ArticleJournal of applied clinical medical physics2022
Automation of radiation treatment planning for rectal cancer.
Article in Journal of applied clinical medical physics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 1 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
- Article
- NRG Oncology Assessment of Artificial Intelligence for Automatic Treatment Planning in Radiation Therapy Clinical Trials: Present and Future.International journal of radiation oncology, biology, physics · 2025Article
- Artificial Intelligence-Based Radiotherapy Contouring and Planning to Improve Global Access to Cancer Care.JCO global oncology · 2024Article
- Addressing the Global Expertise Gap in Radiation Oncology: The Radiation Planning Assistant.JCO global oncology · 2023Review
- 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
- Leveraging intelligent optimization for automated, cardiac-sparing accelerated partial breast treatment planning.Frontiers in oncology · 2023Article
- Clinical acceptability of fully automated external beam radiotherapy for cervical cancer with three different beam delivery techniques.Medical physics · 2022Article
- Automation of radiation treatment planning for rectal cancer.Journal of applied clinical medical physics · 2022Article
Corrections and comments
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Authors and funding
12 authors at 4 institutions in 2 countries.
Funding
Abstract
purposeTo develop an automated workflow for rectal cancer three-dimensional conformal radiotherapy (3DCRT) treatment planning that combines deep learning (DL) aperture predictions and forward-planning algorithms.
methodsWe designed an algorithm to automate the clinical workflow for 3DCRT planning with field aperture creations and field-in-field (FIF) planning. DL models (DeepLabV3+ architecture) were trained, validated, and tested on 555 patients to automatically generate aperture shapes for primary (posterior-anterior [PA] and opposed laterals) and boost fields. Network inputs were digitally reconstructed radiographs, gross tumor volume (GTV), and nodal GTV. A physician scored each aperture for 20 patients on a 5-point scale (>3 is acceptable). A planning algorithm was then developed to create a homogeneous dose using a combination of wedges and subfields. The algorithm iteratively identifies a hotspot volume, creates a subfield, calculates dose, and optimizes beam weight all without user intervention. The algorithm was tested on 20 patients using clinical apertures with varying wedge angles and definitions of hotspots, and the resulting plans were scored by a physician. The end-to-end workflow was tested and scored by a physician on another 39 patients.
resultsThe predicted apertures had Dice scores of 0.95, 0.94, and 0.90 for PA, laterals, and boost fields, respectively. Overall, 100%, 95%, and 87.5% of the PA, laterals, and boost apertures were scored as clinically acceptable, respectively. At least one auto-plan was clinically acceptable for all patients. Wedged and non-wedged plans were clinically acceptable for 85% and 50% of patients, respectively. The hotspot dose percentage was reduced from 121% (σ = 14%) to 109% (σ = 5%) of prescription dose for all plans. The integrated end-to-end workflow of automatically generated apertures and optimized FIF planning gave clinically acceptable plans for 38/39 (97%) of patients.
conclusionWe have successfully automated the clinical workflow for generating radiotherapy plans for rectal cancer for our institution.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.