[HTML][HTML] Radiomics with artificial intelligence: a practical guide for beginners
Radiomics is a relatively new word for the field of radiology, meaning the extraction of a high
number of quantitative features from medical images. Artificial intelligence (AI) is broadly a …
number of quantitative features from medical images. Artificial intelligence (AI) is broadly a …
A systematic review reporting quality of radiomics research in neuro-oncology: toward clinical utility and quality improvement using high-dimensional imaging features
Background To evaluate radiomics analysis in neuro-oncologic studies according to a
radiomics quality score (RQS) system to find room for improvement in clinical use. Methods …
radiomics quality score (RQS) system to find room for improvement in clinical use. Methods …
Quantitative MRI-based radiomics for noninvasively predicting molecular subtypes and survival in glioma patients
Gliomas can be classified into five molecular groups based on the status of IDH mutation,
1p/19q codeletion, and TERT promoter mutation, whereas they need to be obtained by …
1p/19q codeletion, and TERT promoter mutation, whereas they need to be obtained by …
Comparison of feature selection methods and machine learning classifiers for radiomics analysis in glioma grading
P Sun, D Wang, VC Mok, L Shi - Ieee Access, 2019 - ieeexplore.ieee.org
Radiomics-based researches have shown predictive abilities with machine-learning
approaches. However, it is still unknown whether different radiomics strategies affect the …
approaches. However, it is still unknown whether different radiomics strategies affect the …
Independent component analysis for unraveling the complexity of cancer omics datasets
Independent component analysis (ICA) is a matrix factorization approach where the signals
captured by each individual matrix factors are optimized to become as mutually independent …
captured by each individual matrix factors are optimized to become as mutually independent …
A radiomics model for preoperative prediction of brain invasion in meningioma non-invasively based on MRI: A multicentre study
Background Prediction of brain invasion pre-operatively rather than postoperatively would
contribute to the selection of surgical techniques, predicting meningioma grading and …
contribute to the selection of surgical techniques, predicting meningioma grading and …
Artificial intelligence in multiparametric magnetic resonance imaging: A review
Multiparametric magnetic resonance imaging (mpMRI) is an indispensable tool in the
clinical workflow for the diagnosis and treatment planning of various diseases. Machine …
clinical workflow for the diagnosis and treatment planning of various diseases. Machine …
Comparison study of radiomics and deep learning-based methods for thyroid nodules classification using ultrasound images
Thyroid nodules have a high prevalence and a small percentage is malignant. Many non-
invasive methods have been developed with the help of the Internet of Things to improve the …
invasive methods have been developed with the help of the Internet of Things to improve the …
Radiomics prognostication model in glioblastoma using diffusion-and perfusion-weighted MRI
We aimed to develop and validate a multiparametric MR radiomics model using
conventional, diffusion-, and perfusion-weighted MR imaging for better prognostication in …
conventional, diffusion-, and perfusion-weighted MR imaging for better prognostication in …
Virtual phantom analyses for preprocessing evaluation and detection of a robust feature set for MRI‐radiomics of the brain
Purpose The purpose of the paper was to use a virtual phantom to identify a set of radiomic
features from T1‐weighted and T2‐weighted magnetic resonance imaging (MRI) of the brain …
features from T1‐weighted and T2‐weighted magnetic resonance imaging (MRI) of the brain …