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Session: Multi-Disciplinary General ePoster Viewing [Return to Session]

Automated Field-In-Field Integrated with Aperture Prediction for Whole Brain Radiotherapy

K Huang1,2*, Y Xiao2, S Hernandez1,2, C Nguyen2, C Wang3, T Briere2, C Cardenas4, L Court2, (1) The University of Texas MD Anderson Cancer Center UT Health Graduate School of Biomedical Sciences at Houston, Houston TX, (2) Department of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston TX, (3) Department of Radiation Oncology, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston TX (4) The University of Alabama at Birmingham, Birmingham, AL


PO-GePV-M-3 (Sunday, 7/10/2022)   [Eastern Time (GMT-4)]

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Purpose: To develop an automatic field-in-field (FIF) solution for whole-brain radiation therapy (WBRT) that creates a homogenous dose distribution by minimizing hotspots, resulting in clinically acceptable plans.

Methods: An auto-planning algorithm was configured to suite physician whole-brain planning practices to automatically generate FIF plans independent of treatment planning systems. Planning variables included the definition of hotspots, the target volume, the maximum number of subfields, and the minimum MU per field. The algorithm iteratively identifies a hotspot, creates two opposing subfields, calculates dose, and optimizes beam weight based on user-configured constraints of DVH coverage and least-squared cost functions. The algorithm was tested on 6 retrospective whole brain patients. First, an in-house, clinically validated, landmark-based automated beam aperture technique was used to generate the treatment fields and initial plans. The FIF algorithm was then applied to optimize the plans using physician defined goals of V99.9(brain)=100% (Rx=30Gy/10fx) and 107% hotspot definition. The final auto-optimized plans were assessed for clinical acceptability by an experienced radiation oncologist using a 5-point scale (>3 acceptable).

Results: The average machine time to automatically produce a plan in 15 mins and does not require user intervention. 4 or 6 subfields were added to plans. All the auto-plans were clinically acceptable with 3 plans use-as-is and 3 plans needing minor edits that were considered optional. FIF reduced hotspots from 116% to 107%. The FIF algorithm eliminated 110% hotspots. The sizes of 107% hotspot were reduced from 886 (±481) cc to 59 (±24) cc on average. The average maximum point dose percentage after FIF was 108%±2.5%.

Conclusion: This algorithm successfully produced high-quality plans and can improve treatment planning efficiency when incorporated into an automatic planning workflow. The algorithm has the potential to be configured for other disease sites and physician preferences.


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