ArticleMedical physics2022
Robust automated radiation therapy treatment planning using scenario-specific dose prediction and robust dose mimicking.
Article in 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.
- AI in Proton Therapy Treatment Planning: A Narrative Review.International journal of particle therapy · 2026Article
- Evaluating dose distribution in prostate IMRT patients using deep learning: the influence of loss function on model performance.Physical and engineering sciences in medicine · 2026Article
- A Preliminary Study on Deep Learning-Based Plan Quality Prediction in Gamma Knife Radiosurgery for Brain Metastases.Cancers · 2025Article
- Enhancing stereotactic ablative boost radiotherapy dose prediction for bulky lung cancer: A multi-scale dilated network approach with scale-balanced structure loss.Journal of applied clinical medical physics · 2025Article
- Dosimetric Study and Robustness Analysis of Base Note Intensive Locked Field Radiotherapy for Left Breast Cancer.Breast cancer (Dove Medical Press) · 2024Article
- Automated treatment planning for proton pencil beam scanning using deep learning dose prediction and dose-mimicking optimization.Journal of applied clinical medical physics · 2023Article
- TrDosePred: A deep learning dose prediction algorithm based on transformers for head and neck cancer radiotherapy.Journal of applied clinical medical physics · 2023Article
- Learning image representations for content-based image retrieval of radiotherapy treatment plans.Physics in medicine and biology · 2023Article
- OpenKBP-Opt: an international and reproducible evaluation of 76 knowledge-based planning pipelines.Physics in medicine and biology · 2022Article
- Robust automated radiation therapy treatment planning using scenario-specific dose prediction and robust dose mimicking.Medical physics · 2022Article
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2 authors.
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Abstract
purposeWe present a framework for robust automated treatment planning using machine learning, comprising scenario-specific dose prediction and robust dose mimicking.
methodsThe scenario dose prediction pipeline is divided into the prediction of nominal dose from input image and the prediction of scenario dose from nominal dose, each using a deep learning model with U-net architecture. By using a specially developed dose-volume histogram-based loss function, the predicted scenario doses are ensured sufficient target coverage despite the possibility of the training data being non-robust. Deliverable plans may then be created by solving a robust dose mimicking problem with the predictions as scenario-specific reference doses.
resultsNumerical experiments are performed using a data set of 52 intensity-modulated proton therapy plans for prostate patients. We show that the predicted scenario doses resemble their respective ground truth well, in particular while having target coverage comparable to that of the nominal scenario. The deliverable plans produced by the subsequent robust dose mimicking were showed to be robust against the same scenario set considered for prediction.
conclusionsWe demonstrate the feasibility and merits of the proposed methodology for incorporating robustness into automated treatment planning algorithms.
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