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Conventional MRI cannot be used to identify H3 K27M mutation status. This study aimed to investigate the feasibility of predicting H3 K27M mutation status by applying an automated machine learning (autoML) approach to the MR radiomics features of patients with midline gliomas.
This article was published in the following journal.
Highlighting the risk of biases in radiomics-based models will help improve their quality and increase usage as decision support systems in the clinic. In this study we use machine learning-based meth...
To investigate the predictive capability of machine learning-based multiparametric magnetic resonance (MR) imaging radiomics for evaluating the aggressiveness of papillary thyroid carcinoma (PTC) preo...
This study aimed to investigate the effectiveness of using delta-radiomics to predict overall survival (OS) for patients with recurrent malignant gliomas treated by concurrent stereotactic radiosurger...
Parkinson's disease is the second most common neurodegenerative disease in the elderly after Alzheimer's disease. The aetiology and pathogenesis of Parkinson's disease (PD) are still unclear, but the ...
Radiomics is a rapidly evolving field of research concerned with the extraction and quantification of patterns - the so-called radiomic features - within medical images. Radiomic features capture tiss...
Recently, radiomics combined with machine learning method has been widely used in clinical practice. Compared with traditional imaging studies that explore the underlying mechanisms, the m...
This study will collect retrospective CT scan images and clinical data from patients with incidental lung nodules seen in hospitals across London. We will research whether we can use machi...
This study aims to use radiomics analysis and deep learning approaches for seizure focus detection in pediatric patients with temporal lobe epilepsy (TLE). Ten positron emission tomograph ...
In this study, investigators utilize a radiomics prediction model to predict the tumor response to neoadjuvant chemoradiotherapy (nCRT) before the nCRT is administered for patients with lo...
The investigators hypothesize that machine learning methods using a combination of novel, quantitative measures of cardio-respiratory variability can accurately predict the optimal time to...
A MACHINE LEARNING paradigm used to make predictions about future instances based on a given set of labeled paired input-output training (sample) data.
A MACHINE LEARNING paradigm used to make predictions about future instances based on a given set of unlabeled paired input-output training (sample) data.
SUPERVISED MACHINE LEARNING algorithm which learns to assign labels to objects from a set of training examples. Examples are learning to recognize fraudulent credit card activity by examining hundreds or thousands of fraudulent and non-fraudulent credit card activity, or learning to make disease diagnosis or prognosis based on automatic classification of microarray gene expression profiles drawn from hundreds or thousands of samples.
A type of ARTIFICIAL INTELLIGENCE that enable COMPUTERS to independently initiate and execute LEARNING when exposed to new data.
In INFORMATION RETRIEVAL, machine-sensing or identification of visible patterns (shapes, forms, and configurations). (Harrod's Librarians' Glossary, 7th ed)
According to the American Brain Tumor Association, just over 24,000 patients will be diagnosed with a primary malignant brain tumour during 2012 in the US alone. Some 80% of primary malignant brain tumours are gliomas, a broad term which includes all tum...
Radiology is the branch of medicine that studies imaging of the body; X-ray (basic, angiography, barium swallows), ultrasound, MRI, CT and PET. These imaging techniques can be used to diagnose, but also to treat a range of conditions, by allowing visuali...