ArticleInternational journal of radiation oncology, biology, physics2017
Predicting Patient-specific Dosimetric Benefits of Proton Therapy for Skull-base Tumors Using a Geometric Knowledge-based Method.
Article in International journal of radiation oncology, biology, physics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 23 citations in OpenAlex.
- Perspectives on the model-based approach to proton therapy trials: A retrospective study of a lung cancer randomized trial.Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2020Trial
- Contour-based lung dose prediction for breast proton therapy.Journal of applied clinical medical physics · 2018Trial
- Optimizing allocation of limited proton fractions via NTCP-optimized combined proton-photon radiotherapy.Physical and engineering sciences in medicine · 2026Article
- Evaluating the technical feasibility of biology-guided dose painting in proton therapy.Physics and imaging in radiation oncology · 2025Article
- Article
- Estimating Potential Benefits to Neurocognition with Proton Therapy in Adults with Brain Tumors.International journal of particle therapy · 2023Article
- A scoping review of patient selection methods for proton therapy.Journal of medical radiation sciences · 2022Article
- A Decision Support Tool to Optimize Selection of Head and Neck Cancer Patients for Proton Therapy.Cancers · 2022Article
- Proton therapy with a fixed beamline for skull-base chordomas and chondrosarcomas: outcomes and toxicity.Radiation oncology (London, England) · 2021Article
- Radiobiological and dosimetric impact of RayStation pencil beam and Monte Carlo algorithms on intensity-modulated proton therapy breast cancer plans.Journal of applied clinical medical physics · 2019Article
- Evaluation of an Automated Proton Planning Solution.Cureus · 2018Article
- Automation in intensity modulated radiotherapy treatment planning-a review of recent innovations.The British journal of radiology · 2018Review
- Artificial intelligence in radiation oncology: A specialty-wide disruptive transformation?Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2018Review
- Automated Knowledge-Based Intensity-Modulated Proton Planning: An International Multicenter Benchmarking Study.Cancers · 2018Article
- Machine Learning in Radiation Oncology: Opportunities, Requirements, and Needs.Frontiers in oncology · 2018Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
purposeTo predict the organ at risk (OAR) dose levels achievable with proton beam therapy (PBT), solely based on the geometric arrangement of the target volume in relation to the OARs. A comparison with an alternative therapy yields a prediction of the patient-specific benefits offered by PBT. This could enable physicians at hospitals without proton capabilities to make a better-informed referral decision or aid patient selection in model-based clinical trials. METHODS AND MATERIALS: Skull-base tumors were chosen to test the method, owing to their geometric complexity and multitude of nearby OARs. By exploiting the correlations between the dose and distance-to-target in existing PBT plans, the models were independently trained for 6 types of OARs: brainstem, cochlea, optic chiasm, optic nerve, parotid gland, and spinal cord. Once trained, the models could estimate the feasible dose-volume histogram and generalized equivalent uniform dose (gEUD) for OAR structures of new patients. The models were trained using 20 patients and validated using an additional 21 patients. Validation was achieved by comparing the predicted gEUD to that of the actual PBT plan.
resultsThe predicted and planned gEUD were in good agreement. Considering all OARs, the prediction error was +1.4 ± 5.1 Gy (mean ± standard deviation), and Pearson's correlation coefficient was 93%. By comparing with an intensity modulated photon treatment plan, the model could classify whether an OAR structure would experience a gain, with a sensitivity of 93% (95% confidence interval: 87%-97%) and specificity of 63% (95% confidence interval: 38%-84%).
conclusionsWe trained and validated models that could quickly and accurately predict the patient-specific benefits of PBT for skull-base tumors. Similar models could be developed for other tumor sites. Such models will be useful when an estimation of the feasible benefits of PBT is desired but the experience and/or resources required for treatment planning are unavailable.
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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.