AIMC Topic: Organs at Risk

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Deep learning-based segmentation of head and neck organs at risk on CBCT images with dosimetric assessment for radiotherapy.

Physics in medicine and biology Mar 26, 2025
Cone beam computed tomography (CBCT) has become an essential tool in head and neck cancer (HNC) radiotherapy (RT) treatment delivery. Automatic segmentation of the organs at risk (OARs) on CBCT can trigger and accelerate treatment replanning but is s...
Humans Deep Learning Cone-Beam Computed Tomography Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Radiometry Radiotherapy, Image-Guided Image Processing, Computer-Assisted Head and Neck Neoplasms Organs at Risk
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A deep learning model based on Mamba for automatic segmentation in cervical cancer brachytherapy.

Scientific reports Mar 24, 2025
This study developed and evaluated an automatic segmentation model based on the Mamba framework (AM-UNet) for rapid and precise delineation of high-risk clinical target volume (HRCTV) and organs at risk (OARs) in cervical cancer brachytherapy. Using ...
Radiotherapy Planning, Computer-Assisted Brachytherapy Radiotherapy Dosage Middle Aged Deep Learning Organs at Risk Aged Adult Uterine Cervical Neoplasms Tomography, X-Ray Computed Humans Female
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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...
Humans Algorithms Retrospective Studies Radiotherapy Planning, Computer-Assisted Deep Learning Radiotherapy Dosage Head and Neck Neoplasms Radiotherapy, Intensity-Modulated Organs at Risk
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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...
Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Humans Radiotherapy, Intensity-Modulated Artificial Intelligence Algorithms Deep Learning Head and Neck Neoplasms Organs at Risk
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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).
Head and Neck Neoplasms Organs at Risk Radiotherapy Planning, Computer-Assisted Male Middle Aged Deep Learning Radiometry Radiotherapy Dosage Radiotherapy, Intensity-Modulated Proton Therapy Humans Female Aged
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Development and validation of a deep reinforcement learning algorithm for auto-delineation of organs at risk in cervical cancer radiotherapy.

Scientific reports Feb 25, 2025
This study was conducted to develop and validate a novel deep reinforcement learning (DRL) algorithm incorporating the segment anything model (SAM) to enhance the accuracy of automatic contouring organs at risk during radiotherapy for cervical cancer...
Female Middle Aged Radiotherapy Planning, Computer-Assisted Humans Adult Deep Learning Organs at Risk Algorithms Reinforcement Machine Learning Uterine Cervical Neoplasms Tomography, X-Ray Computed
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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 ...
Knowledge Bases Organs at Risk Radiotherapy Planning, Computer-Assisted Algorithms Radiotherapy Dosage Humans Retrospective Studies Radiotherapy, Intensity-Modulated Brain Neoplasms Radiosurgery
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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...
Radiotherapy Dosage Radiotherapy, Intensity-Modulated Organs at Risk Radiotherapy Planning, Computer-Assisted Radiation Dosage Humans Retrospective Studies Deep Learning Lung Neoplasms
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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...
Software Algorithms Tomography, X-Ray Computed Deep Learning Image Processing, Computer-Assisted Organs at Risk Humans Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Radiotherapy, Intensity-Modulated
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A deep learning-based peer review method for radiotherapy planning.

Medical physics Feb 11, 2025
BACKGROUND: Quality control (QC) in radiotherapy planning is crucial for ensuring treatment efficacy and patient safety. Traditionally, QC relies on standard indicators and subjective assessments, which may lead to inconsistencies.
Organs at Risk Humans Radiotherapy Dosage Quality Control Peer Review Deep Learning Radiotherapy Planning, Computer-Assisted
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