Journal: Scientific Reports

Article Title: Combining Multiple Magnetic Resonance Imaging Sequences Provides Independent Reproducible Radiomics Features

doi: 10.1038/s41598-018-37984-8

Figure Lengend Snippet: Radiomics features extracted using our in-house software.

Article Snippet: All the post-acquisition steps were performed using an in-house software (Matlab R2013b [The Mathworks, Natick, MA, USA]) adapted from the Vallieres radiomics toolbox .

Techniques: Software, Standard Deviation

Journal: Physics in medicine and biology

Article Title: 4D radiomics: impact of 4D-CBCT image quality on radiomic analysis

doi: 10.1088/1361-6560/abd668

Figure Lengend Snippet: Histogram and GLCM radiomics errors across 10 phases of patient 3.

Article Snippet: In total, 540 radiomic features were extracted from the GTV of images based on the MATLAB based radiomics toolbox ( Vallières et al 2015 ).

Techniques:

Journal: Physics in medicine and biology

Article Title: 4D radiomics: impact of 4D-CBCT image quality on radiomic analysis

doi: 10.1088/1361-6560/abd668

Figure Lengend Snippet: Average radiomics features of the histogram, GLCM, GLRLM, GLSZM, NGTDM and wavelet of patient 3.

Article Snippet: In total, 540 radiomic features were extracted from the GTV of images based on the MATLAB based radiomics toolbox ( Vallières et al 2015 ).

Techniques:

Journal: Physics in medicine and biology

Article Title: 4D radiomics: impact of 4D-CBCT image quality on radiomic analysis

doi: 10.1088/1361-6560/abd668

Figure Lengend Snippet: Radiomics errors of all three testing patients with different training data and different projection numbers.

Article Snippet: In total, 540 radiomic features were extracted from the GTV of images based on the MATLAB based radiomics toolbox ( Vallières et al 2015 ).

Techniques:

Journal: Frontiers in Aging

Article Title: The Usefulness of Radiomics Methodology for Developing Descriptive and Prognostic Image-Based Phenotyping in the Aging Population: Results From a Small Feasibility Study

doi: 10.3389/fragi.2022.853671

Figure Lengend Snippet: Prognostic value of radiomic data. (A) Heatmap of Rho of Spearman Correlation coefficients for an association of Radiomic Features and Incidence of new diseases and risk factors ( n = 101). On the x -axis, radiomics features are shown, and on the y -axis are the incidence of comorbidities and risk factors. The elements of the heatmap are color-coded depending on the value of the correlation coefficient. Red is for the highest value and green for the lowest, with 5 different colors in between. Abbreviations: DM: Diabetes Mellitus; COPD: Chronic Obstructive Pulmonary Disease; #: number; PC: primary care; ED: emergency department; IHF: Intensity Histogram Features; GLCM: Gray-Level Co-occurrence Matrix; GLRLM: Gray-Level run-Length Matrix; GLSZM: Gray-Level Size Zone Matrix; NGTDM: Neighborhood Gray-Tone Difference Matrix. Note. Tau B of Kendal was used for the statistical analysis. (B) Manhattan plot of p -values for associations between radiomic features and incidence of new diseases and risk factors ( n = 101). p -values for univariate associations between each radiomic feature and the incidence of new disease and risk factors after 2 years of following from baseline ultrasound. Radiomic features are situated on the x -axis in the same order as the heatmap, while the corresponding p -values are located on the y -axis and graph with a -LOG10 ( p -value) scale. Points above the red line ( p = <0.05) indicate radiomic features in which case the incidence of new diseases or risk factors showed significant association. (C) Hierarchical cluster dendrogram ( n = 44). Hierarchical cluster dendrogram of radiomic features significantly associated with hearing impairment, stroke, myocardial infarction, dementia or memory loss, and falls. Three independent clusters are identified for the radiomic phenotype ( p = 0.001).

Article Snippet: The features were extracted using the MATLAB toolbox Radiomics implemented by Vallières and others ( ).

Techniques:

Journal: Frontiers in Aging

Article Title: The Usefulness of Radiomics Methodology for Developing Descriptive and Prognostic Image-Based Phenotyping in the Aging Population: Results From a Small Feasibility Study

doi: 10.3389/fragi.2022.853671

Figure Lengend Snippet: Mitochondrial radiomic signature of ultrasound images. Radiomics aims to capture the informative content hidden in medical images, overcoming the limitations of the human eyes and human cognitive patterns. These patterns can be expressed in terms of macroscopic image-based radiomic features and carry information about their underlying pathophysiological processes and pinpoint specific biological mechanisms. This allows us to infer phenotypes or signatures, including prognostic information. Here we graphically showed that a radiomic phenotype, capturing the muscle heterogeneity, was strongly prognostic of the development of hearing impairment, stroke, myocardial infarction, dementia/memory loss, and/or falls. Based on the type of disease associated with the muscle ultrasound changes, we also believe this identified group of diseases shares a mitochondrial link. Icons utilized in this figure were obtain from the Noun Project from the following authors: Gorkem Oner (mitochondria), Gregor Cresnar (ear), Artem Kovyazin (brain), Tatina Vazest (heart), Luis Padra (fading head) and Visual Language Company (slipping person).

Article Snippet: The features were extracted using the MATLAB toolbox Radiomics implemented by Vallières and others ( ).

Techniques: