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standardized environment for radiomics analysis  (MathWorks Inc)


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    Structured Review

    MathWorks Inc standardized environment for radiomics analysis
    Proposed methods. Machine-learning (ML) models were constructed using different combinations of five demographic, eight quantitative computed tomography (qCT) and 95 texture-based CT <t>radiomics</t> measurements. The dataset was split into a 5-fold cross-validation training dataset (75% of the data) and testing dataset (25% of the data). The training dataset was used with feature selection methods to select five features, which were then input into a ML classifier to be trained. The ML models were then tested with the testing dataset for COPD status and COPD severity classification. ROC: receiver operating characteristic; SHAP: SHapely Additive exPlanations.
    Standardized Environment For Radiomics Analysis, 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/standardized environment for radiomics analysis/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    standardized environment for radiomics analysis - by Bioz Stars, 2026-03
    90/100 stars

    Images

    1) Product Images from "Enhancing COPD classification using combined quantitative computed tomography and texture-based radiomics: a CanCOLD cohort study"

    Article Title: Enhancing COPD classification using combined quantitative computed tomography and texture-based radiomics: a CanCOLD cohort study

    Journal: ERJ Open Research

    doi: 10.1183/23120541.00968-2023

    Proposed methods. Machine-learning (ML) models were constructed using different combinations of five demographic, eight quantitative computed tomography (qCT) and 95 texture-based CT radiomics measurements. The dataset was split into a 5-fold cross-validation training dataset (75% of the data) and testing dataset (25% of the data). The training dataset was used with feature selection methods to select five features, which were then input into a ML classifier to be trained. The ML models were then tested with the testing dataset for COPD status and COPD severity classification. ROC: receiver operating characteristic; SHAP: SHapely Additive exPlanations.
    Figure Legend Snippet: Proposed methods. Machine-learning (ML) models were constructed using different combinations of five demographic, eight quantitative computed tomography (qCT) and 95 texture-based CT radiomics measurements. The dataset was split into a 5-fold cross-validation training dataset (75% of the data) and testing dataset (25% of the data). The training dataset was used with feature selection methods to select five features, which were then input into a ML classifier to be trained. The ML models were then tested with the testing dataset for COPD status and COPD severity classification. ROC: receiver operating characteristic; SHAP: SHapely Additive exPlanations.

    Techniques Used: Construct, Computed Tomography, Biomarker Discovery, Selection

    Models comparing the impact of the addition of texture-based radiomics to conventional measurements (demographics and qCT features) for classifying COPD status and COPD severity in the testing dataset
    Figure Legend Snippet: Models comparing the impact of the addition of texture-based radiomics to conventional measurements (demographics and qCT features) for classifying COPD status and COPD severity in the testing dataset

    Techniques Used:

    Receiver operating characteristic curves and SHapely Additive exPlanations (SHAP) analysis for COPD status with different input feature set combinations. qCT: quantitative computed tomography; AUC: area under the receiver operating characteristic curve; HU 15 : 15th percentile of the density histogram; TAC: total airway count; LAC: low-attenuation clusters; GLCM jointavg : grey-level co-occurrence matrix (GLCM) joint average; GLDZM zdentr : grey-level distance zone matrix (GLDZM) zone distance entropy; GLDZM ldlge : GLDZM large distance low grey-level emphasis; GLDZM zdnunorm : GLDZM zone distance non-uniformity normalised. # : significantly different AUC from demographics and qCT model; ¶ : significantly different AUC from demographics and texture-based radiomics model.
    Figure Legend Snippet: Receiver operating characteristic curves and SHapely Additive exPlanations (SHAP) analysis for COPD status with different input feature set combinations. qCT: quantitative computed tomography; AUC: area under the receiver operating characteristic curve; HU 15 : 15th percentile of the density histogram; TAC: total airway count; LAC: low-attenuation clusters; GLCM jointavg : grey-level co-occurrence matrix (GLCM) joint average; GLDZM zdentr : grey-level distance zone matrix (GLDZM) zone distance entropy; GLDZM ldlge : GLDZM large distance low grey-level emphasis; GLDZM zdnunorm : GLDZM zone distance non-uniformity normalised. # : significantly different AUC from demographics and qCT model; ¶ : significantly different AUC from demographics and texture-based radiomics model.

