AIMC Topic: Organs at Risk

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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) Nov 25, 2024
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...
Radiotherapy Dosage Humans Radiotherapy, Intensity-Modulated Deep Learning Radiosurgery Algorithms Monte Carlo Method Lung Neoplasms Organs at Risk Radiotherapy Planning, Computer-Assisted
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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 Nov 23, 2024
BACKGROUNDS: Intensity-modulated proton therapy (IMPT) is particularly susceptible to range and setup uncertainties, as well as anatomical changes.
Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Radiotherapy, Intensity-Modulated Proton Therapy Humans Deep Learning Algorithms Cone-Beam Computed Tomography Retrospective Studies Head and Neck Neoplasms Organs at Risk
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Artificial intelligence contouring in radiotherapy for organs-at-risk and lymph node areas.

Radiation oncology (London, England) Nov 21, 2024
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 ...
Humans Artificial Intelligence Software Male Organs at Risk Child Tomography, X-Ray Computed Radiotherapy Planning, Computer-Assisted Lymph Nodes Female Magnetic Resonance Imaging Adult
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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 Nov 15, 2024
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...
Brachytherapy Radiotherapy Dosage Humans Radiotherapy, Intensity-Modulated Imaging, Three-Dimensional Algorithms Retrospective Studies Organs at Risk Female Radiotherapy Planning, Computer-Assisted Uterine Cervical Neoplasms Deep Learning
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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 Nov 14, 2024
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.
Radiotherapy Planning, Computer-Assisted Radiation Dosage Signal-To-Noise Ratio Pelvic Neoplasms Radiographic Image Interpretation, Computer-Assisted Feasibility Studies Organs at Risk Radiotherapy, Image-Guided Radiographic Image Enhancement Radiotherapy Dosage Radiotherapy, Intensity-Modulated Phantoms, Imaging Abdominal Neoplasms Algorithms Prospective Studies Tomography, X-Ray Computed Female Male Deep Learning Image Processing, Computer-Assisted Humans
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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 Nov 6, 2024
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).
Humans Artificial Intelligence Organs at Risk Algorithms Radiotherapy Planning, Computer-Assisted User-Computer Interface Radiotherapy Dosage Head and Neck Neoplasms Radiotherapy, Intensity-Modulated
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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)) Oct 18, 2024
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...
Organs at Risk Radiotherapy Planning, Computer-Assisted Deep Learning Head and Neck Neoplasms Prostatic Neoplasms Humans Male
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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 Oct 14, 2024
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...
Humans Neural Networks, Computer Organs at Risk Algorithms Radiotherapy Planning, Computer-Assisted Prognosis Radiotherapy Dosage Female Radiotherapy, Intensity-Modulated Middle Aged Aged Deep Learning Adult Image Processing, Computer-Assisted Uterine Cervical Neoplasms Endometrial 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 Oct 8, 2024
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 Magnetic Resonance Imaging Prostatic Neoplasms Organs at Risk Humans Tomography, X-Ray Computed Male
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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 Sep 27, 2024
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...
Organs at Risk Uterine Cervical Neoplasms Radiotherapy Planning, Computer-Assisted Brachytherapy Radiation Dosage Humans Deep Learning Radiotherapy Dosage Tomography, X-Ray Computed Imaging, Three-Dimensional Female Image Processing, Computer-Assisted
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