AIMC Topic: Organs at Risk

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RapidBrachyDL: Rapid Radiation Dose Calculations in Brachytherapy Via Deep Learning.

International journal of radiation oncology, biology, physics May 12, 2020
PURPOSE: Detailed and accurate absorbed dose calculations from radiation interactions with the human body can be obtained with the Monte Carlo (MC) method. However, the MC method can be slow for use in the time-sensitive clinical workflow. The aim of...
Urinary Bladder Iridium Radioisotopes Brachytherapy Radiotherapy Dosage Monte Carlo Method Deep Learning Prostatic Neoplasms Prostate Organs at Risk Radiotherapy Planning, Computer-Assisted Rectum Colon, Sigmoid Female Uterine Cervical Neoplasms Male Humans Neural Networks, Computer Retrospective Studies
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Clinical implementation of MRI-based organs-at-risk auto-segmentation with convolutional networks for prostate radiotherapy.

Radiation oncology (London, England) May 11, 2020
BACKGROUND: Structure delineation is a necessary, yet time-consuming manual procedure in radiotherapy. Recently, convolutional neural networks have been proposed to speed-up and automatise this procedure, obtaining promising results. With the advent ...
Magnetic Resonance Imaging Humans Deep Learning Prostatic Neoplasms Organs at Risk Male Imaging, Three-Dimensional Radiotherapy Planning, Computer-Assisted Image Processing, Computer-Assisted
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Improving accuracy and robustness of deep convolutional neural network based thoracic OAR segmentation.

Physics in medicine and biology Mar 31, 2020
Deep convolutional neural network (DCNN) has shown great success in various medical image segmentation tasks, including organ-at-risk (OAR) segmentation from computed tomography (CT) images. However, most studies use the dataset from the same source(...
Deep Learning Image Processing, Computer-Assisted Organs at Risk Thorax Humans Tomography, X-Ray Computed
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A New Approach for Automated Image Segmentation of Organs at Risk in Cervical Cancer.

Radiology. Imaging cancer Mar 27, 2020
Tomography, X-Ray Computed Female Uterine Cervical Neoplasms Deep Learning Humans Neural Networks, Computer Organs at Risk
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Performance assessment of a new optimization system for robotic SBRT MLC-based plans.

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) Feb 20, 2020
PURPOSE: To assess the performance of a new optimization system, VOLO, for CyberKnife MLC-based SBRT plans in comparison with the existing Sequential optimizer.
Models, Theoretical Humans Radiotherapy Planning, Computer-Assisted Reproducibility of Results Radiotherapy, Intensity-Modulated Robotics Male Algorithms Radiotherapy Dosage Radiosurgery Organs at Risk Neoplasms Female
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Comparison of statistical machine learning models for rectal protocol compliance in prostate external beam radiation therapy.

Medical physics Feb 19, 2020
PURPOSE: Limiting the dose to the rectum can be one of the most challenging aspects of creating a dosimetric external beam radiation therapy (EBRT) plan for prostate cancer treatment. Rectal sparing devices such as hydrogel spacers offer the prospect...
Organs at Risk Radiotherapy, Computer-Assisted Male Prostatic Neoplasms Rectum Humans Machine Learning Tomography, X-Ray Computed
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Multi-task learning for the segmentation of organs at risk with label dependence.

Medical image analysis Feb 7, 2020
Automatic segmentation of organs at risk is crucial to aid diagnoses and remains a challenging task in medical image analysis domain. To perform the segmentation, we use multi-task learning (MTL) to accurately determine the contour of organs at risk ...
Radiotherapy Humans Organs at Risk Thorax Neural Networks, Computer Tomography, X-Ray Computed
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Automatic delineation of the clinical target volume and organs at risk by deep learning for rectal cancer postoperative radiotherapy.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology Feb 7, 2020
BACKGROUND AND PURPOSE: Manual delineation of clinical target volumes (CTVs) and organs at risk (OARs) is time-consuming, and automatic contouring tools lack clinical validation. We aimed to construct and validate the use of convolutional neural netw...
Rectal Neoplasms Humans Organs at Risk Radiotherapy Planning, Computer-Assisted Retrospective Studies Deep Learning
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Predicting 10-Year Risk of End-Organ Complications of Type 2 Diabetes With and Without Metabolic Surgery: A Machine Learning Approach.

Diabetes care Feb 6, 2020
OBJECTIVE: To construct and internally validate prediction models to estimate the risk of long-term end-organ complications and mortality in patients with type 2 diabetes and obesity that can be used to inform treatment decisions for patients and pra...
Humans Machine Learning Retrospective Studies Adolescent Female Time Factors Computer Simulation Organs at Risk Diabetes Mellitus, Type 2 Adult Prognosis Aged Diabetes Complications Middle Aged Aged, 80 and over Follow-Up Studies Male Disease Progression Bariatric Surgery Young Adult
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A deep learning approach to radiation dose estimation.

Physics in medicine and biology Feb 4, 2020
Currently methods for predicting absorbed dose after administering a radiopharmaceutical are rather crude in daily clinical practice. Most importantly, individual tissue density distributions as well as local variations of the concentration of the ra...
Humans Lutetium Reproducibility of Results Radioisotopes Neural Networks, Computer Organs at Risk Neoplasms Tissue Distribution Algorithms Deep Learning Tomography, X-Ray Computed Radiation Dosage Glutamate Carboxypeptidase II Radiopharmaceuticals Monte Carlo Method
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