ArticleJournal of applied clinical medical physics2023
Automated field-in-field whole brain radiotherapy planning.
Article in Journal of applied clinical medical physics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 5 citations in OpenAlex.
- Human-like AI-based auto-field-in-field whole-brain radiotherapy treatment planning with conversation large language model feedback.Journal of applied clinical medical physics · 2026Article
- Isocenter Optimization in WBRT: Concurrent Sparing of Lens and Lacrimal Gland via Anterior Penumbra Sharpening.Journal of applied clinical medical physics · 2026Article
- Article
- Automated treatment planning for brain and spinal metastases.Reports of practical oncology and radiotherapy : journal of Greatpoland Cancer Center in Poznan and Polish Society of Radiation Oncology · 2026Article
- Artificial Intelligence-Based Radiotherapy Contouring and Planning to Improve Global Access to Cancer Care.JCO global oncology · 2024Article
- Automated Contouring and Planning in Radiation Therapy: What Is 'Clinically Acceptable'?Diagnostics (Basel, Switzerland) · 2023Review
- Automated field-in-field whole brain radiotherapy planning.Journal of applied clinical medical physics · 2023Article
- Validation of an automated contouring and treatment planning tool for pediatric craniospinal radiation therapy.Frontiers in oncology · 2023Article
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeWe 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.
methodsA 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.
resultsThe 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
conclusionsThis algorithm successfully produced high-quality WBRT plans and can improve treatment planning efficiency when incorporated into an automatic planning workflow.
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