AIMC Topic: Radiation Injuries

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Machine-learning based MRI radiomics models for early detection of radiation-induced brain injury in nasopharyngeal carcinoma.

BMC cancer Jun 1, 2020
BACKGROUND: Early radiation-induced temporal lobe injury (RTLI) diagnosis in nasopharyngeal carcinoma (NPC) is clinically challenging, and prediction models of RTLI are lacking. Hence, we aimed to develop radiomic models for early detection of RTLI.
Adult Male Middle Aged ROC Curve Longitudinal Studies Machine Learning Algorithms Retrospective Studies Female Image Interpretation, Computer-Assisted Radiation Injuries Nasopharyngeal Carcinoma Nasopharyngeal Neoplasms Temporal Lobe Magnetic Resonance Imaging Brain Injuries Predictive Value of Tests Aftercare Models, Biological Humans
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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...
Humans Forecasting Phenotype Neoplasm Staging Neoplasm Recurrence, Local Radiotherapy Planning, Computer-Assisted False Positive Reactions Image Processing, Computer-Assisted Lung Neoplasms Genotype Computational Biology Treatment Outcome Databases, Factual Radiosurgery Prognosis Radiotherapy Tomography, X-Ray Computed Radiation Injuries Deep Learning Imaging Genomics
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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...
Humans Deep Learning Radiometry Lymphopenia Models, Statistical Esophageal Neoplasms Radiotherapy Radiation Injuries
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Overlooked pitfalls in multi-class machine learning classification in radiation oncology and how to avoid them.

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) Jan 25, 2020
In radiation oncology, Machine Learning classification publications are typically related to two outcome classes, e.g. the presence or absence of distant metastasis. However, multi-class classification problems also have great clinical relevance, e.g...
Monte Carlo Method Radiation Injuries Radiation Exposure Models, Theoretical Area Under Curve Radiation Oncology Technology Assessment, Biomedical Humans Machine Learning Biomarkers Risk Factors
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Detection and Monitoring of Thermal Lesions Induced by Microwave Ablation Using Ultrasound Imaging and Convolutional Neural Networks.

IEEE journal of biomedical and health informatics Sep 6, 2019
Microwave ablation (MWA) for cancer treatment is frequently monitored by ultrasound (US) B-mode imaging in the clinic, which often fails due to the low intrinsic contrast between the thermal lesion and normal tissue. Deep learning, especially convolu...
Surgery, Computer-Assisted Image Interpretation, Computer-Assisted Microwaves Radiation Injuries Radiofrequency Ablation Animals Swine Deep Learning Ultrasonography Liver Neural Networks, Computer
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Cath Lab Robotics: Paradigm Change in Interventional Cardiology?

Current cardiology reports Aug 31, 2019
PURPOSE OF REVIEW: To review the contemporary evidence for robotic-assisted percutaneous coronary and vascular interventions, discussing its current capabilities, limitations, and potential future applications.
Radiation Injuries Radiation Exposure Cardiology Humans Robotics Treatment Outcome Coronary Angiography Percutaneous Coronary Intervention Coronary Artery Disease Therapy, Computer-Assisted
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Future of Radiotherapy in Nasopharyngeal Carcinoma.

The British journal of radiology Jul 9, 2019
Nasopharyngeal carcinoma (NPC) is a malignancy with unique clinical biological profiles such as associated Epstein-Barr virus infection and high radiosensitivity. Radiotherapy has long been recognized as the mainstay for the treatment of NPC. However...
Humans Nasopharyngeal Carcinoma Nasopharyngeal Neoplasms Proton Therapy Organs at Risk Molecular Targeted Therapy Artificial Intelligence Radiotherapy, Intensity-Modulated Forecasting Radiotherapy Precision Medicine Radiation Injuries Immunotherapy
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Application of a machine learning method to whole brain white matter injury after radiotherapy for nasopharyngeal carcinoma.

Cancer imaging : the official publication of the International Cancer Imaging Society Mar 25, 2019
BACKGROUND: The purpose/aim of this study was to 1) use magnetic resonance diffusion tensor imaging (DTI), fibre bundle/tract-based spatial statistics (TBSS) and machine learning methods to study changes in the white matter (WM) structure and whole b...
Brain Injuries Radiotherapy, Conformal White Matter Dose Fractionation, Radiation Radiation Injuries Nasopharyngeal Carcinoma Diffusion Tensor Imaging Anisotropy Reproducibility of Results Adult Male Middle Aged Humans ROC Curve Machine Learning Brain Female
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Potential of a machine-learning model for dose optimization in CT quality assurance.

European radiology Feb 19, 2019
OBJECTIVES: To evaluate machine learning (ML) to detect chest CT examinations with dose optimization potential for quality assurance in a retrospective, cross-sectional study.
Aged Aged, 80 and over Young Adult Adult Cross-Sectional Studies Humans Radiation Dosage Male Radiography, Thoracic Machine Learning Middle Aged Multidetector Computed Tomography Retrospective Studies Adolescent Radiation Injuries Female Quality Assurance, Health Care Thoracic Diseases
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Intervening Nidal Brain Parenchyma and Risk of Radiation-Induced Changes After Radiosurgery for Brain Arteriovenous Malformation: A Study Using an Unsupervised Machine Learning Algorithm.

World neurosurgery Jan 22, 2019
OBJECTIVE: To assess the sensitivity and specificity of arteriovenous malformation (AVM) nidal component identification and quantification using an unsupervised machine learning algorithm and to evaluate the association between intervening nidal brai...
Male Female Unsupervised Machine Learning Humans Middle Aged Risk Factors Adolescent Radiosurgery Algorithms Aged Child Radiation Injuries Prospective Studies Young Adult Intracranial Arteriovenous Malformations Brain Adult Parenchymal Tissue
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