AIMC Topic: Radiotherapy, Intensity-Modulated
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Toward a human-centric co-design methodology for AI detection of differences between planned and delivered dose in radiotherapy.
Journal of applied clinical medical physics
Mar 31, 2025
INTRODUCTION: Many artificial intelligence (AI) solutions have been proposed to enhance the radiotherapy (RT) workflow, but limited applications have been implemented to date, suggesting an implementation gap. One contributing factor to this gap is a...
Closing the gap in plan quality: Leveraging deep-learning dose prediction for adaptive radiotherapy.
Journal of applied clinical medical physics
Mar 19, 2025
PURPOSE: Balancing quality and efficiency has been a challenge for online adaptive therapy. Most systems start the online re-optimization with the original planning goals. While some systems allow planners to modify the planning goals, achieving a hi...
Enhanced dose prediction for head and neck cancer artificial intelligence-driven radiotherapy based on transfer learning with limited training data.
Journal of applied clinical medical physics
Mar 14, 2025
PURPOSE: Training deep learning dose prediction models for the latest cutting-edge radiotherapy techniques, such as AI-based nodal radiotherapy (AINRT) and Daily Adaptive AI-based nodal radiotherapy (DA-AINRT), is challenging due to limited data. Thi...
Geometric and Dosimetric Evaluation of a RayStation Deep Learning Model for Auto-Segmentation of Organs at Risk in a Real-World Head and Neck Cancer Dataset.
Clinical oncology (Royal College of Radiologists (Great Britain))
Mar 1, 2025
AIMS: To assess geometric accuracy and dosimetric impact of a deep learning segmentation (DLS) model on a large, diverse dataset of head and neck cancer (HNC) patients treated with intensity-modulated proton therapy (IMPT).
A knowledge-based planning model to identify fraction-reduction opportunities in brain stereotactic radiotherapy.
Journal of applied clinical medical physics
Feb 21, 2025
OBJECTIVE: To develop and validate a HyperArc-based RapidPlan (HARP) model for three-fraction brain stereotactic radiotherapy (SRT) plans to then use to replan previously treated five-fraction SRT plans. Demonstrating the possibility of reducing the ...
Dose prediction via deep learning to enhance treatment planning of lung radiotherapy including simultaneous integrated boost techniques.
Medical physics
Feb 18, 2025
BACKGROUND: Recent studies have shown deep learning techniques are able to predict three-dimensional (3D) dose distributions of radiotherapy treatment plans. However, their use in dose prediction for treatments with varied prescription doses includin...
Advanced prediction of multi-leaf collimator leaf position using artificial neural network.
Medical physics
Feb 18, 2025
BACKGROUND: Multi-leaf collimators (MLCs) are crucial for modern radiotherapy as they ensure precise target irradiation through accurate leaf positioning. Accurate prediction of MLC leaf positions is vital for the effectiveness and safety of treatmen...
Evaluation and failure analysis of four commercial deep learning-based autosegmentation software for abdominal organs at risk.
Journal of applied clinical medical physics
Feb 13, 2025
PURPOSE: Deep learning-based segmentation of organs-at-risk (OAR) is emerging to become mainstream in clinical practice because of the superior performance over atlas and model-based autocontouring methods. While several commercial deep learning-base...
The implementation of knowledge-based planning with partial OAR contours for prostate radiotherapy.
Journal of applied clinical medical physics
Feb 7, 2025
PURPOSE: Intra- and inter-observer contour uncertainty is a continuous challenge in treatment planning for radiotherapy. Our proposed solution to address this challenge is the use of partial contours for treatment planning, focusing on uninvolved or ...
Synthetic CT generation from CBCT and MRI using StarGAN in the Pelvic Region.
Radiation oncology (London, England)
Feb 4, 2025
RATIONALE AND OBJECTIVES: This study evaluated StarGAN, a deep learning model designed to generate synthetic computed tomography (sCT) images from magnetic resonance imaging (MRI) and cone-beam computed tomography (CBCT) data using a single model. Th...
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