Automated field‐in‐field whole brain radiotherapy planning

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.

Artificial Intelligence–Based Radiotherapy Contouring and Planning to Improve Global Access to Cancer Care

“…In other work, we have shown that automatically adding subfields results in higher levels of acceptability. 26,28,29 Chest wall acceptability results were also lower for use as-is, although high after minor edits. This is consistent with our earlier work, where we showed that these plans are easily corrected to give clinically acceptable plans.…”

Validation of an automated contouring and treatment planning tool for pediatric craniospinal radiation therapy

“…Physicians noted that they would prefer to use additional brain sub-fields to reduce the size of the 107% hotspot. While our current CSI approach does not include sub-fields for the brain fields, they could easily be added using a technique that has been separately developed for whole brain radiation ( 15 ). Finally, one physician noted that the MLCs could be opened around the back of the skull to ensure that patients with pseudomeningoceles would be treated properly, with no negative effect on the patient.…”

“…To alleviate demanding workflows and increase global access to high-quality radiation therapy, artificial intelligence has been introduced to automate various aspects of the radiation therapy treatment planning process. The Radiation Planning Assistant (RPA) planning team has developed algorithms to automate contouring, treatment planning, and quality assurance for adult disease sites, including the cervix, chest wall, spine, head and neck, and whole brain (10)(11)(12)(13)(14)(15). Court et al recently summarized how the RPA was designed alongside leaders in resource-constrained countries to address the global expertise gap in radiation oncology (16).…”