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in-house matlab v.2019 toolbox  (MathWorks Inc)


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    MathWorks Inc in-house matlab v.2019 toolbox
    Visualizations of one of the topmost features of M R (orginal_collage2D_glcmV_JointEnergyEntorpy) (R1-R4) and M P (Shape: 5 %/95 % invariant 1) (P1–P4) between four different patients. The columns 1,2 represent patients with a low risk of rising PSA and columns 3,4 represent patients with a high risk of rising PSA. It can be observed that the visualizations of Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe) gray level cooccurrence matrix (GLCM) radiomic feature on apparent diffusion coefficient (ADC) maps indicates the presence of higher density of high entropy regions for which M R has classified as rPSA + ( : R3, R4), as compared to the ones for which M R has classified as rPSA − ( : R1, R2). Similarly, the pathomic visualizations of Shape: 5 %/95 % invariant 1 depicts that a high risk of rising PSA with more aggressive cancer leads to uniformly small, malformed lumen resulting in a lower 5th/95th percentile ratio (lower range) ( : P3, P4) as compared to cases with lower risk of rising PSA ( : P1, P2). For radiomic visualizations, the feature array output from the pyradiomics package was used to overlay on top of the ADC using matplotlib package. For pathomics visualizations, in-house <t>MATLAB</t> code was used to overlay the visualizations.
    In House Matlab V.2019 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/result/in-house matlab v.2019 toolbox/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    in-house matlab v.2019 toolbox - by Bioz Stars, 2026-02
    90/100 stars

    Images

    1) Product Images from "An integrated radiology-pathology machine learning classifier for outcome prediction following radical prostatectomy: Preliminary findings"

    Article Title: An integrated radiology-pathology machine learning classifier for outcome prediction following radical prostatectomy: Preliminary findings

    Journal: Heliyon

    doi: 10.1016/j.heliyon.2024.e29602

    Visualizations of one of the topmost features of M R (orginal_collage2D_glcmV_JointEnergyEntorpy) (R1-R4) and M P (Shape: 5 %/95 % invariant 1) (P1–P4) between four different patients. The columns 1,2 represent patients with a low risk of rising PSA and columns 3,4 represent patients with a high risk of rising PSA. It can be observed that the visualizations of Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe) gray level cooccurrence matrix (GLCM) radiomic feature on apparent diffusion coefficient (ADC) maps indicates the presence of higher density of high entropy regions for which M R has classified as rPSA + ( : R3, R4), as compared to the ones for which M R has classified as rPSA − ( : R1, R2). Similarly, the pathomic visualizations of Shape: 5 %/95 % invariant 1 depicts that a high risk of rising PSA with more aggressive cancer leads to uniformly small, malformed lumen resulting in a lower 5th/95th percentile ratio (lower range) ( : P3, P4) as compared to cases with lower risk of rising PSA ( : P1, P2). For radiomic visualizations, the feature array output from the pyradiomics package was used to overlay on top of the ADC using matplotlib package. For pathomics visualizations, in-house MATLAB code was used to overlay the visualizations.
    Figure Legend Snippet: Visualizations of one of the topmost features of M R (orginal_collage2D_glcmV_JointEnergyEntorpy) (R1-R4) and M P (Shape: 5 %/95 % invariant 1) (P1–P4) between four different patients. The columns 1,2 represent patients with a low risk of rising PSA and columns 3,4 represent patients with a high risk of rising PSA. It can be observed that the visualizations of Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe) gray level cooccurrence matrix (GLCM) radiomic feature on apparent diffusion coefficient (ADC) maps indicates the presence of higher density of high entropy regions for which M R has classified as rPSA + ( : R3, R4), as compared to the ones for which M R has classified as rPSA − ( : R1, R2). Similarly, the pathomic visualizations of Shape: 5 %/95 % invariant 1 depicts that a high risk of rising PSA with more aggressive cancer leads to uniformly small, malformed lumen resulting in a lower 5th/95th percentile ratio (lower range) ( : P3, P4) as compared to cases with lower risk of rising PSA ( : P1, P2). For radiomic visualizations, the feature array output from the pyradiomics package was used to overlay on top of the ADC using matplotlib package. For pathomics visualizations, in-house MATLAB code was used to overlay the visualizations.

