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in-house software based on matlab platform version 9.3 matlab r2017b  (MathWorks Inc)


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    Structured Review

    MathWorks Inc in-house software based on matlab platform version 9.3 matlab r2017b
    Imaging and <t> radiomics </t> methodology.
    In House Software Based On Matlab Platform Version 9.3 Matlab R2017b, 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/in-house+software+based+on+matlab+platform+version+9%2E3+matlab+r2017b/pmc11435603-4-21-30
    Average 90 stars, based on 1 article reviews
    in-house software based on matlab platform version 9.3 matlab r2017b - by Bioz Stars, 2026-09
    90/100 stars

    Images

    1) Product Images from "A Scoping Review of Machine-Learning Derived Radiomic Analysis of CT and PET Imaging to Investigate Atherosclerotic Cardiovascular Disease"

    Article Title: A Scoping Review of Machine-Learning Derived Radiomic Analysis of CT and PET Imaging to Investigate Atherosclerotic Cardiovascular Disease

    Journal: Tomography

    doi: 10.3390/tomography10090108

    Imaging and radiomics methodology.
    Figure Legend Snippet: Imaging and radiomics methodology.

    Techniques Used: Imaging, Extraction, Software, Biomarker Discovery, Construct, Activity Assay

    Related Articles

    Positron Emission Tomography-Computed Tomography:

    Article Title: A Scoping Review of Machine-Learning Derived Radiomic Analysis of CT and PET Imaging to Investigate Atherosclerotic Cardiovascular Disease
    Article Snippet: Ebrahimian et al. [ ] , Dual-energy CT angiography , Adherence to radiomics guidelines: nil Feature extraction software: PyRadiomics integrated into Dual-Energy Tumour Analysis prototype software (eXamine, Siemens Healthineers, Forcheim, Germany) , Segmentation: automated segmentation using Dual-Energy Tumour Analysis prototype software (eXamine, Siemens Healthineers, Forcheim, Germany) Features extracted: shape, first-order, GLCM, NGTDM, GLSZM, GLRLM, GLDM, and higher-order features Machine learning techniques: multinomial logistic regression , Performance assessment: AUC from the ROC Internal validation: DNM No external validation. .. Kafouris et al. [ ] , PET/CT using 0.14 mCi/kg 18 F-FDG , Adherence to radiomics guidelines: features extracted according to IBSI guidelines Feature extraction software: in-house software based on Matlab platform (Version 9.3, Matlab R2017b, Natick, MA, USA) , Segmentation: manual segmentation around the carotid artery wall Features extracted: first order, GLCM, GLRLM, GLSZM and NGTDM Machine learning techniques: univariate logistic regression , Performance assessment: AUC from the ROC Internal validation: bootstrapping generating 200 bootstrap samples No external validation. .. Liu et al. [ ] , CT angiography , Adherence to radiomics guidelines: nil Feature extraction software: Radcloud platform (Huiying Medical Technology, Beijing, China) , Segmentation: manual segmentation of the coronary plaque using ITK-SNAP software (version 3.7, http://www.itksnap.org/ , accessed on 27 August 2024) Features extracted: shape, first order, GLDM, GLRLM, GLCM, GLSZM and NGTDM Machine learning techniques: LASSO used to construct a ‘radiomics score’ , Performance assessment: AUC from the ROC Internal validation: dataset split into training set (n = 135) and validation set (n = 58) External validation using 87 patients.

    Extraction:

    Article Title: A Scoping Review of Machine-Learning Derived Radiomic Analysis of CT and PET Imaging to Investigate Atherosclerotic Cardiovascular Disease
    Article Snippet: Ebrahimian et al. [ ] , Dual-energy CT angiography , Adherence to radiomics guidelines: nil Feature extraction software: PyRadiomics integrated into Dual-Energy Tumour Analysis prototype software (eXamine, Siemens Healthineers, Forcheim, Germany) , Segmentation: automated segmentation using Dual-Energy Tumour Analysis prototype software (eXamine, Siemens Healthineers, Forcheim, Germany) Features extracted: shape, first-order, GLCM, NGTDM, GLSZM, GLRLM, GLDM, and higher-order features Machine learning techniques: multinomial logistic regression , Performance assessment: AUC from the ROC Internal validation: DNM No external validation. .. Kafouris et al. [ ] , PET/CT using 0.14 mCi/kg 18 F-FDG , Adherence to radiomics guidelines: features extracted according to IBSI guidelines Feature extraction software: in-house software based on Matlab platform (Version 9.3, Matlab R2017b, Natick, MA, USA) , Segmentation: manual segmentation around the carotid artery wall Features extracted: first order, GLCM, GLRLM, GLSZM and NGTDM Machine learning techniques: univariate logistic regression , Performance assessment: AUC from the ROC Internal validation: bootstrapping generating 200 bootstrap samples No external validation. .. Liu et al. [ ] , CT angiography , Adherence to radiomics guidelines: nil Feature extraction software: Radcloud platform (Huiying Medical Technology, Beijing, China) , Segmentation: manual segmentation of the coronary plaque using ITK-SNAP software (version 3.7, http://www.itksnap.org/ , accessed on 27 August 2024) Features extracted: shape, first order, GLDM, GLRLM, GLCM, GLSZM and NGTDM Machine learning techniques: LASSO used to construct a ‘radiomics score’ , Performance assessment: AUC from the ROC Internal validation: dataset split into training set (n = 135) and validation set (n = 58) External validation using 87 patients.