    Techniques Used: Computed Tomography

    Receiver operating characteristic curves and SHapely Additive exPlanations (SHAP) analysis for COPD severity with different input feature set combinations. qCT: quantitative computed tomography; AUC: area under the receiver operating characteristic curve; NJC: normalised join count; TAC: total airway count; WA%: wall area %; GLDZM zdnunorm : grey-level distance zone matrix (GLDZM) zone distance non-uniformity normalised; GLDZM ldlge : GLDZM large distance low grey-level emphasis; GLCM jointavg : grey-level co-occurrence matrix joint average; GLDZM zdnu : GLDZM zone distance non-uniformity. # : significantly different AUC from demographics and qCT model; ¶ : significantly different AUC from demographics and texture-based radiomics model.
    Figure Legend Snippet: Receiver operating characteristic curves and SHapely Additive exPlanations (SHAP) analysis for COPD severity with different input feature set combinations. qCT: quantitative computed tomography; AUC: area under the receiver operating characteristic curve; NJC: normalised join count; TAC: total airway count; WA%: wall area %; GLDZM zdnunorm : grey-level distance zone matrix (GLDZM) zone distance non-uniformity normalised; GLDZM ldlge : GLDZM large distance low grey-level emphasis; GLCM jointavg : grey-level co-occurrence matrix joint average; GLDZM zdnu : GLDZM zone distance non-uniformity. # : significantly different AUC from demographics and qCT model; ¶ : significantly different AUC from demographics and texture-based radiomics model.

    Techniques Used: Computed Tomography

    Pearson's correlation coefficients (r) for CT features (all qCT and texture-based radiomics selected in the machine-learning models) with baseline spirometry measurements for the whole cohort
    Figure Legend Snippet: Pearson's correlation coefficients (r) for CT features (all qCT and texture-based radiomics selected in the machine-learning models) with baseline spirometry measurements for the whole cohort

    Techniques Used:



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    Image Search Results


    Proposed methods. Machine-learning (ML) models were constructed using different combinations of five demographic, eight quantitative computed tomography (qCT) and 95 texture-based CT radiomics measurements. The dataset was split into a 5-fold cross-validation training dataset (75% of the data) and testing dataset (25% of the data). The training dataset was used with feature selection methods to select five features, which were then input into a ML classifier to be trained. The ML models were then tested with the testing dataset for COPD status and COPD severity classification. ROC: receiver operating characteristic; SHAP: SHapely Additive exPlanations.

    Journal: ERJ Open Research

    Article Title: Enhancing COPD classification using combined quantitative computed tomography and texture-based radiomics: a CanCOLD cohort study

    doi: 10.1183/23120541.00968-2023

    Figure Lengend Snippet: Proposed methods. Machine-learning (ML) models were constructed using different combinations of five demographic, eight quantitative computed tomography (qCT) and 95 texture-based CT radiomics measurements. The dataset was split into a 5-fold cross-validation training dataset (75% of the data) and testing dataset (25% of the data). The training dataset was used with feature selection methods to select five features, which were then input into a ML classifier to be trained. The ML models were then tested with the testing dataset for COPD status and COPD severity classification. ROC: receiver operating characteristic; SHAP: SHapely Additive exPlanations.

    Article Snippet: To extract the texture-based CT radiomic features, an in-house-developed pipeline that uses the Standardized Environment for Radiomics Analysis [ ] (MATLAB-based framework) was constructed to calculate the features in compliance with the Image Biomarker Standardisation Initiative (IBSI) [ ].

    Techniques: Construct, Computed Tomography, Biomarker Discovery, Selection

    Models comparing the impact of the addition of texture-based radiomics to conventional measurements (demographics and qCT features) for classifying COPD status and COPD severity in the testing dataset

    Journal: ERJ Open Research

    Article Title: Enhancing COPD classification using combined quantitative computed tomography and texture-based radiomics: a CanCOLD cohort study

    doi: 10.1183/23120541.00968-2023

    Figure Lengend Snippet: Models comparing the impact of the addition of texture-based radiomics to conventional measurements (demographics and qCT features) for classifying COPD status and COPD severity in the testing dataset

    Article Snippet: To extract the texture-based CT radiomic features, an in-house-developed pipeline that uses the Standardized Environment for Radiomics Analysis [ ] (MATLAB-based framework) was constructed to calculate the features in compliance with the Image Biomarker Standardisation Initiative (IBSI) [ ].

    Techniques:

    Receiver operating characteristic curves and SHapely Additive exPlanations (SHAP) analysis for COPD status with different input feature set combinations. qCT: quantitative computed tomography; AUC: area under the receiver operating characteristic curve; HU 15 : 15th percentile of the density histogram; TAC: total airway count; LAC: low-attenuation clusters; GLCM jointavg : grey-level co-occurrence matrix (GLCM) joint average; GLDZM zdentr : grey-level distance zone matrix (GLDZM) zone distance entropy; GLDZM ldlge : GLDZM large distance low grey-level emphasis; GLDZM zdnunorm : GLDZM zone distance non-uniformity normalised. # : significantly different AUC from demographics and qCT model; ¶ : significantly different AUC from demographics and texture-based radiomics model.