    Techniques Used: Diffusion-based Assay



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    in-house matlab v.2019 toolbox  (MathWorks Inc)
    90
    MathWorks Inc in-house matlab v.2019 toolbox
    Visualizations of one of the topmost features of M R (orginal_collage2D_glcmV_JointEnergyEntorpy) (R1-R4) and M P (Shape: 5 %/95 % invariant 1) (P1–P4) between four different patients. The columns 1,2 represent patients with a low risk of rising PSA and columns 3,4 represent patients with a high risk of rising PSA. It can be observed that the visualizations of Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe) gray level cooccurrence matrix (GLCM) radiomic feature on apparent diffusion coefficient (ADC) maps indicates the presence of higher density of high entropy regions for which M R has classified as rPSA + ( : R3, R4), as compared to the ones for which M R has classified as rPSA − ( : R1, R2). Similarly, the pathomic visualizations of Shape: 5 %/95 % invariant 1 depicts that a high risk of rising PSA with more aggressive cancer leads to uniformly small, malformed lumen resulting in a lower 5th/95th percentile ratio (lower range) ( : P3, P4) as compared to cases with lower risk of rising PSA ( : P1, P2). For radiomic visualizations, the feature array output from the pyradiomics package was used to overlay on top of the ADC using matplotlib package. For pathomics visualizations, in-house <t>MATLAB</t> code was used to overlay the visualizations.
    In House Matlab V.2019 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/result/in-house matlab v.2019 toolbox/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    in-house matlab v.2019 toolbox - by Bioz Stars, 2026-02
    90/100 stars
    		  Buy from Supplier

    Image Search Results


    Visualizations of one of the topmost features of M R (orginal_collage2D_glcmV_JointEnergyEntorpy) (R1-R4) and M P (Shape: 5 %/95 % invariant 1) (P1–P4) between four different patients. The columns 1,2 represent patients with a low risk of rising PSA and columns 3,4 represent patients with a high risk of rising PSA. It can be observed that the visualizations of Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe) gray level cooccurrence matrix (GLCM) radiomic feature on apparent diffusion coefficient (ADC) maps indicates the presence of higher density of high entropy regions for which M R has classified as rPSA + ( : R3, R4), as compared to the ones for which M R has classified as rPSA − ( : R1, R2). Similarly, the pathomic visualizations of Shape: 5 %/95 % invariant 1 depicts that a high risk of rising PSA with more aggressive cancer leads to uniformly small, malformed lumen resulting in a lower 5th/95th percentile ratio (lower range) ( : P3, P4) as compared to cases with lower risk of rising PSA ( : P1, P2). For radiomic visualizations, the feature array output from the pyradiomics package was used to overlay on top of the ADC using matplotlib package. For pathomics visualizations, in-house MATLAB code was used to overlay the visualizations.

    Journal: Heliyon

    Article Title: An integrated radiology-pathology machine learning classifier for outcome prediction following radical prostatectomy: Preliminary findings

    doi: 10.1016/j.heliyon.2024.e29602

    Figure Lengend Snippet: Visualizations of one of the topmost features of M R (orginal_collage2D_glcmV_JointEnergyEntorpy) (R1-R4) and M P (Shape: 5 %/95 % invariant 1) (P1–P4) between four different patients. The columns 1,2 represent patients with a low risk of rising PSA and columns 3,4 represent patients with a high risk of rising PSA. It can be observed that the visualizations of Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe) gray level cooccurrence matrix (GLCM) radiomic feature on apparent diffusion coefficient (ADC) maps indicates the presence of higher density of high entropy regions for which M R has classified as rPSA + ( : R3, R4), as compared to the ones for which M R has classified as rPSA − ( : R1, R2). Similarly, the pathomic visualizations of Shape: 5 %/95 % invariant 1 depicts that a high risk of rising PSA with more aggressive cancer leads to uniformly small, malformed lumen resulting in a lower 5th/95th percentile ratio (lower range) ( : P3, P4) as compared to cases with lower risk of rising PSA ( : P1, P2). For radiomic visualizations, the feature array output from the pyradiomics package was used to overlay on top of the ADC using matplotlib package. For pathomics visualizations, in-house MATLAB code was used to overlay the visualizations.

    Article Snippet: The pathomic feature extraction was performed using an in-house MATLAB V.2019 (MathWorks, Natick, Massachusetts, USA) toolbox.

    Techniques: Diffusion-based Assay