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cgita toolbox  (MathWorks Inc)


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    MathWorks Inc cgita toolbox
    Cgita 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/cgita toolbox/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    cgita toolbox - by Bioz Stars, 2026-03
    90/100 stars

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    Image Search Results


    Summary of radiomics and AI-based radiomics studies conducted on lymphoma using 18 F-FDG-PET/CT images. All studies included in this Table are retrospective, except Ceriani et al. [ <xref ref-type= 63 ], which is prospective in its validation. This distinction highlights one of the key limitations of the research landscape, as retrospective studies may introduce biases that can affect the interpretation of the findings." width="100%" height="100%">

    Journal: Cancers

    Article Title: Enhancing Lymphoma Diagnosis, Treatment, and Follow-Up Using 18 F-FDG PET/CT Imaging: Contribution of Artificial Intelligence and Radiomics Analysis

    doi: 10.3390/cancers16203511

    Figure Lengend Snippet: Summary of radiomics and AI-based radiomics studies conducted on lymphoma using 18 F-FDG-PET/CT images. All studies included in this Table are retrospective, except Ceriani et al. [ 63 ], which is prospective in its validation. This distinction highlights one of the key limitations of the research landscape, as retrospective studies may introduce biases that can affect the interpretation of the findings.

    Article Snippet: Lue et al. [ ] , 2020 , 35 , Response to Therapy, OS, PFS , Retrospective , CGITA (MATLAB 2012a) , HL , Statistical analysis , Treatment Response Prediction: HIR_GLRM PET : OR = 36.4, p = 0.014 RLNU_GLRM CT : OR = 30.4, p = 0.020 PFS: INU_GLRM PET : HR = 9.29, p = 0.023 Wavelet SRE_GLRM CT : HR = 18.40, p = 0.012 OS: ZSNU_GLSZM PET : HR = 41.02, p = 0.001 A prognostic stratification model: PFS ( p < 0.001), OS ( p < 0.001). , Predictive features for treatment response HIR_GLRMPET and RLNU_GLRMCT. Independent predictive features for survival: ZSNU(GLSZM PET ), INU(GLRM PET ), and wavelet SRE (GLRM CT ).

    Techniques: Biomarker Discovery, Introduce, Extraction, Software, Diagnostic Assay, Selection, Imaging, Positron Emission Tomography, Gene Expression, Amplification, Marker, Transformation Assay

    PRISMA literature list

    Journal: International Journal of Surgery (London, England)

    Article Title: The gap before real clinical application of imaging-based machine-learning and radiomic models for chemoradiation outcome prediction in esophageal cancer: a systematic review and meta-analysis

    doi: 10.1097/JS9.0000000000000441

    Figure Lengend Snippet: PRISMA literature list

    Article Snippet: Yip 2016 , 45-50.4 Gy + cisplatin and 5-FU/irinotecan/paclitaxel , Retrospective single-center , None , None , MATLAB-CGITA , Manual , GLCM+SRHGRE+HGRE+SZHGE+HGZE , Response to nCRT , Histopathology , NA.

    Techniques: Biomarker Discovery, Extraction, Software, Imaging, Histopathology, Control

    Main information about the articles included for assessment of radiomics in cervical cancer.

    Journal: The British Journal of Radiology

    Article Title: Radiomics in cervical and endometrial cancer

    doi: 10.1259/bjr.20201314

    Figure Lengend Snippet: Main information about the articles included for assessment of radiomics in cervical cancer.

    Article Snippet: Tsujikawa et al , 2017 , R , 83 , PET/CT , First, and higher order , PET/CT textural features may reflect the differences in histological architecture between CC subtypes. , CGITA MATLAB.

    Techniques: Imaging, Software, Biomarker Discovery, Derivative Assay