    Software:

    Article Title: A Scoping Review of Machine-Learning Derived Radiomic Analysis of CT and PET Imaging to Investigate Atherosclerotic Cardiovascular Disease
    Article Snippet: Ebrahimian et al. [ ] , Dual-energy CT angiography , Adherence to radiomics guidelines: nil Feature extraction software: PyRadiomics integrated into Dual-Energy Tumour Analysis prototype software (eXamine, Siemens Healthineers, Forcheim, Germany) , Segmentation: automated segmentation using Dual-Energy Tumour Analysis prototype software (eXamine, Siemens Healthineers, Forcheim, Germany) Features extracted: shape, first-order, GLCM, NGTDM, GLSZM, GLRLM, GLDM, and higher-order features Machine learning techniques: multinomial logistic regression , Performance assessment: AUC from the ROC Internal validation: DNM No external validation. .. Kafouris et al. [ ] , PET/CT using 0.14 mCi/kg 18 F-FDG , Adherence to radiomics guidelines: features extracted according to IBSI guidelines Feature extraction software: in-house software based on Matlab platform (Version 9.3, Matlab R2017b, Natick, MA, USA) , Segmentation: manual segmentation around the carotid artery wall Features extracted: first order, GLCM, GLRLM, GLSZM and NGTDM Machine learning techniques: univariate logistic regression , Performance assessment: AUC from the ROC Internal validation: bootstrapping generating 200 bootstrap samples No external validation. .. Liu et al. [ ] , CT angiography , Adherence to radiomics guidelines: nil Feature extraction software: Radcloud platform (Huiying Medical Technology, Beijing, China) , Segmentation: manual segmentation of the coronary plaque using ITK-SNAP software (version 3.7, http://www.itksnap.org/ , accessed on 27 August 2024) Features extracted: shape, first order, GLDM, GLRLM, GLCM, GLSZM and NGTDM Machine learning techniques: LASSO used to construct a ‘radiomics score’ , Performance assessment: AUC from the ROC Internal validation: dataset split into training set (n = 135) and validation set (n = 58) External validation using 87 patients.

    Biomarker Discovery:

    Article Title: A Scoping Review of Machine-Learning Derived Radiomic Analysis of CT and PET Imaging to Investigate Atherosclerotic Cardiovascular Disease
    Article Snippet: Ebrahimian et al. [ ] , Dual-energy CT angiography , Adherence to radiomics guidelines: nil Feature extraction software: PyRadiomics integrated into Dual-Energy Tumour Analysis prototype software (eXamine, Siemens Healthineers, Forcheim, Germany) , Segmentation: automated segmentation using Dual-Energy Tumour Analysis prototype software (eXamine, Siemens Healthineers, Forcheim, Germany) Features extracted: shape, first-order, GLCM, NGTDM, GLSZM, GLRLM, GLDM, and higher-order features Machine learning techniques: multinomial logistic regression , Performance assessment: AUC from the ROC Internal validation: DNM No external validation. .. Kafouris et al. [ ] , PET/CT using 0.14 mCi/kg 18 F-FDG , Adherence to radiomics guidelines: features extracted according to IBSI guidelines Feature extraction software: in-house software based on Matlab platform (Version 9.3, Matlab R2017b, Natick, MA, USA) , Segmentation: manual segmentation around the carotid artery wall Features extracted: first order, GLCM, GLRLM, GLSZM and NGTDM Machine learning techniques: univariate logistic regression , Performance assessment: AUC from the ROC Internal validation: bootstrapping generating 200 bootstrap samples No external validation. .. Liu et al. [ ] , CT angiography , Adherence to radiomics guidelines: nil Feature extraction software: Radcloud platform (Huiying Medical Technology, Beijing, China) , Segmentation: manual segmentation of the coronary plaque using ITK-SNAP software (version 3.7, http://www.itksnap.org/ , accessed on 27 August 2024) Features extracted: shape, first order, GLDM, GLRLM, GLCM, GLSZM and NGTDM Machine learning techniques: LASSO used to construct a ‘radiomics score’ , Performance assessment: AUC from the ROC Internal validation: dataset split into training set (n = 135) and validation set (n = 58) External validation using 87 patients.



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    in-house software based on matlab platform version 9.3 matlab r2017b  (MathWorks Inc)
    90
    MathWorks Inc in-house software based on matlab platform version 9.3 matlab r2017b
    Imaging and <t> radiomics </t> methodology.
    In House Software Based On Matlab Platform Version 9.3 Matlab R2017b, 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/in-house+software+based+on+matlab+platform+version+9%2E3+matlab+r2017b/pmc11435603-4-21-30
    Average 90 stars, based on 1 article reviews
    in-house software based on matlab platform version 9.3 matlab r2017b - by Bioz Stars, 2026-09
    90/100 stars
    		  Buy from Supplier

    Image Search Results


    Imaging and radiomics methodology.

    Journal: Tomography

    Article Title: A Scoping Review of Machine-Learning Derived Radiomic Analysis of CT and PET Imaging to Investigate Atherosclerotic Cardiovascular Disease

    doi: 10.3390/tomography10090108

    Figure Lengend Snippet: Imaging and radiomics methodology.

    Article Snippet: Kafouris et al. [ ] , PET/CT using 0.14 mCi/kg 18 F-FDG , Adherence to radiomics guidelines: features extracted according to IBSI guidelines Feature extraction software: in-house software based on Matlab platform (Version 9.3, Matlab R2017b, Natick, MA, USA) , Segmentation: manual segmentation around the carotid artery wall Features extracted: first order, GLCM, GLRLM, GLSZM and NGTDM Machine learning techniques: univariate logistic regression , Performance assessment: AUC from the ROC Internal validation: bootstrapping generating 200 bootstrap samples No external validation.

    Techniques: Imaging, Extraction, Software, Biomarker Discovery, Construct, Activity Assay