    Journal: ERJ Open Research

    Article Title: Enhancing COPD classification using combined quantitative computed tomography and texture-based radiomics: a CanCOLD cohort study

    doi: 10.1183/23120541.00968-2023

    Figure Lengend Snippet: Receiver operating characteristic curves and SHapely Additive exPlanations (SHAP) analysis for COPD status with different input feature set combinations. qCT: quantitative computed tomography; AUC: area under the receiver operating characteristic curve; HU 15 : 15th percentile of the density histogram; TAC: total airway count; LAC: low-attenuation clusters; GLCM jointavg : grey-level co-occurrence matrix (GLCM) joint average; GLDZM zdentr : grey-level distance zone matrix (GLDZM) zone distance entropy; GLDZM ldlge : GLDZM large distance low grey-level emphasis; GLDZM zdnunorm : GLDZM zone distance non-uniformity normalised. # : significantly different AUC from demographics and qCT model; ¶ : significantly different AUC from demographics and texture-based radiomics model.

    Article Snippet: To extract the texture-based CT radiomic features, an in-house-developed pipeline that uses the Standardized Environment for Radiomics Analysis [ ] (MATLAB-based framework) was constructed to calculate the features in compliance with the Image Biomarker Standardisation Initiative (IBSI) [ ].

    Techniques: Computed Tomography

    Receiver operating characteristic curves and SHapely Additive exPlanations (SHAP) analysis for COPD severity with different input feature set combinations. qCT: quantitative computed tomography; AUC: area under the receiver operating characteristic curve; NJC: normalised join count; TAC: total airway count; WA%: wall area %; GLDZM zdnunorm : grey-level distance zone matrix (GLDZM) zone distance non-uniformity normalised; GLDZM ldlge : GLDZM large distance low grey-level emphasis; GLCM jointavg : grey-level co-occurrence matrix joint average; GLDZM zdnu : GLDZM zone distance non-uniformity. # : significantly different AUC from demographics and qCT model; ¶ : significantly different AUC from demographics and texture-based radiomics model.

    Journal: ERJ Open Research

    Article Title: Enhancing COPD classification using combined quantitative computed tomography and texture-based radiomics: a CanCOLD cohort study

    doi: 10.1183/23120541.00968-2023

    Figure Lengend Snippet: Receiver operating characteristic curves and SHapely Additive exPlanations (SHAP) analysis for COPD severity with different input feature set combinations. qCT: quantitative computed tomography; AUC: area under the receiver operating characteristic curve; NJC: normalised join count; TAC: total airway count; WA%: wall area %; GLDZM zdnunorm : grey-level distance zone matrix (GLDZM) zone distance non-uniformity normalised; GLDZM ldlge : GLDZM large distance low grey-level emphasis; GLCM jointavg : grey-level co-occurrence matrix joint average; GLDZM zdnu : GLDZM zone distance non-uniformity. # : significantly different AUC from demographics and qCT model; ¶ : significantly different AUC from demographics and texture-based radiomics model.

    Article Snippet: To extract the texture-based CT radiomic features, an in-house-developed pipeline that uses the Standardized Environment for Radiomics Analysis [ ] (MATLAB-based framework) was constructed to calculate the features in compliance with the Image Biomarker Standardisation Initiative (IBSI) [ ].

    Techniques: Computed Tomography

    Pearson's correlation coefficients (r) for CT features (all qCT and texture-based radiomics selected in the machine-learning models) with baseline spirometry measurements for the whole cohort

    Journal: ERJ Open Research

    Article Title: Enhancing COPD classification using combined quantitative computed tomography and texture-based radiomics: a CanCOLD cohort study

    doi: 10.1183/23120541.00968-2023

    Figure Lengend Snippet: Pearson's correlation coefficients (r) for CT features (all qCT and texture-based radiomics selected in the machine-learning models) with baseline spirometry measurements for the whole cohort

    Article Snippet: To extract the texture-based CT radiomic features, an in-house-developed pipeline that uses the Standardized Environment for Radiomics Analysis [ ] (MATLAB-based framework) was constructed to calculate the features in compliance with the Image Biomarker Standardisation Initiative (IBSI) [ ].

    Techniques: