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Organs at Risk

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Automating the optimization of proton PBS treatment planning for head and neck cancers using policy gradient-based deep reinforcement learning.

Medical physics 39887764
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...
Automation Deep Learning Head and Neck Neoplasms Humans Organs at Risk Proton Therapy Radiotherapy Dosage Radiotherapy Planning, Computer-Assisted Reinforcement Machine Learning
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Deep learning-based synthetic CT for dosimetric monitoring of combined conventional radiotherapy and lattice boost in large lung tumors.

Radiation oncology (London, England) 39844209
PURPOSE: Conventional radiotherapy (CRT) has limited local control and poses a high risk of severe toxicity in large lung tumors. This study aimed to develop an integrated treatment plan that combines CRT with lattice boost radiotherapy (LRT) and mon...
Aged Cone-Beam Computed Tomography Deep Learning Female Humans Lung Neoplasms Male Middle Aged Organs at Risk Radiometry Radiotherapy Dosage Radiotherapy Planning, Computer-Assisted Radiotherapy, Intensity-Modulated Tomography, X-Ray Computed
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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 40000766
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...
Adult Algorithms Deep Learning Female Humans Middle Aged Organs at Risk Radiotherapy Planning, Computer-Assisted Reinforcement Machine Learning Tomography, X-Ray Computed Uterine Cervical Neoplasms
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A knowledge-based planning model to identify fraction-reduction opportunities in brain stereotactic radiotherapy.

Journal of applied clinical medical physics 39985114
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 ...
Algorithms Brain Neoplasms Humans Knowledge Bases Organs at Risk Radiosurgery Radiotherapy Dosage Radiotherapy Planning, Computer-Assisted Radiotherapy, Intensity-Modulated Retrospective Studies
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Dose prediction via deep learning to enhance treatment planning of lung radiotherapy including simultaneous integrated boost techniques.

Medical physics 39967020
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...
Deep Learning Humans Lung Neoplasms Organs at Risk Radiation Dosage Radiotherapy Dosage Radiotherapy Planning, Computer-Assisted Radiotherapy, Intensity-Modulated Retrospective Studies
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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 39946266
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...
Algorithms Deep Learning Humans Image Processing, Computer-Assisted Organs at Risk Radiotherapy Dosage Radiotherapy Planning, Computer-Assisted Radiotherapy, Intensity-Modulated Software Tomography, X-Ray Computed
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A deep learning-based peer review method for radiotherapy planning.

Medical physics 39935240
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.
Deep Learning Humans Organs at Risk Peer Review Quality Control Radiotherapy Dosage Radiotherapy Planning, Computer-Assisted
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The implementation of knowledge-based planning with partial OAR contours for prostate radiotherapy.

Journal of applied clinical medical physics 39917897
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 ...
Algorithms Artificial Intelligence Humans Knowledge Bases Male Organs at Risk Prostatic Neoplasms Radiotherapy Dosage Radiotherapy Planning, Computer-Assisted Radiotherapy, Intensity-Modulated Retrospective Studies
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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 40087841
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...
Algorithms Artificial Intelligence Deep Learning Head and Neck Neoplasms Humans Organs at Risk Radiotherapy Dosage Radiotherapy Planning, Computer-Assisted Radiotherapy, Intensity-Modulated
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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 40068304
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...
Cone-Beam Computed Tomography Deep Learning Head and Neck Neoplasms Humans Image Processing, Computer-Assisted Organs at Risk Radiometry Radiotherapy Dosage Radiotherapy Planning, Computer-Assisted Radiotherapy, Image-Guided
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