AIMC Topic: Radiotherapy Planning, Computer-Assisted

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Deep Convolution Neural Network (DCNN) Multiplane Approach to Synthetic CT Generation From MR images-Application in Brain Proton Therapy.

International journal of radiation oncology, biology, physics Jul 2, 2019
PURPOSE: The first aim of this work is to present a novel deep convolution neural network (DCNN) multiplane approach and compare it to single-plane prediction of synthetic computed tomography (sCT) by using the real computed tomography (CT) as ground...
Multimodal Imaging Glioblastoma Feasibility Studies Radiotherapy Planning, Computer-Assisted Reproducibility of Results Brain Neoplasms Magnetic Resonance Imaging Skull Proton Therapy Radiotherapy, Image-Guided Technology, Radiologic Head Radiotherapy Dosage Air Algorithms Tomography, X-Ray Computed Humans Neural Networks, Computer
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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 Dosage Radiotherapy, Intensity-Modulated Radiometry Male Prostatic Neoplasms Humans Organs at Risk Neural Networks, Computer Radiotherapy Planning, Computer-Assisted
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Three-dimensional dose prediction for lung IMRT patients with deep neural networks: robust learning from heterogeneous beam configurations.

Medical physics Jun 17, 2019
PURPOSE: The use of neural networks to directly predict three-dimensional dose distributions for automatic planning is becoming popular. However, the existing methods use only patient anatomy as input and assume consistent beam configuration for all ...
Lung Neoplasms Radiotherapy Planning, Computer-Assisted Radiation Dosage Radiotherapy Dosage Humans Deep Learning Radiotherapy, Intensity-Modulated
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A Deep Learning Model for Predicting Xerostomia Due to Radiation Therapy for Head and Neck Squamous Cell Carcinoma in the RTOG 0522 Clinical Trial.

International journal of radiation oncology, biology, physics Jun 13, 2019
PURPOSE: Xerostomia commonly occurs in patients who undergo head and neck radiation therapy and can seriously affect patients' quality of life. In this study, we developed a xerostomia prediction model with radiation treatment data using a 3-dimensio...
Submandibular Gland Xerostomia Hypopharyngeal Neoplasms Parotid Gland Radiotherapy Planning, Computer-Assisted Radiotherapy, Conformal Pharyngeal Neoplasms Oropharyngeal Neoplasms Radiotherapy, Image-Guided Laryngeal Neoplasms Logistic Models Squamous Cell Carcinoma of Head and Neck Area Under Curve Tomography, X-Ray Computed Deep Learning ROC Curve Humans Retrospective Studies
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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.
Deep Learning Radiotherapy Planning, Computer-Assisted Radiation Dosage Radiotherapy Dosage Radiotherapy, Intensity-Modulated
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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.
Datasets as Topic False Negative Reactions Humans False Positive Reactions Dose Fractionation, Radiation Bayes Theorem Technology, Radiologic Neural Networks, Computer Radiotherapy Dosage Neoplasms Radiologists Radiotherapy, Image-Guided Organ Specificity Algorithms Simulation Training Cohort Studies Radiotherapy Planning, Computer-Assisted ROC Curve Medical Errors Radiotherapy Setup Errors
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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 ...
Uterine Cervical Neoplasms Deep Learning Radiotherapy Planning, Computer-Assisted Brachytherapy Algorithms Humans Radiotherapy Dosage Female
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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 Humans Radiotherapy Dosage Particle Accelerators 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.
Radiotherapy Dosage Radiotherapy, Intensity-Modulated Bone and Bones Deep Learning Radiotherapy, Image-Guided Humans Magnetic Resonance Imaging Radiotherapy Planning, Computer-Assisted Tomography, X-Ray Computed Brain Neoplasms
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Towards real-time respiratory motion prediction based on long short-term memory neural networks.

Physics in medicine and biology Apr 10, 2019
Radiation therapy of thoracic and abdominal tumors requires incorporating the respiratory motion into treatments. To precisely account for the patient's respiratory motions and predict the respiratory signals, a generalized model for predictions of d...
Humans Abdominal Neoplasms Motion Neural Networks, Computer Radiotherapy Planning, Computer-Assisted Thoracic Neoplasms Respiration
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