AIMC Topic: Organs at Risk

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Clinical implementation and evaluation of deep learning-assisted automatic radiotherapy treatment planning for lung cancer.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) Aug 2, 2024
PURPOSE: The purpose of the study is to investigate the clinical application of deep learning (DL)-assisted automatic radiotherapy planning for lung cancer.
Radiotherapy Planning, Computer-Assisted Automation Radiotherapy Dosage Radiotherapy, Intensity-Modulated Humans Deep Learning Retrospective Studies Lung Neoplasms Organs at Risk
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Deep-learning-based segmentation using individual patient data on prostate cancer radiation therapy.

PloS one Jul 31, 2024
PURPOSE: Organ-at-risk segmentation is essential in adaptive radiotherapy (ART). Learning-based automatic segmentation can reduce committed labor and accelerate the ART process. In this study, an auto-segmentation model was developed by employing ind...
Deep Learning Prostate Urinary Bladder Image Processing, Computer-Assisted Organs at Risk Humans Prostatic Neoplasms Radiotherapy Planning, Computer-Assisted Tomography, X-Ray Computed Male
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Deep learning-based voxel sampling for particle therapy treatment planning.

Physics in medicine and biology Jul 19, 2024
Scanned particle therapy often requires complex treatment plans, robust optimization, as well as treatment adaptation. Plan optimization is especially complicated for heavy ions due to the variable relative biological effectiveness. We present a nove...
Heavy Ion Radiotherapy Head and Neck Neoplasms Organs at Risk Radiotherapy Planning, Computer-Assisted Humans Deep Learning
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Synthetic CT generation for pelvic cases based on deep learning in multi-center datasets.

Radiation oncology (London, England) Jul 9, 2024
BACKGROUND AND PURPOSE: To investigate the feasibility of synthesizing computed tomography (CT) images from magnetic resonance (MR) images in multi-center datasets using generative adversarial networks (GANs) for rectal cancer MR-only radiotherapy.
Magnetic Resonance Imaging Pelvis Feasibility Studies Middle Aged Deep Learning Image Processing, Computer-Assisted Radiotherapy Dosage Rectal Neoplasms Organs at Risk Radiotherapy Planning, Computer-Assisted Humans Tomography, X-Ray Computed Female Aged Adult Male
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Localized fine-tuning and clinical evaluation of deep-learning based auto-segmentation (DLAS) model for clinical target volume (CTV) and organs-at-risk (OAR) in rectal cancer radiotherapy.

Radiation oncology (London, England) Jul 2, 2024
BACKGROUND AND PURPOSE: Various deep learning auto-segmentation (DLAS) models have been proposed, some of which have been commercialized. However, the issue of performance degradation is notable when pretrained models are deployed in the clinic. This...
Humans Retrospective Studies Tomography, X-Ray Computed Rectal Neoplasms Organs at Risk Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Male Radiotherapy, Intensity-Modulated Female Middle Aged Aged Deep Learning Adult
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From plan to delivery: Machine learning based positional accuracy prediction of multi-leaf collimator and estimation of delivery effect in volumetric modulated arc therapy.

Journal of applied clinical medical physics Jun 20, 2024
PURPOSE: The positional accuracy of MLC is an important element in establishing the exact dosimetry in VMAT. We comprehensively analyzed factors that may affect MLC positional accuracy in VMAT, and constructed a model to predict MLC positional deviat...
Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Algorithms Radiotherapy, Intensity-Modulated Humans Neoplasms Particle Accelerators Machine Learning Organs at Risk
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Comparison of the use of a clinically implemented deep learning segmentation model with the simulated study setting for breast cancer patients receiving radiotherapy.

Acta oncologica (Stockholm, Sweden) Jun 20, 2024
BACKGROUND: Deep learning (DL) models for auto-segmentation in radiotherapy have been extensively studied in retrospective and pilot settings. However, these studies might not reflect the clinical setting. This study compares the use of a clinically ...
Pilot Projects Organs at Risk Radiotherapy Planning, Computer-Assisted Breast Neoplasms Middle Aged Retrospective Studies Deep Learning Female Humans
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UDBRNet: A novel uncertainty driven boundary refined network for organ at risk segmentation.

PloS one Jun 17, 2024
Organ segmentation has become a preliminary task for computer-aided intervention, diagnosis, radiation therapy, and critical robotic surgery. Automatic organ segmentation from medical images is a challenging task due to the inconsistent shape and siz...
Image Processing, Computer-Assisted Humans Algorithms Organs at Risk Neural Networks, Computer Tomography, X-Ray Computed Uncertainty Lung
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Evaluation of multiple-vendor AI autocontouring solutions.

Radiation oncology (London, England) May 31, 2024
BACKGROUND: Multiple artificial intelligence (AI)-based autocontouring solutions have become available, each promising high accuracy and time savings compared with manual contouring. Before implementing AI-driven autocontouring into clinical practice...
Tomography, X-Ray Computed Humans Image Processing, Computer-Assisted Artificial Intelligence Organs at Risk Algorithms Radiotherapy Planning, Computer-Assisted
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Evaluation of an automated clinical decision system with deep learning dose prediction and NTCP model for prostate cancer proton therapy.

Physics in medicine and biology May 30, 2024
To evaluate the feasibility of using a deep learning dose prediction approach to identify patients who could benefit most from proton therapy based on the normal tissue complication probability (NTCP) model.Two 3D UNets were established to predict ph...
Radiotherapy Planning, Computer-Assisted Probability Automation Radiation Dosage Radiotherapy Dosage Proton Therapy Decision Support Systems, Clinical Prostatic Neoplasms Organs at Risk Humans Male Deep Learning Uncertainty
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