AIMC Topic: Radiotherapy Planning, Computer-Assisted
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Personalized auto-segmentation for magnetic resonance imaging-guided adaptive radiotherapy of large brain metastases.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Feb 4, 2025
BACKGROUND AND PURPOSE: Magnetic resonance-guided adaptive radiotherapy (MRgART) may improve the efficacy of large brain metastases (BMs)(≥2 cm), whereas the workflow requires optimized. This study develops a two-stage, personalized deep learning aut...
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...
Automated contouring for breast cancer radiotherapy in the isocentric lateral decubitus position: a neural network-based solution for enhanced precision and efficiency.
Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al]
Feb 3, 2025
BACKGROUND: Adjuvant radiotherapy is essential for reducing local recurrence and improving survival in breast cancer patients, but it carries a risk of ischemic cardiac toxicity, which increases with heart exposure. The isocentric lateral decubitus p...
TransAnaNet: Transformer-based anatomy change prediction network for head and neck cancer radiotherapy.
Medical physics
Jan 31, 2025
BACKGROUND: Adaptive radiotherapy (ART) can compensate for the dosimetric impact of anatomic change during radiotherapy of head-neck cancer (HNC) patients. However, implementing ART universally poses challenges in clinical workflow and resource alloc...
Automating the optimization of proton PBS treatment planning for head and neck cancers using policy gradient-based deep reinforcement learning.
Medical physics
Jan 31, 2025
BACKGROUND: Proton pencil beam scanning (PBS) treatment planning for head and neck (H&N) cancers is a time-consuming and experience-demanding task where a large number of potentially conflicting planning objectives are involved. Deep reinforcement le...
The potential use of deep learning in performing autocorrection of setup errors in patients receiving radiotherapy.
Radiography (London, England : 1995)
Jan 31, 2025
INTRODUCTION: Modern radiotherapy practice relies on multiple approaches for verification of patient positioning. All of these techniques require experienced radiotherapists who understand the anatomical landmarks and the limitations of the used veri...
Deep learning-based Monte Carlo dose prediction for heavy-ion online adaptive radiotherapy and fast quality assurance: A feasibility study.
Medical physics
Jan 27, 2025
BACKGROUND: Online adaptive radiotherapy (OART) and rapid quality assurance (QA) are essential for effective heavy ion therapy (HIT). However, there is a shortage of deep learning (DL) models and workflows for predicting Monte Carlo (MC) doses in suc...
Brachytherapy Seed Placement by Robotic Bronchoscopy with Cone Beam Computed Tomography Guidance for Peripheral Lung Cancer: A Human Cadaveric Feasibility Pilot.
International journal of radiation oncology, biology, physics
Jan 25, 2025
PURPOSE: This study evaluates the feasibility of using robotic-assisted bronchoscopy with cone beam computed tomography (RB-CBCT) platform to perform low-dose-rate brachytherapy (LDR-BT) implants in a mechanically ventilated human cadaveric model. Po...
Breast radiation therapy fluence painting with multi-agent deep reinforcement learning.
Medical physics
Jan 23, 2025
BACKGROUND: The electronic compensation (ECOMP) technique for breast radiation therapy provides excellent dose conformity and homogeneity. However, the manual fluence painting process presents a challenge for efficient clinical operation.
MR-linac: role of artificial intelligence and automation.
Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al]
Jan 22, 2025
The integration of artificial intelligence (AI) into radiotherapy has advanced significantly during the past 5 years, especially in terms of automating key processes like organ at risk delineation and treatment planning. These innovations have enhanc...
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