AIMC Topic: Radiometry

Clear Filters Showing 111 to 120 of 125 articles

Multicenter development of a deep learning radiomics and dosiomics nomogram to predict radiation pneumonia risk in non-small cell lung cancer.

Scientific reports
Radiation pneumonia (RP) is the most common side effect of chest radiotherapy, and can affect patients' quality of life. This study aimed to establish a combined model of radiomics, dosiomics, deep learning (DL) based on simulated location CT and dos...

RadField3D: a data generator and data format for deep learning in radiation-protection dosimetry for medical applications.

Journal of radiological protection : official journal of the Society for Radiological Protection
In this research work, we present our open-source Geant4-based Monte-Carlo simulation application, called RadField3D, for generating three-dimensional radiation field datasets for dosimetry. Accompanying, we introduce a fast, machine-interpretable da...

Deep Learning-Based Prediction of Radiation Therapy Dose Distributions in Nasopharyngeal Carcinomas: A Preliminary Study Incorporating Multiple Features Including Images, Structures, and Dosimetry.

Technology in cancer research & treatment
Intensity-modulated radiotherapy (IMRT) is currently the most important treatment method for nasopharyngeal carcinoma (NPC). This study aimed to enhance prediction accuracy by incorporating dose information into a deep convolutional neural network (...

Deep Learning for Patient-Specific Quality Assurance: Predicting Gamma Passing Rates for IMRT Based on Delivery Fluence Informed by log Files.

Technology in cancer research & treatment
In this study, we propose a deep learning-based approach to predict Intensity-modulated radiation therapy (IMRT) quality assurance (QA) gamma passing rates using delivery fluence informed by log files. A total of 112 IMRT plans for chest cancers we...

Radiomics, deep learning and early diagnosis in oncology.

Emerging topics in life sciences
Medical imaging, including X-ray, computed tomography (CT), and magnetic resonance imaging (MRI), plays a critical role in early detection, diagnosis, and treatment response prediction of cancer. To ease radiologists' task and help with challenging c...

A Machine learning model trained on dual-energy CT radiomics significantly improves immunotherapy response prediction for patients with stage IV melanoma.

Journal for immunotherapy of cancer
BACKGROUND: To assess the additive value of dual-energy CT (DECT) over single-energy CT (SECT) to radiomics-based response prediction in patients with metastatic melanoma preceding immunotherapy.

CT Dosimetry: What Has Been Achieved and What Remains to Be Done.

Investigative radiology
Radiation dose in computed tomography (CT) has become a hot topic due to an upward trend in the number of CT procedures worldwide and the relatively high doses associated with these procedures. The main aim of this review article is to provide an ove...

Robotic Dispersal Technique for 35 GBq of 140La in an L-polygon Pattern.

Health physics
In 2018, Defence Research and Development Canada, in partnership with Natural Resources Canada, led a field trial of survey and mapping of a large dispersion of radioactivity using Unmanned Aerial Vehicles (UAVs). The intent was to disperse La materi...

[Application of Radiomics in Classification and Prediction of Benign and Malignant Lung Tumors].

Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation
Aiming at the lack of quantitative evaluation methods in clinical diagnosis of lung cancer, a classification and prediction model of lung cancer based on Support Vector Machine (SVM) was constructed by using radiomics method. Firstly, the definition ...

Deep Learning Algorithm for Reducing CT Slice Thickness: Effect on Reproducibility of Radiomic Features in Lung Cancer.

Korean journal of radiology
OBJECTIVE: To retrospectively assess the effect of CT slice thickness on the reproducibility of radiomic features (RFs) of lung cancer, and to investigate whether convolutional neural network (CNN)-based super-resolution (SR) algorithms can improve t...