Knowledge-based and automated treatment planning
Software that learns from hundreds of past plans what dose distribution is achievable for a new patient, and produces a plan in minutes that a planner would have taken hours to reach.
Overview
Radiotherapy planning has been a manual optimisation by skilled dosimetrists, with quality varying between planners and centres; trial reviews repeatedly found plan quality affected outcomes. Knowledge-based planning models (RapidPlan and others) predict achievable dose-volume histograms from anatomy, and automated multi-criteria and AI planning generate plans directly. They raise the floor of plan quality, cut planning time from hours to minutes, and make daily adaptive replanning feasible. Human review remains essential, and models can inherit the habits of the plans they were trained on.
How it works
Statistical or deep-learning models map patient anatomy to achievable dose, driving automated optimisation toward consistently high-quality plans.
- Consistent plan quality
- Minutes instead of hours
- Enables adaptive workflows
- Inherits biases of the training plans
- Still needs expert review
- Commissioning per site and technique
Latest papers
topQuery for this technology: (TITLE:"Knowledge-based and automated treatment planning" OR ABSTRACT:"Knowledge-based and automated treatment planning") AND (cancer OR tumor OR tumour OR oncology OR carcinoma OR lymphoma OR leukemia OR leukaemia OR myeloma OR sarcoma OR melanoma OR glioma). Results are unfiltered search hits about Knowledge-based and automated treatment planning, not a curated reading list.
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