AIMC Topic: Organs at Risk

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Impact of nominal photon energies on normal tissue sparing in knowledge-based radiotherapy treatment planning for rectal cancer patients.

PloS one Mar 7, 2019
The interactive adjustment of the optimization objectives during the treatment planning process has made it difficult to evaluate the impact of beam quality exclusively in radiotherapy. Without consensus in the published results, the arbitrary select...
Organs at Risk Humans Radiotherapy Planning, Computer-Assisted Retrospective Studies Radiotherapy Dosage Male Organ Sparing Treatments Aged Female Radiotherapy, Intensity-Modulated Middle Aged Photons Rectal Neoplasms Aged, 80 and over Knowledge Bases Adult
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Deep Learning-Based Delineation of Head and Neck Organs at Risk: Geometric and Dosimetric Evaluation.

International journal of radiation oncology, biology, physics Mar 2, 2019
PURPOSE: Organ-at-risk (OAR) delineation is a key step in treatment planning but can be time consuming, resource intensive, subject to variability, and dependent on anatomical knowledge. We studied deep learning (DL) for automated delineation of mult...
Head and Neck Neoplasms Esophagus Benchmarking Mouth Humans Larynx Deep Learning Submandibular Gland Brain Stem Esophageal Sphincter, Upper Parotid Gland Organs at Risk Radiotherapy Planning, Computer-Assisted Pharyngeal Muscles
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Automated noncoplanar treatment planning strategy in stereotactic radiosurgery of multiple cranial metastases: HyperArc and CyberKnife dose distributions.

Medical dosimetry : official journal of the American Association of Medical Dosimetrists Mar 1, 2019
The purpose of this study was to evaluate and compare the dosimetric effects of HyperArc-based stereotactic radiosurgery (SRS) and a robotic radiosurgery system-based planning using CyberKnife for multiple cranial metastases. In total, 11 cancer pati...
Organs at Risk Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Radiosurgery Radiometry Male Middle Aged Magnetic Resonance Imaging Robotics Female Aged Brain Neoplasms Humans Algorithms
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Dose evaluation of MRI-based synthetic CT generated using a machine learning method for prostate cancer radiotherapy.

Medical dosimetry : official journal of the American Association of Medical Dosimetrists Feb 1, 2019
Magnetic resonance imaging (MRI)-only radiotherapy treatment planning is attractive since MRI provides superior soft tissue contrast over computed tomographies (CTs), without the ionizing radiation exposure. However, it requires the generation of a s...
Magnetic Resonance Imaging Prostatic Neoplasms Organs at Risk Radiotherapy Planning, Computer-Assisted Retrospective Studies Tomography, X-Ray Computed Male Radiotherapy Dosage Radiotherapy, Intensity-Modulated Humans Machine Learning
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Prediction of skin dose in low-kV intraoperative radiotherapy using machine learning models trained on results of in vivo dosimetry.

Medical physics Jan 25, 2019
PURPOSE: The purpose of this study was to implement a machine learning model to predict skin dose from targeted intraoperative (TARGIT) treatment resulting in timely adoption of strategies to limit excessive skin dose.
Machine Learning Female Aged Aged, 80 and over Humans Neural Networks, Computer Intraoperative Care Mastectomy, Segmental In Vivo Dosimetry Models, Statistical Skin Adult Organs at Risk Breast Neoplasms Radiotherapy Dosage Middle Aged
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Dosimetric features-driven machine learning model for DVH prediction in VMAT treatment planning.

Medical physics Jan 2, 2019
PURPOSE: Few features characterizing the dosimetric properties of the patients are included in currently available dose-volume histogram (DVH) prediction models, making it intractable to build a correlative relationship between the input and output p...
Radiometry Machine Learning Organs at Risk Quality Control Radiotherapy Planning, Computer-Assisted Radiotherapy, Intensity-Modulated
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Knowledge-based dose prediction models for head and neck cancer are strongly affected by interorgan dependency and dataset inconsistency.

Medical physics Dec 24, 2018
PURPOSE: The goal of this study was to generate a large treatment plan database for head and neck (H&N) cancer patients that can be considered as the gold standard to train and validate models for knowledge-based (KB) treatment planning and QA. With ...
Image Processing, Computer-Assisted Radiotherapy Dosage Models, Theoretical Radiotherapy, Intensity-Modulated Humans Databases, Factual Head and Neck Neoplasms Tomography, X-Ray Computed Organs at Risk Knowledge Bases Radiotherapy Planning, Computer-Assisted Quality Assurance, Health Care
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Multi-Institutional Validation of a Knowledge-Based Planning Model for Patients Enrolled in RTOG 0617: Implications for Plan Quality Controls in Cooperative Group Trials.

Practical radiation oncology Dec 15, 2018
PURPOSE: This study aimed to evaluate the feasibility of using a single-institution, knowledge-based planning (KBP) model as a dosimetric plan quality control (QC) for multi-institutional clinical trials. The efficacy of this QC tool was retrospectiv...
Knowledge Bases Organs at Risk Quality Control Lung Neoplasms Radiotherapy Planning, Computer-Assisted Humans Carcinoma, Non-Small-Cell Lung Dose Fractionation, Radiation Software Feasibility Studies Radiometry Decision Support Systems, Clinical Models, Biological
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Deep-learning based surface region selection for deep inspiration breath hold (DIBH) monitoring in left breast cancer radiotherapy.

Physics in medicine and biology Dec 12, 2018
Deep inspiration breath hold (DIBH) with surface supervising is a common technique for cardiac dose reduction in left breast cancer radiotherapy. Surface supervision accuracy relies on the characteristics of surface region. In this study, a convoluti...
Organs at Risk Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Breath Holding Female Deep Learning Monitoring, Physiologic Unilateral Breast Neoplasms Humans
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Technical Note: More accurate and efficient segmentation of organs-at-risk in radiotherapy with convolutional neural networks cascades.

Medical physics Dec 7, 2018
PURPOSE: Manual delineation of organs-at-risk (OARs) in radiotherapy is both time-consuming and subjective. Automated and more accurate segmentation is of the utmost importance in clinical application. The purpose of this study is to further improve ...
Time Factors Image Processing, Computer-Assisted Organs at Risk Radiotherapy Planning, Computer-Assisted Neural Networks, Computer Tomography, X-Ray Computed
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