AIMC Topic: Radiotherapy

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Radiomics and deep learning in lung cancer.

Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] May 4, 2020
Lung malignancies have been extensively characterized through radiomics and deep learning. By providing a three-dimensional characterization of the lesion, models based on radiomic features from computed tomography (CT) and positron-emission tomograp...
Computational Biology Treatment Outcome Neoplasm Staging Image Processing, Computer-Assisted Lung Neoplasms Genotype Radiosurgery Radiotherapy Radiation Injuries Imaging Genomics Neoplasm Recurrence, Local Radiotherapy Planning, Computer-Assisted False Positive Reactions Humans Forecasting Phenotype Tomography, X-Ray Computed Deep Learning Databases, Factual Prognosis
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High quality proton portal imaging using deep learning for proton radiation therapy: a phantom study.

Biomedical physics & engineering express Apr 27, 2020
Purpose; For shoot-through proton treatments, like FLASH radiotherapy, there will be protons exiting the patient which can be used for proton portal imaging (PPI), revealing valuable information for the validation of tumor location in the beam's-eye-...
Proton Therapy Protons Radiotherapy Scattering, Radiation Phantoms, Imaging Computer Simulation Reproducibility of Results Neoplasms Nonlinear Dynamics Signal-To-Noise Ratio Deep Learning Image Processing, Computer-Assisted Humans Neural Networks, Computer Algorithms
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Artificial Intelligence in radiotherapy: state of the art and future directions.

Medical oncology (Northwood, London, England) Apr 22, 2020
Recent advances in computing capability allowed the development of sophisticated predictive models to assess complex relationships within observational data, described as Artificial Intelligence. Medicine is one of the several fields of application a...
Humans Artificial Intelligence Radiotherapy Planning, Computer-Assisted Algorithms Radiotherapy Dosage Animals Radiotherapy Neoplasms
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Multi-task learning for the segmentation of organs at risk with label dependence.

Medical image analysis Feb 7, 2020
Automatic segmentation of organs at risk is crucial to aid diagnoses and remains a challenging task in medical image analysis domain. To perform the segmentation, we use multi-task learning (MTL) to accurately determine the contour of organs at risk ...
Humans Thorax Radiotherapy Neural Networks, Computer Tomography, X-Ray Computed Organs at Risk
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A novel deep learning model using dosimetric and clinical information for grade 4 radiotherapy-induced lymphopenia prediction.

Physics in medicine and biology Feb 4, 2020
Radiotherapy-induced lymphopenia has increasingly been shown to reduce cancer survivorship. We developed a novel hybrid deep learning model to efficiently integrate an entire set of dosimetric parameters of a radiation treatment plan with a patient's...
Radiation Injuries Radiometry Lymphopenia Humans Esophageal Neoplasms Radiotherapy Deep Learning Models, Statistical
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An artificial neural network to model response of a radiotherapy beam monitoring system.

Medical physics Feb 3, 2020
PURPOSE: The integral quality monitor (IQM) is a real-time radiotherapy beam monitoring system, which consists of a spatially sensitive large-area ion chamber, mounted at the collimator of the linear accelerator (linac), and a calculation algorithm t...
Uncertainty Particle Accelerators Neural Networks, Computer Radiotherapy
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Comparative clinical evaluation of atlas and deep-learning-based auto-segmentation of organ structures in liver cancer.

Radiation oncology (London, England) Nov 27, 2019
BACKGROUND: Accurate and standardized descriptions of organs at risk (OARs) are essential in radiation therapy for treatment planning and evaluation. Traditionally, physicians have contoured patient images manually, which, is time-consuming and subje...
Reproducibility of Results Aged Male Liver Neoplasms Image Processing, Computer-Assisted Middle Aged Deep Learning Tomography, X-Ray Computed Republic of Korea Female Humans Radiotherapy Neural Networks, Computer Organs at Risk Radiotherapy Planning, Computer-Assisted Observer Variation
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Patient-specific reconstruction of volumetric computed tomography images from a single projection view via deep learning.

Nature biomedical engineering Oct 28, 2019
Tomographic imaging using penetrating waves generates cross-sectional views of the internal anatomy of a living subject. For artefact-free volumetric imaging, projection views from a large number of angular positions are required. Here we show that a...
Neck Radiotherapy Planning, Computer-Assisted Radiotherapy Humans Cone-Beam Computed Tomography Head Biopsy, Needle Lung Imaging, Three-Dimensional Deep Learning Abdomen
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Preparing for Artificial Intelligence: Systems-Level Implications for the Medical Imaging and Radiation Therapy Professions.

Journal of medical imaging and radiation sciences Oct 21, 2019
Innovations in artificial intelligence (AI) are driving a new industrial revolution, and as a result, the medical radiation sciences is experiencing transformational, open, beneficial, yet disruptive changes. Many studies have already been published ...
Radiology Radiotherapy Artificial Intelligence Humans Delivery of Health Care Radiography
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Artificial Intelligence in Radiotherapy: A Philosophical Perspective.

Journal of medical imaging and radiation sciences Oct 4, 2019
The increasing uptake of machine learning solutions for segmentation and planning leaves no doubt that artificial intelligence (AI) will soon be providing input into a range of radiotherapy procedures. Although this promises to deliver increased spee...
Creativity Humans Chickens Artificial Intelligence Radiotherapy Consciousness Patient Safety Philosophy, Medical Animals
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