Automated field‐in‐field whole brain radiotherapy planning. Issue 2 (10th November 2022) - Huang, Kai
Abstract: Purpose: We developed and tested an automatic field‐in‐field (FIF) solution for whole‐brain radiotherapy (WBRT) planning that creates a homogeneous dose distribution by minimizing hotspots, resulting in clinically acceptable plans. Methods: A configurable auto‐planning algorithm was developed to automatically generate FIF WBRT plans independent of the treatment planning system. Configurable parameters include the definition of hotspots, target volume, maximum number of subfields, and minimum number of monitor units per field. This algorithm iteratively identifies a hotspot, creates two opposing subfields, calculates the dose, and optimizes the beam weight based on user‐configured constraints of dose‐volume histogram coverage and least‐squared cost functions. The algorithm was retrospectively tested on 17 whole‐brain patients. First, an in‐house landmark‐based automated beam aperture technique was used to generate the treatment fields and initial plans. Second, the FIF algorithm was employed to optimize the plans using physician‐defined goals of 99.9% of the brain volume receiving 100% of the prescription dose (30 Gy in 10 fractions) and a target hotspot definition of 107% of the prescription dose. The final auto‐optimized plans were assessed for clinical acceptability by an experienced radiation oncologist using a five‐point scale. Results: The FIF algorithm reduced the mean (± SD) plan hotspot percentage dose from 35.0 Gy (116.6%) ± 0.6 Gy (2.0%) to 32.6 Gy (108.8%) ± 0.4 Gy (1.2%). Also, it decreased the mean (± SD) hotspot V107% [cm 3 ] from 959 ± 498 cm 3 to 145 ± 224 cm 3 . On average, plans were produced in 16 min without any user intervention. Furthermore, 76.5% of the auto‐plans were clinically acceptable (needing no or minor stylistic edits), and all of them were clinically acceptable after minor clinically necessary edits. Conclusions: This algorithm successfully produced high‐quality WBRT plans and can improve treatment planning efficiency when incorporated into an automatic planning workflow.