AIMC Topic: Radiotherapy Dosage

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Dose distribution prediction in isodose feature-preserving voxelization domain using deep convolutional neural network.

Medical physics Jun 7, 2019
PURPOSE: To implement a framework for dose prediction using a deep convolutional neural network (CNN) based on the concept of isodose feature-preserving voxelization (IFPV) in simplifying the representation of the dose distribution.
Radiotherapy Planning, Computer-Assisted Radiation Dosage Radiotherapy Dosage Radiotherapy, Intensity-Modulated Deep Learning
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Development and Validation of a Bayesian Network Method to Detect External Beam Radiation Therapy Physician Order Errors.

International journal of radiation oncology, biology, physics May 31, 2019
PURPOSE: To investigate a Bayesian network (BN)-based method to detect errors in external beam radiation therapy physician orders.
Neural Networks, Computer Algorithms Neoplasms Cohort Studies Humans Technology, Radiologic Radiotherapy Dosage Organ Specificity Dose Fractionation, Radiation Medical Errors Radiotherapy Setup Errors Radiotherapy, Image-Guided Simulation Training Radiotherapy Planning, Computer-Assisted Datasets as Topic False Negative Reactions False Positive Reactions ROC Curve Bayes Theorem Radiologists
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Intelligent inverse treatment planning via deep reinforcement learning, a proof-of-principle study in high dose-rate brachytherapy for cervical cancer.

Physics in medicine and biology May 29, 2019
Inverse treatment planning in radiation therapy is formulated as solving optimization problems. The objective function and constraints consist of multiple terms designed for different clinical and practical considerations. Weighting factors of these ...
Female Uterine Cervical Neoplasms Deep Learning Radiotherapy Planning, Computer-Assisted Humans Algorithms Brachytherapy Radiotherapy Dosage
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Predicting VMAT patient-specific QA results using a support vector classifier trained on treatment plan characteristics and linac QC metrics.

Physics in medicine and biology Apr 29, 2019
The use of treatment plan characteristics to predict patient-specific quality assurance (QA) measurement results has recently been reported as a strategy to help facilitate automated pre-treatment verification workflows or to provide a virtual assess...
Machine Learning Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Particle Accelerators Humans Radiotherapy, Intensity-Modulated Patient-Specific Modeling
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MRI-only brain radiotherapy: Assessing the dosimetric accuracy of synthetic CT images generated using a deep learning approach.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology Apr 11, 2019
PURPOSE: This study assessed the dosimetric accuracy of synthetic CT images generated from magnetic resonance imaging (MRI) data for focal brain radiation therapy, using a deep learning approach.
Brain Neoplasms Deep Learning Humans Tomography, X-Ray Computed Radiotherapy, Intensity-Modulated Radiotherapy, Image-Guided Magnetic Resonance Imaging Bone and Bones Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage
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Stochastic frontier analysis as knowledge-based model to improve sparing of organs-at-risk for VMAT-treated prostate cancer.

Physics in medicine and biology Apr 8, 2019
Stochastic frontier analysis (SFA) is used as a novel knowledge-based technique in order to develop a predictive model of dosimetric features from significant geometric parameters describing a patient morphology. 406 patients treated with VMAT for pr...
Organs at Risk Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Radiotherapy, Intensity-Modulated Male Prostatic Neoplasms Knowledge Bases Stochastic Processes Radiometry Humans
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Dosimetric study on learning-based cone-beam CT correction in adaptive radiation therapy.

Medical dosimetry : official journal of the American Association of Medical Dosimetrists Apr 1, 2019
INTRODUCTION: Cone-beam CT (CBCT) image quality is important for its quantitative analysis in adaptive radiation therapy. However, due to severe artifacts, the CBCTs are primarily used for verifying patient setup only so far. We have developed a lear...
Pelvis Organs at Risk Radiotherapy Planning, Computer-Assisted Radiosurgery Radiometry Quality Improvement Radiotherapy Dosage Brain Neoplasms Cone-Beam Computed Tomography Decision Trees Humans Machine Learning Retrospective Studies
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A deep learning- and partial least square regression-based model observer for a low-contrast lesion detection task in CT.

Medical physics Apr 1, 2019
PURPOSE: This work aims to develop a new framework of image quality assessment using deep learning-based model observer (DL-MO) and to validate it in a low-contrast lesion detection task that involves CT images with patient anatomical background.
Image Processing, Computer-Assisted Models, Statistical Least-Squares Analysis Radiotherapy Dosage Radiotherapy Planning, Computer-Assisted Observer Variation Tomography, X-Ray Computed Deep Learning Liver Neoplasms Neural Networks, Computer Algorithms Retrospective Studies Humans
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A deep learning method for prediction of three-dimensional dose distribution of helical tomotherapy.

Medical physics Mar 30, 2019
PURPOSE: To develop a deep learning method for prediction of three-dimensional (3D) voxel-by-voxel dose distributions of helical tomotherapy (HT).
Image Processing, Computer-Assisted Organs at Risk Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Humans Tomography, X-Ray Computed Deep Learning Radiotherapy, Intensity-Modulated Nasopharyngeal Neoplasms
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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...
Radiotherapy Planning, Computer-Assisted Algorithms Adolescent Radiotherapy Dosage Adult Head and Neck Neoplasms Aged Radiotherapy, Intensity-Modulated Middle Aged Aged, 80 and over Organs at Risk Deep Learning Humans Young Adult
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