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Organs at Risk

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Assessment of clinical feasibility:offline adaptive radiotherapy for lung cancer utilizing kV iCBCT and UNet++ based deep learning model.

Journal of applied clinical medical physics 39611881
BACKGROUND: Lung cancer poses a significant global health challenge. Adaptive radiotherapy (ART) addresses uncertainties due to lung tumor dynamics. We aimed to investigate a comprehensively and systematically validated offline ART regimen with high ...
Aged Cone-Beam Computed Tomography Deep Learning Feasibility Studies Female Humans Image Processing, Computer-Assisted Lung Neoplasms Male Middle Aged Organs at Risk Radiotherapy Dosage Radiotherapy Planning, Computer-Assisted Radiotherapy, Intensity-Modulated
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Evaluation and comparison of synthetic computed tomography algorithms with 3T MRI for prostate radiotherapy: AI-based versus bulk density method.

Journal of applied clinical medical physics 39611806
PURPOSE: Synthetic computed tomography (sCT)-algorithms, which generate computed tomography images from magnetic resonance imaging data, are becoming part of the clinical radiotherapy workflow. The aim of this retrospective study was to evaluate and ...
Algorithms Artificial Intelligence Humans Image Processing, Computer-Assisted Magnetic Resonance Imaging Male Organs at Risk Prostatic Neoplasms Radiotherapy Dosage Radiotherapy Planning, Computer-Assisted Radiotherapy, Intensity-Modulated Retrospective Studies Tomography, X-Ray Computed
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Dose prediction of CyberKnife Monte Carlo plan for lung cancer patients based on deep learning: robust learning of variable beam configurations.

Radiation oncology (London, England) 39587661
BACKGROUND: Accurate calculation of lung cancer dose using the Monte Carlo (MC) algorithm in CyberKnife (CK) is essential for precise planning. We aim to employ deep learning to directly predict the 3D dose distribution calculated by the MC algorithm...
Algorithms Deep Learning Humans Lung Neoplasms Monte Carlo Method Organs at Risk Radiosurgery Radiotherapy Dosage Radiotherapy Planning, Computer-Assisted Radiotherapy, Intensity-Modulated
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Deep learning-based multiple-CT optimization: An adaptive treatment planning approach to account for anatomical changes in intensity-modulated proton therapy for head and neck cancers.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology 39581351
BACKGROUNDS: Intensity-modulated proton therapy (IMPT) is particularly susceptible to range and setup uncertainties, as well as anatomical changes.
Algorithms Cone-Beam Computed Tomography Deep Learning Head and Neck Neoplasms Humans Organs at Risk Proton Therapy Radiotherapy Dosage Radiotherapy Planning, Computer-Assisted Radiotherapy, Intensity-Modulated Retrospective Studies
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Artificial intelligence contouring in radiotherapy for organs-at-risk and lymph node areas.

Radiation oncology (London, England) 39574153
INTRODUCTION: The delineation of organs-at-risk and lymph node areas is a crucial step in radiotherapy, but it is time-consuming and associated with substantial user-dependent variability in contouring. Artificial intelligence (AI) appears to be the ...
Adult Artificial Intelligence Child Female Humans Lymph Nodes Magnetic Resonance Imaging Male Organs at Risk Radiotherapy Planning, Computer-Assisted Software Tomography, X-Ray Computed
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Attention 3D UNET for dose distribution prediction of high-dose-rate brachytherapy of cervical cancer: Intracavitary applicators.

Journal of applied clinical medical physics 39545816
BACKGROUND: Formulating a clinically acceptable plan within the time-constrained clinical setting of brachytherapy poses challenges to clinicians. Deep learning based dose prediction methods have shown favorable solutions for enhancing efficiency, bu...
Algorithms Brachytherapy Deep Learning Female Humans Imaging, Three-Dimensional Organs at Risk Radiotherapy Dosage Radiotherapy Planning, Computer-Assisted Radiotherapy, Intensity-Modulated Retrospective Studies Uterine Cervical Neoplasms
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Breast radiotherapy planning: A decision-making framework using deep learning.

Medical physics 39625151
BACKGROUND: Effective breast cancer treatment planning requires balancing tumor control while minimizing radiation exposure to healthy tissues. Choosing between intensity-modulated radiation therapy (IMRT) and three-dimensional conformal radiation th...
Breast Neoplasms Decision Making Deep Learning Female Humans Organs at Risk Radiotherapy Dosage Radiotherapy Planning, Computer-Assisted Radiotherapy, Conformal Radiotherapy, Intensity-Modulated Retrospective Studies
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Quality and mechanical efficiency of automated knowledge-based planning for volumetric-modulated arc therapy in head and neck cancer.

Journal of applied clinical medical physics 39616630
OBJECTIVES: This study aimed to examine the effectiveness of the automated RapidPlan in assessing plan quality and to explore how beam complexity affects the mechanical performance of volumetric modulated arc therapy for head and neck cancers.
Algorithms Head and Neck Neoplasms Humans Knowledge Bases Organs at Risk Quality Assurance, Health Care Radiotherapy Dosage Radiotherapy Planning, Computer-Assisted Radiotherapy, Intensity-Modulated
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Evaluating the dosimetric impact of deep-learning-based auto-segmentation in prostate cancer radiotherapy: Insights into real-world clinical implementation and inter-observer variability.

Journal of applied clinical medical physics 39616629
PURPOSE: This study aimed to investigate the dosimetric impact of deep-learning-based auto-contouring for clinical target volume (CTV) and organs at risk (OARs) delineation in prostate cancer radiotherapy planning. Additionally, we compared the geome...
Deep Learning Feasibility Studies Humans Imaging, Three-Dimensional Male Observer Variation Organs at Risk Prostate Prostatic Neoplasms Radiometry Radiotherapy Dosage Radiotherapy Planning, Computer-Assisted Radiotherapy, Intensity-Modulated Retrospective Studies Tomography, X-Ray Computed
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Automated treatment planning with deep reinforcement learning for head-and-neck (HN) cancer intensity modulated radiation therapy (IMRT).

Physics in medicine and biology 39577088
To develop a deep reinforcement learning (DRL) agent to self-interact with the treatment planning system to automatically generate intensity modulated radiation therapy (IMRT) treatment plans for head-and-neck (HN) cancer with consistent organ-at-ris...
Automation Deep Learning Head and Neck Neoplasms Humans Organs at Risk Radiotherapy Dosage Radiotherapy Planning, Computer-Assisted Radiotherapy, Intensity-Modulated
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