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Semi-Automated Treatment Re-Planning for Real-Time Online Adaptive Radiotherapy

P Chow

P Chow*, N Agazaryan , M Cao , D Low , P Lee , J Lamb , University of California at Los Angeles, Los Angeles, CA


SU-H3-GePD-J(A)-2 (Sunday, July 30, 2017) 4:00 PM - 4:30 PM Room: Joint Imaging-Therapy ePoster Lounge - A

Purpose: On-board magnetic resonance imaging (MRI) has greatly facilitated online adaptive radiotherapy. Our institution has developed an MRI-guided adaptive workflow for abdominal stereotactic body radiotherapy designed to optimize sparing of stomach and small bowel given daily changes in the relationship between these organs and the planning target volume (PTV). For each fraction, the setup MRI is used to assess anatomic and dosimetric changes. If organ at risk (OAR) dose constraints are not met, adaptive planning is performed. The severe time limitation posed by having the patient remain on the table potentially results in a sub-optimal plan. A study was undertaken to determine the degree to which on-line adapted plans were sub-optimal and to investigate a potential semi-automated method to improve adaptive plan quality.

Methods: Twelve adapted fractions were retrospectively re-planned off-line to assess if a better quality plan could be achieved. A semi-automated process was then simulated by calculating an array of 36 plans with a preset variation in the optimization weights of the PTV and the primary OAR of separately by +-20%, +-50%, and +100%. From this matrix, the best plan was chosen based on target coverage and OAR sparing.

Results: In all retrospectively re-planned fractions, the OAR dose was decreased to close to or better than in the original plan. The mean dose to the OAR was reduced an average of 6.5%. Simulation of the semi-automated re-planning process of two fractions yielded a plan that was on average 5.7% lower in dose from 19 Gy to the duodenum from the original plan while target coverage was the same.

Conclusion: Re-planning of patients undergoing online adaptive radiotherapy can improve the quality of the treatment plan delivered to the patient and, if done on a computer cluster or similar computational framework using this semi-automated process, could be completed in minutes.

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