radiomics package in the matlab r2023a medical image toolbox  (MathWorks Inc)

Journal: Frontiers in Veterinary Science

Article Title: Computed tomography radiomics models of tumor differentiation in canine small intestinal tumors

doi: 10.3389/fvets.2024.1450304

Figure Lengend Snippet: Comparison between receiver-operating characteristics curves of the PTLN clinical-radiomics SVM, random forest and PTLN SVM, random forest. (A) The clinical-radiomics SVM model with Method 4 and BC16 showed a significantly higher AUC than the radiomics SVM model with Method 3 and BW128 (AUC 0.9775 vs. 0.9483). (B) The clinical-radiomics random forest model with Method 4 and BC32 exhibited a higher AUC than the radiomics random forest model with Method 4 and BC32 (AUC 0.9419 vs. 0.9231), although the difference was not significant.

Article Snippet: Radiomics models were constructed using the Statistics and Machine Learning Toolbox in MATLAB (MathWorks, Natick, MA, United States).

Techniques: Comparison

Journal: Frontiers in Veterinary Science

Article Title: Computed tomography radiomics models of tumor differentiation in canine small intestinal tumors

doi: 10.3389/fvets.2024.1450304

Figure Lengend Snippet: Comparison between receiver-operating characteristics curves of the PTLN clinical-radiomics SVM and random forest models. The SVM model with Method 4 and BC16 showed a significantly higher AUC than the random forest model with Method 4 and BC32 (AUC 0.9775 vs. 0.9412).

Article Snippet: Radiomics models were constructed using the Statistics and Machine Learning Toolbox in MATLAB (MathWorks, Natick, MA, United States).

Techniques: Comparison

Journal: Frontiers in Veterinary Science

Article Title: Computed tomography radiomics models of tumor differentiation in canine small intestinal tumors

doi: 10.3389/fvets.2024.1450304

Figure Lengend Snippet: Comparison among commonly selected radiomics features.

Article Snippet: Radiomics models were constructed using the Statistics and Machine Learning Toolbox in MATLAB (MathWorks, Natick, MA, United States).

Techniques: Comparison

Journal: Proceedings of SPIE--the International Society for Optical Engineering

Article Title: Evaluating clinical and radiomic features for predicting lung cancer recurrence pre- and post-tumor resection

doi: 10.1117/12.3006091

Figure Lengend Snippet: Radiomic features analyzed in this study.

Article Snippet: Feature Extraction Tumor masks were imported into our in-house radiomic feature extraction toolboxes created in MATLAB ® 2015b (The Mathworks Inc., Natick, Massachusetts) and C++ ( https://isocpp.org ).

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:

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: