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Organs at Risk

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3D radiotherapy dose prediction on head and neck cancer patients with a hierarchically densely connected U-net deep learning architecture.

Physics in medicine and biology Mar 18, 2019
The treatment planning process for patients with head and neck (H&N) cancer is regarded as one of the most complicated due to large target volume, multiple prescription dose levels, and many radiation-sensitive critical structures near the target. Tr...
Aged Aged, 80 and over Young Adult Adult Humans Algorithms Organs at Risk Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Radiotherapy, Intensity-Modulated Middle Aged Deep Learning Adolescent Head and Neck Neoplasms
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Dual-energy CT for automatic organs-at-risk segmentation in brain-tumor patients using a multi-atlas and deep-learning approach.

Scientific reports Mar 11, 2019
In radiotherapy, computed tomography (CT) datasets are mostly used for radiation treatment planning to achieve a high-conformal tumor coverage while optimally sparing healthy tissue surrounding the tumor, referred to as organs-at-risk (OARs). Based o...
Tomography, X-Ray Computed Brain Neoplasms Deep Learning Image Processing, Computer-Assisted Humans Magnetic Resonance Imaging Organs at Risk Radiotherapy Planning, Computer-Assisted
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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...
Radiotherapy, Intensity-Modulated Organ Sparing Treatments Photons Knowledge Bases Organs at Risk Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Female Male Aged Middle Aged Humans Aged, 80 and over Rectal Neoplasms Retrospective Studies 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...
Benchmarking Brain Stem Mouth Esophageal Sphincter, Upper Parotid Gland Humans Radiotherapy Planning, Computer-Assisted Organs at Risk Deep Learning Pharyngeal Muscles Head and Neck Neoplasms Larynx Esophagus Submandibular Gland
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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...
Humans Algorithms Female Aged Brain Neoplasms Radiometry Organs at Risk Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Male Radiosurgery Middle Aged Magnetic Resonance Imaging Robotics
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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...
Radiotherapy, Intensity-Modulated Organs at Risk Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Humans Machine Learning Retrospective Studies Tomography, X-Ray Computed Male Magnetic Resonance Imaging Prostatic Neoplasms
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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.
In Vivo Dosimetry Organs at Risk Radiotherapy Dosage Intraoperative Care Mastectomy, Segmental Female Aged Aged, 80 and over Adult Breast Neoplasms Middle Aged Models, Statistical Skin Humans Neural Networks, Computer Machine Learning
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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...
Machine Learning Organs at Risk Quality Control Radiotherapy Planning, Computer-Assisted Radiotherapy, Intensity-Modulated Radiometry
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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 ...
Knowledge Bases Organs at Risk Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Tomography, X-Ray Computed Humans Radiotherapy, Intensity-Modulated Quality Assurance, Health Care Image Processing, Computer-Assisted Databases, Factual Models, Theoretical Head and Neck Neoplasms
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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 Radiotherapy Planning, Computer-Assisted Lung Neoplasms Dose Fractionation, Radiation Humans Carcinoma, Non-Small-Cell Lung Radiometry Software Feasibility Studies Decision Support Systems, Clinical Models, Biological
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