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


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    MathWorks Inc radiomics package in the matlab r2023a medical image toolbox
    Radiomics Package In The Matlab R2023a Medical Image Toolbox, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/radiomics+matlab+toolbox/pm40390117-60-9-13
    Average 90 stars, based on 1 article reviews
    radiomics package in the matlab r2023a medical image toolbox - by Bioz Stars, 2026-10
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    Article Title: Deep radiomic signature with immune cell markers predicts the survival of glioma patients
    Article Snippet: Imaging biomarkers offer a non-invasive way to predict the response of immunotherapy prior to treatment.. In this work, we propose a novel type of deep radiomic features (DRFs) computed from a convolutional neural network (CNN), which capture tumor characteristics related to immune cell markers and overall survival.. Our study uses four MRI sequences (T1-weighted, T1-weighted post-contrast, T2weighted and FLAIR) with corresponding immune cell markers of 151 patients with brain tumor.

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    Article Title: Interpretable Machine Learning for Characterization of Focal Liver Lesions by Contrast-Enhanced Ultrasound
    Article Snippet: .. Once the wash-in, peak, and wash-out frames were defined based on the fiducial time points, texture analysis was performed separately for each of these frames by using the texture feature extraction module of the radiomics MATLAB toolbox implemented by Vallières et al. [ ], [ ]. ..



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    Radiomic features analyzed in this study.

    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:

    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).

    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:

    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).

    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: