinical plans from a single institution. They were then honed to be part of a fully automatable system by incorporating scriptable planning structures, plan creation, and plan optimization. These models were verified and validated using quantitative (model statistics) and qualitative (dose-volume histogram estimation review) analysis. The resulting KBP-generated plans were reviewed by physicians and rated for clinical acceptability. RESULTS: Autoplanning models were created for anorectal, bladder, breast/chest wall, cervix, esophagus, head and neck, liver, lung/mediastinum, prostate, and prostate with nodes treatment sites. All models were successfully created to be part of a fully automated system without the need for human intervention to create a fully optimized plan. The physician review indicated that, on average, 88% of all KBP-generated plans were "acceptable as is" and 98% were "acceptable after minor edits." CONCLUSION: KBP models for multiple treatment sites were used as a basis to generate fully automatable, efficient, consistent, high-quality, and clinically acceptable plans. These plans do not require human intervention, demonstrating the potential this work has to significantly impact treatment planning workflows.

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