RapidPlan knowledge based planning: iterative learning process and model ability to steer planning strategies.

Publisher: BioMed Central Country of Publication: England NLM ID: 101265111 Publication Model: Electronic Cited Medium: Internet ISSN: 1748-717X (Electronic) Linking ISSN: 1748717X NLM ISO Abbreviation: Radiat Oncol Subsets: MEDLINE

Original Publication: [London] : BioMed Central, 2006-

Head and Neck Neoplasms/*radiotherapy ; Radiotherapy Planning, Computer-Assisted/*methods ; Radiotherapy, Intensity-Modulated/*methods; Humans ; Organs at Risk ; Quality Improvement ; Radiotherapy Dosage ; Radiotherapy, Intensity-Modulated/adverse effects

Purpose: To determine if the performance of a knowledge based RapidPlan (RP) planning model could be improved with an iterative learning process, i.e. if plans generated by an RP model could be used as new input to re-train the model and achieve better performance.
Methods: Clinical VMAT plans from 83 patients presenting with head and neck cancer were selected to train an RP model, CL-1. With this model, new plans on the same patients were generated, and subsequently used as input to train a novel model, CL-2. Both models were validated on a cohort of 20 patients and dosimetric results compared. Another set of 83 plans was realised on the same patients with different planning criteria, by using a simple template with no attempt to manually improve the plan quality. Those plans were employed to train another model, TP-1. The differences between the plans generated by CL-1 and TP-1 for the validation cohort of patients were compared with respect to the differences between the original plans used to build the two models.
Results: The CL-2 model presented an improvement relative to CL-1, with higher R ² values and better regression plots. The mean doses to parallel organs decreased with CL-2, while D 1% to serial organs increased (but not significantly). The different models CL-1 and TP-1 were able to yield plans according to each original strategy.
Conclusion: A refined RP model allowed the generation of plans with improved quality, mostly for parallel organs at risk and, possibly, also the intrinsic model quality.

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Keywords: Knowledge based planning; RapidPlan

Date Created: 20191101 Date Completed: 20200408 Latest Revision: 20200408

20221213

PMC6822368

10.1186/s13014-019-1403-0

31666094