AIMC Topic: Organs at Risk

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Comparing deep learning-based auto-segmentation of organs at risk and clinical target volumes to expert inter-observer variability in radiotherapy planning.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology Dec 5, 2019
BACKGROUND: Deep learning-based auto-segmented contours (DC) aim to alleviate labour intensive contouring of organs at risk (OAR) and clinical target volumes (CTV). Most previous DC validation studies have a limited number of expert observers for com...
Head and Neck Neoplasms Organs at Risk Radiotherapy Planning, Computer-Assisted Observer Variation Humans Male Deep Learning
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Comparative clinical evaluation of atlas and deep-learning-based auto-segmentation of organ structures in liver cancer.

Radiation oncology (London, England) Nov 27, 2019
BACKGROUND: Accurate and standardized descriptions of organs at risk (OARs) are essential in radiation therapy for treatment planning and evaluation. Traditionally, physicians have contoured patient images manually, which, is time-consuming and subje...
Tomography, X-Ray Computed Republic of Korea Female Reproducibility of Results Humans Neural Networks, Computer Organs at Risk Radiotherapy Planning, Computer-Assisted Observer Variation Deep Learning Aged Radiotherapy Liver Neoplasms Male Image Processing, Computer-Assisted Middle Aged
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Winter is over: The use of Artificial Intelligence to individualise radiation therapy for breast cancer.

Breast (Edinburgh, Scotland) Nov 26, 2019
Artificial intelligence demonstrated its value for automated contouring of organs at risk and target volumes as well as for auto-planning of radiation dose distributions in terms of saving time, increasing consistency, and improving dose-volumes para...
Female Breast Neoplasms Organs at Risk Radiotherapy Planning, Computer-Assisted Humans Artificial Intelligence Precision Medicine Radiotherapy Dosage
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A simple knowledge-based tool for stereotactic radiosurgery pre-planning.

Journal of applied clinical medical physics Nov 19, 2019
We studied the dosimetry of single-isocenter treatment plans generated to treat a solitary intracranial lesion using linac-based stereotactic radiosurgery (SRS). A common metric for evaluating SRS plan quality is the volume of normal brain tissue irr...
Radiotherapy Planning, Computer-Assisted Algorithms Radiotherapy Dosage Retrospective Studies Brain Neoplasms Radiotherapy, Intensity-Modulated Humans Knowledge Bases Radiosurgery Organs at Risk
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Improving automatic delineation for head and neck organs at risk by Deep Learning Contouring.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology Oct 22, 2019
INTRODUCTION: Adequate head and neck (HN) organ-at-risk (OAR) delineation is crucial for HN radiotherapy and for investigating the relationships between radiation dose to OARs and radiation-induced side effects. The automatic contouring algorithms th...
Radiotherapy Planning, Computer-Assisted Observer Variation Neck Neoplasm Staging Organs at Risk Young Adult Adult Male Neural Networks, Computer Adolescent Middle Aged Algorithms Head and Neck Neoplasms Humans Deep Learning Female Aged
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Synthetic MRI-aided multi-organ segmentation on male pelvic CT using cycle consistent deep attention network.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology Oct 17, 2019
BACKGROUND AND PURPOSE: Manual contouring is labor intensive, and subject to variations in operator knowledge, experience and technique. This work aims to develop an automated computed tomography (CT) multi-organ segmentation method for prostate canc...
Tomography, X-Ray Computed Male Deep Learning Pelvis Organs at Risk Magnetic Resonance Imaging Prostatic Neoplasms Humans
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Future of Radiotherapy in Nasopharyngeal Carcinoma.

The British journal of radiology Jul 9, 2019
Nasopharyngeal carcinoma (NPC) is a malignancy with unique clinical biological profiles such as associated Epstein-Barr virus infection and high radiosensitivity. Radiotherapy has long been recognized as the mainstay for the treatment of NPC. However...
Proton Therapy Radiation Injuries Nasopharyngeal Carcinoma Nasopharyngeal Neoplasms Molecular Targeted Therapy Radiotherapy, Intensity-Modulated Radiotherapy Forecasting Precision Medicine Immunotherapy Humans Organs at Risk Artificial Intelligence
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Attention-enabled 3D boosted convolutional neural networks for semantic CT segmentation using deep supervision.

Physics in medicine and biology Jul 2, 2019
A deeply supervised attention-enabled boosted convolutional neural network (DAB-CNN) is presented as a superior alternative to current state-of-the-art convolutional neural networks (CNNs) for semantic CT segmentation. Spatial attention gates (AGs) w...
Humans Neural Networks, Computer Male Imaging, Three-Dimensional Algorithms Prostatic Neoplasms Tomography, X-Ray Computed Organs at Risk
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Incorporating dosimetric features into the prediction of 3D VMAT dose distributions using deep convolutional neural network.

Physics in medicine and biology Jun 20, 2019
An accurate prediction of achievable dose distribution on a patient specific basis would greatly improve IMRT/VMAT planning in both efficiency and quality. Recently machine learning techniques have been proposed for IMRT dose prediction based on pati...
Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Humans Male Radiometry Radiotherapy, Intensity-Modulated Neural Networks, Computer Prostatic Neoplasms Organs at Risk
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Benefits of deep learning for delineation of organs at risk in head and neck cancer.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology May 27, 2019
PURPOSE/OBJECTIVE: Precise delineation of organs at risk (OARs) in head and neck cancer (HNC) is necessary for accurate radiotherapy. Although guidelines exist, significant interobserver variability (IOV) remains. The aim was to validate a 3D convolu...
Middle Aged Deep Learning Humans Head and Neck Neoplasms Organs at Risk Neural Networks, Computer Aged Female Aged, 80 and over Male Observer Variation
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