AIMC Topic: Radiotherapy, Intensity-Modulated

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Predicting voxel-level dose distributions for esophageal radiotherapy using densely connected network with dilated convolutions.

Physics in medicine and biology Oct 21, 2020
This work aims to develop a voxel-level dose prediction framework by integrating distance information between PTV and OARs, as well as image information, into a densely-connected network (DCNN). Firstly, a four-channel feature map, consisting of a PT...
Organs at Risk Humans Radiotherapy Planning, Computer-Assisted Neural Networks, Computer Radiotherapy Dosage Radiotherapy, Intensity-Modulated Tomography, X-Ray Computed Image Processing, Computer-Assisted Esophageal Neoplasms
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A deep learning approach to generate synthetic CT in low field MR-guided adaptive radiotherapy for abdominal and pelvic cases.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology Oct 17, 2020
PURPOSE: Artificial intelligence (AI) can play a significant role in Magnetic Resonance guided Radiotherapy (MRgRT), especially to speed up the online adaptive workflow. The aim of this study is to set up a Deep Learning (DL) approach able to generat...
Magnetic Resonance Imaging Pelvis Radiotherapy Planning, Computer-Assisted Artificial Intelligence Radiotherapy Dosage Tomography, X-Ray Computed Radiotherapy, Intensity-Modulated Deep Learning Humans Abdomen
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A deep learning model to predict dose-volume histograms of organs at risk in radiotherapy treatment plans.

Medical physics Oct 15, 2020
PURPOSE: To develop a deep learning-based model to predict achievable dose-volume histograms (DVHs) of organs at risk (OARs) for automation of inverse planning.
Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Radiotherapy, Intensity-Modulated Nasopharyngeal Neoplasms Humans Deep Learning Organs at Risk
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Verification of the machine delivery parameters of a treatment plan via deep learning.

Physics in medicine and biology Sep 30, 2020
We developed a generative adversarial network (GAN)-based deep learning approach to estimate the multileaf collimator (MLC) aperture and corresponding monitor units (MUs) from a given 3D dose distribution. The proposed design of the adversarial netwo...
Lung Neoplasms Rectal Neoplasms Radiotherapy Planning, Computer-Assisted Phantoms, Imaging Nasopharyngeal Neoplasms Radiotherapy Dosage Radiotherapy, Intensity-Modulated Humans Reproducibility of Results Deep Learning
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Multi-view radiomics and dosiomics analysis with machine learning for predicting acute-phase weight loss in lung cancer patients treated with radiotherapy.

Physics in medicine and biology Sep 28, 2020
We propose a multi-view data analysis approach using radiomics and dosiomics (R&D) texture features for predicting acute-phase weight loss (WL) in lung cancer radiotherapy. Baseline weight of 388 patients who underwent intensity modulated radiation t...
Retrospective Studies Tomography, X-Ray Computed Female Aged Humans Machine Learning Algorithms Middle Aged Weight Loss Lung Neoplasms Radiographic Image Interpretation, Computer-Assisted Aged, 80 and over Adult Male Radiotherapy, Intensity-Modulated Acute-Phase Reaction
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Automatic IMRT planning via static field fluence prediction (AIP-SFFP): a deep learning algorithm for real-time prostate treatment planning.

Physics in medicine and biology Sep 8, 2020
The purpose of this work was to develop a deep learning (DL) based algorithm, Automatic intensity-modulated radiotherapy (IMRT) Planning via Static Field Fluence Prediction (AIP-SFFP), for automated prostate IMRT planning with real-time planning effi...
Automation Humans Radiotherapy Dosage Male Radiotherapy, Intensity-Modulated Organs at Risk Deep Learning Radiotherapy Planning, Computer-Assisted Prostatic Neoplasms
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Deep learning-based metal artifact reduction using cycle-consistent adversarial network for intensity-modulated head and neck radiation therapy treatment planning.

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) Sep 7, 2020
PURPOSE: To develop a deep learning-based metal artifact reduction (DL-MAR) method using unpaired data and to evaluate its dosimetric impact in head and neck intensity-modulated radiation therapy (IMRT) compared with the water density override method...
Artifacts Radiotherapy, Intensity-Modulated Tomography, X-Ray Computed Deep Learning Metals Humans Radiotherapy Planning, Computer-Assisted Algorithms
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Cone-beam computed tomography-based radiomics in prostate cancer: a mono-institutional study.

Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] Sep 1, 2020
PURPOSE: The purpose of the reported study was to investigate the value of cone-beam computed tomography (CBCT)-based radiomics for risk stratification and prediction of biochemical relapse in prostate cancer.
Male Middle Aged ROC Curve Computational Biology Logistic Models Cone-Beam Computed Tomography Image Processing, Computer-Assisted Machine Learning Aged Radiotherapy, Intensity-Modulated Aged, 80 and over Neoplasm Staging Humans Neoplasm Grading Prostate-Specific Antigen Adenocarcinoma Radiotherapy Planning, Computer-Assisted Area Under Curve Prostatic Neoplasms
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Comparison of CBCT-based dose calculation methods in head and neck cancer radiotherapy: from Hounsfield unit to density calibration curve to deep learning.

Medical physics Aug 11, 2020
PURPOSE: Anatomical variations occur during head and neck (H&N) radiotherapy treatment. kV cone-beam computed tomography (CBCT) images can be used for daily dose monitoring to assess dose variations owing to anatomic changes. Deep learning methods (D...
Calibration Deep Learning Radiotherapy Dosage Humans Head and Neck Neoplasms Cone-Beam Computed Tomography Radiotherapy, Intensity-Modulated Spiral Cone-Beam Computed Tomography Radiation Oncology Phantoms, Imaging Radiotherapy Planning, Computer-Assisted
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A knowledge-based intensity-modulated radiation therapy treatment planning technique for locally advanced nasopharyngeal carcinoma radiotherapy.

Radiation oncology (London, England) Aug 3, 2020
BACKGROUND: To investigate the feasibility of a knowledge-based automated intensity-modulated radiation therapy (IMRT) planning technique for locally advanced nasopharyngeal carcinoma (NPC) radiotherapy.
Radiotherapy Dosage Knowledge Bases Nasopharyngeal Neoplasms Radiotherapy, Intensity-Modulated Organs at Risk Humans Nasopharyngeal Carcinoma Machine Learning Radiotherapy Planning, Computer-Assisted Retrospective Studies
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