ArticleRadiation oncology (London, England)2018
Planning comparison of five automated treatment planning solutions for locally advanced head and neck cancer.
Article in Radiation oncology (London, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- Development and Clinical Validation of a Protocol-Agnostic Machine Learning Platform for Automated Treatment Planning in External Beam Radiation Therapy.Advances in radiation oncology · 2026Article
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- Objective-function-guided automated VMAT planning reduces OAR dose, low-dose exposure, and inter-planner variability in breast radiotherapy.Scientific reports · 2026Article
- Prospective blinded clinical evaluation of a fully automated workflow for prostate radiotherapy.Physics and imaging in radiation oncology · 2026Article
- Photon-counting detector computing tomography in treatment planning of head-and-neck cancer.Physics and imaging in radiation oncology · 2026Article
- Clinical implementation of an automated VMAT treatment planning script for head and neck cancer patients: three-year experience.Frontiers in oncology · 2026Article
- Beyond auto-segmentation: the case for planning and dosimetry AI in head and neck radiation oncology.BMJ oncology · 2026Review
- 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
- Hybrid proton planning combining spread-out Bragg peak beams with transmission beams to shorten field delivery times while maintaining plan quality.Physics and imaging in radiation oncology · 2025Article
- Proof of concept of fully automated adaptive workflow for head and neck radiotherapy treatments with a conventional linear accelerator.Frontiers in oncology · 2025Article
- Article
- Feasibility and Clinical Acceptability of Automation-Assisted 3D Conformal Radiotherapy Planning for Patients With Cervical Cancer in a Resource-Constrained Setting.JCO global oncology · 2023Article
- A comparative study of deep learning-based knowledge-based planning methods for 3D dose distribution prediction of head and neck.Journal of applied clinical medical physics · 2023Article
- A two-step treatment planning strategy incorporating knowledge-based planning for head-and-neck radiotherapy.Journal of applied clinical medical physics · 2023Article
- MOZART, a QSAR Multi-Target Web-Based Tool to Predict Multiple Drug-Enzyme Interactions.Molecules (Basel, Switzerland) · 2023Article
- Using feasibility dose-volume histograms to reduce intercampus plan quality variability for head-and-neck cancer.Journal of applied clinical medical physics · 2023Article
- Dose distribution prediction for head-and-neck cancer radiotherapy using a generative adversarial network: influence of input data.Frontiers in oncology · 2023Article
- Knowledge-based planning for the radiation therapy treatment plan quality assurance for patients with head and neck cancer.Journal of applied clinical medical physics · 2022Article
- Maintaining dosimetric quality when switching to a Monte Carlo dose engine for head and neck volumetric-modulated arc therapy planning.Journal of applied clinical medical physics · 2022Article
- Knowledge-based radiation treatment planning: A data-driven method survey.Journal of applied clinical medical physics · 2021Review
Corrections and comments
- Erratum issuedCorrection to: Planning comparison of five automated treatment planning solutions for locally advanced head and neck cancer.2018 · Lang-Tanadini, S [corrected to Tanadini-Lang, S]
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAutomated treatment planning and/or optimization systems (ATPS) are in the process of broad clinical implementation aiming at reducing inter-planner variability, reducing the planning time allocated for the optimization process and improving plan quality. Five different ATPS used clinically were evaluated for advanced head and neck cancer (HNC).
methodsThree radiation oncology departments compared 5 different ATPS: 1) Automatic Interactive Optimizer (AIO) in combination with RapidArc (in-house developed and Varian Medical Systems); 2) Auto-Planning (AP) (Philips Radiation Oncology Systems); 3) RapidPlan version 13.6 (RP1) with HNC model from University Hospital A (Varian Medical Systems, Palo Alto, USA); 4) RapidPlan version 13.7 (RP2) combined with scripting for automated setup of fields with HNC model from University Hospital B; 5) Raystation multicriteria optimization algorithm version 5 (RS) (Laboratories AB, Stockholm, Sweden). Eight randomly selected HNC cases from institution A and 8 from institution B were used. PTV coverage, mean and maximum dose to the organs at risk and effective planning time were compared. Ranking was done based on 3 Gy increments for the parallel organs.
resultsAll planning systems achieved the hard dose constraints for the PTVs and serial organs for all patients. Overall, AP achieved the best ranking for the parallel organs followed by RS, AIO, RP2 and RP1. The oral cavity mean dose was the lowest for RS (31.3 ± 17.6 Gy), followed by AP (33.8 ± 17.8 Gy), RP1 (34.1 ± 16.7 Gy), AIO (36.1 ± 16.8 Gy) and RP2 (36.3 ± 16.2 Gy). The submandibular glands mean dose was 33.6 ± 10.8 Gy (AP), 35.2 ± 8.4 Gy (AIO), 35.5 ± 9.3 Gy (RP2), 36.9 ± 7.6 Gy (RS) and 38.2 ± 7.0 Gy (RP1). The average effective planning working time was substantially different between the five ATPS (in minutes): < 2 ± 1 for AIO and RP2, 5 ± 1 for AP, 15 ± 2 for RP1 and 340 ± 48 for RS, respectively.
conclusionsAll ATPS were able to achieve all planning DVH constraints and the effective working time was kept bellow 20 min for each ATPS except for RS. For the parallel organs, AP performed the best, although the differences were small.
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