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Organs at Risk

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Online Adaptive Proton Therapy Facilitated by Artificial Intelligence-Based Autosegmentation in Pencil Beam Scanning Proton Therapy.

International journal of radiation oncology, biology, physics 39307323
PURPOSE: Online adaptive proton therapy (oAPT) is essential to address interfractional anatomical changes in patients receiving pencil beam scanning proton therapy. Artificial intelligence (AI)-based autosegmentation can increase the efficiency and a...
Artificial Intelligence Feasibility Studies Humans Linear Energy Transfer Male Organs at Risk Prostatic Neoplasms Proton Therapy Radiotherapy Dosage Radiotherapy Planning, Computer-Assisted Rectum Retrospective Studies Tomography, X-Ray Computed Workflow
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Deep learning-based prediction of the dose-volume histograms for volumetric modulated arc therapy of left-sided breast cancer.

Medical physics 39291645
BACKGROUND: The advancements in artificial intelligence and computational power have made deep learning an attractive tool for radiotherapy treatment planning. Deep learning has the potential to significantly simplify the trial-and-error process invo...
Breast Neoplasms Deep Learning Female Humans Organs at Risk Radiotherapy Dosage Radiotherapy Planning, Computer-Assisted Radiotherapy, Intensity-Modulated Unilateral Breast Neoplasms
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Evaluation of deep learning based dose prediction in head and neck cancer patients using two different types of input contours.

Journal of applied clinical medical physics 39285649
PURPOSE: This study evaluates deep learning (DL) based dose prediction methods in head and neck cancer (HNC) patients using two types of input contours.
Algorithms Deep Learning Head and Neck Neoplasms Humans Organs at Risk Prognosis Radiotherapy Dosage Radiotherapy Planning, Computer-Assisted Radiotherapy, Intensity-Modulated Software
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Automated confidence estimation in deep learning auto-segmentation for brain organs at risk on MRI for radiotherapy.

Journal of applied clinical medical physics 39284283
PURPOSE: We have built a novel AI-driven QA method called AutoConfidence (ACo), to estimate segmentation confidence on a per-voxel basis without gold standard segmentations, enabling robust, efficient review of automated segmentation (AS). We have de...
Algorithms Brain Neoplasms Deep Learning Glioma Humans Image Processing, Computer-Assisted Magnetic Resonance Imaging Organs at Risk Radiotherapy Dosage Radiotherapy Planning, Computer-Assisted Radiotherapy, Intensity-Modulated Retrospective Studies
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Deep learning-based segmentation for high-dose-rate brachytherapy in cervical cancer using 3D Prompt-ResUNet.

Physics in medicine and biology 39270708
To develop and evaluate a 3D Prompt-ResUNet module that utilized the prompt-based model combined with 3D nnUNet for rapid and consistent autosegmentation of high-risk clinical target volume (HRCTV) and organ at risk (OAR) in high-dose-rate brachyther...
Brachytherapy Deep Learning Female Humans Image Processing, Computer-Assisted Imaging, Three-Dimensional Organs at Risk Radiation Dosage Radiotherapy Dosage Radiotherapy Planning, Computer-Assisted Tomography, X-Ray Computed Uterine Cervical Neoplasms
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OSAIRIS: Lessons Learned From the Hospital-Based Implementation and Evaluation of an Open-Source Deep-Learning Model for Radiotherapy Image Segmentation.

Clinical oncology (Royal College of Radiologists (Great Britain)) 39522322
Several studies report the benefits and accuracy of using autosegmentation for organ at risk (OAR) outlining in radiotherapy treatment planning. Typically, evaluations focus on accuracy metrics, and other parameters such as perceived utility and safe...
Deep Learning Head and Neck Neoplasms Humans Male Organs at Risk Prostatic Neoplasms Radiotherapy Planning, Computer-Assisted
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Deep learning based ultra-low dose fan-beam computed tomography image enhancement algorithm: Feasibility study in image quality for radiotherapy.

Journal of applied clinical medical physics 39540681
OBJECTIVE: We investigated the feasibility of deep learning-based ultra-low dose kV-fan-beam computed tomography (kV-FBCT) image enhancement algorithm for clinical application in abdominal and pelvic tumor radiotherapy.
Abdominal Neoplasms Algorithms Deep Learning Feasibility Studies Female Humans Image Processing, Computer-Assisted Male Organs at Risk Pelvic Neoplasms Phantoms, Imaging Prospective Studies Radiation Dosage Radiographic Image Enhancement Radiographic Image Interpretation, Computer-Assisted Radiotherapy Dosage Radiotherapy Planning, Computer-Assisted Radiotherapy, Image-Guided Radiotherapy, Intensity-Modulated Signal-To-Noise Ratio Tomography, X-Ray Computed
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Clinical commissioning and introduction of an in-house artificial intelligence (AI) platform for automated head and neck intensity modulated radiation therapy (IMRT) treatment planning.

Journal of applied clinical medical physics 39503512
BACKGROUND AND PURPOSE: To describe the clinical commissioning of an in-house artificial intelligence (AI) treatment planning platform for head-and-neck (HN) Intensity Modulated Radiation Therapy (IMRT).
Algorithms Artificial Intelligence Head and Neck Neoplasms Humans Organs at Risk Radiotherapy Dosage Radiotherapy Planning, Computer-Assisted Radiotherapy, Intensity-Modulated User-Computer Interface
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Clinical target volume (CTV) automatic delineation using deep learning network for cervical cancer radiotherapy: A study with external validation.

Journal of applied clinical medical physics 39401180
PURPOSE: To explore the accuracy and feasibility of a proposed deep learning (DL) algorithm for clinical target volume (CTV) delineation in cervical cancer radiotherapy and evaluate whether it can perform well in external cervical cancer and endometr...
Adult Aged Algorithms Deep Learning Endometrial Neoplasms Female Humans Image Processing, Computer-Assisted Middle Aged Neural Networks, Computer Organs at Risk Prognosis Radiotherapy Dosage Radiotherapy Planning, Computer-Assisted Radiotherapy, Intensity-Modulated Uterine Cervical Neoplasms
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Evaluation of the accuracy of automated segmentation based on deep learning for prostate cancer patients.

Medical dosimetry : official journal of the American Association of Medical Dosimetrists 39384488
PURPOSE: This study evaluated the accuracy of a commercial deep learning (DL)-based algorithm for segmenting the prostate, seminal vesicles (SV), and organs at risk (OAR) in patients with prostate cancer.
Deep Learning Humans Magnetic Resonance Imaging Male Organs at Risk Prostatic Neoplasms Tomography, X-Ray Computed
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