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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/result/radiomics package in the matlab r2023a medical image toolbox/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    radiomics package in the matlab r2023a medical image toolbox - by Bioz Stars, 2026-03
    90/100 stars

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    MathWorks Inc matlab radiomic package
    Schema for lung cancer segmentation, <t>radiomic</t> feature extraction and predictive modeling. (A) Representative CT images from small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) showing tumor segmentation. (B) Illustrations of radiomic feature extraction for texture, shape, and intensity. (C) Decision of SCCL/NSCLC classification (upper panel) with the receiver operating characteristic (ROC) curves (middle panel) and the heat map of radiomic features (lower panel).
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    Schema for lung cancer segmentation, radiomic feature extraction and predictive modeling. (A) Representative CT images from small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) showing tumor segmentation. (B) Illustrations of radiomic feature extraction for texture, shape, and intensity. (C) Decision of SCCL/NSCLC classification (upper panel) with the receiver operating characteristic (ROC) curves (middle panel) and the heat map of radiomic features (lower panel).

    Journal: Frontiers in Oncology

    Article Title: Differentiating Peripherally-Located Small Cell Lung Cancer From Non-small Cell Lung Cancer Using a CT Radiomic Approach

    doi: 10.3389/fonc.2020.00593

    Figure Lengend Snippet: Schema for lung cancer segmentation, radiomic feature extraction and predictive modeling. (A) Representative CT images from small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) showing tumor segmentation. (B) Illustrations of radiomic feature extraction for texture, shape, and intensity. (C) Decision of SCCL/NSCLC classification (upper panel) with the receiver operating characteristic (ROC) curves (middle panel) and the heat map of radiomic features (lower panel).

    Article Snippet: We extracted the tumor textural features using the MATLAB radiomic package ( https://github.com/mvallieres/radiomics ) and the textural analysis formula ( ).

    Techniques:

    The nnet architecture of the radiomics-based SCLC/NSCLC classifier. This figure presents the input layer with 20 nodes receiving 20 radiomic features, the 3 hidden layers for non-linear mapping, and the output layer with 2 nodes for “SCLC” and “NSCLC” decision upon a hard thresholding f(node)>0 and f(node)≤0, respectively. SCLC, small cell lung cancer; NSCLC, non-small cell lung cancer.

    Journal: Frontiers in Oncology

    Article Title: Differentiating Peripherally-Located Small Cell Lung Cancer From Non-small Cell Lung Cancer Using a CT Radiomic Approach

    doi: 10.3389/fonc.2020.00593

    Figure Lengend Snippet: The nnet architecture of the radiomics-based SCLC/NSCLC classifier. This figure presents the input layer with 20 nodes receiving 20 radiomic features, the 3 hidden layers for non-linear mapping, and the output layer with 2 nodes for “SCLC” and “NSCLC” decision upon a hard thresholding f(node)>0 and f(node)≤0, respectively. SCLC, small cell lung cancer; NSCLC, non-small cell lung cancer.

    Article Snippet: We extracted the tumor textural features using the MATLAB radiomic package ( https://github.com/mvallieres/radiomics ) and the textural analysis formula ( ).

    Techniques:

    The top 20 features selected from the radiomic data set (total 1,731 features) for the small cell lung cancer (SCLC) / non-small-cell lung cancer (NSCLC) classification. (A) Measurements for top 20 features. Each feature (matrix row) consisted of 35 SCLC measurements (index 1:35) and 34 NSCLC measurements (index 36:69). Each feature vector was normalized by max=1. (B) Mutual information map for the top 20 features. A large mutual information value indicated a high redundancy between the features.

    Journal: Frontiers in Oncology

    Article Title: Differentiating Peripherally-Located Small Cell Lung Cancer From Non-small Cell Lung Cancer Using a CT Radiomic Approach

    doi: 10.3389/fonc.2020.00593

    Figure Lengend Snippet: The top 20 features selected from the radiomic data set (total 1,731 features) for the small cell lung cancer (SCLC) / non-small-cell lung cancer (NSCLC) classification. (A) Measurements for top 20 features. Each feature (matrix row) consisted of 35 SCLC measurements (index 1:35) and 34 NSCLC measurements (index 36:69). Each feature vector was normalized by max=1. (B) Mutual information map for the top 20 features. A large mutual information value indicated a high redundancy between the features.

    Article Snippet: We extracted the tumor textural features using the MATLAB radiomic package ( https://github.com/mvallieres/radiomics ) and the textural analysis formula ( ).

    Techniques: Plasmid Preparation