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Summary of studies applying radiomics for renal tumor differentiation.
Ann And Ml Based Svm Classifiers, supplied by Kocak Farma, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Summary of studies applying radiomics for renal tumor differentiation.

Journal: Cancers

Article Title: Radiomics Applications in Renal Tumor Assessment: A Comprehensive Review of the Literature

doi: 10.3390/cancers12061387

Figure Lengend Snippet: Summary of studies applying radiomics for renal tumor differentiation.

Article Snippet: Kocak et al. 2018 [ ] , , Three-phasic CECT , 68 RCC patients , , , , , 5.9 (3.3–8.1) , 5.9 (2.0–12.3) , ANN and ML-based SVM classifiers , 275 texture features extracted: , Distinguishing the three main RCC subtypes.

Techniques: Imaging, Comparison, Biomarker Discovery, Extraction, Diagnostic Assay, Mutagenesis, Selection

Summary of studies applying radiomics to predict nuclear grade in ccRCC.

Journal: Cancers

Article Title: Radiomics Applications in Renal Tumor Assessment: A Comprehensive Review of the Literature

doi: 10.3390/cancers12061387

Figure Lengend Snippet: Summary of studies applying radiomics to predict nuclear grade in ccRCC.

Article Snippet: Kocak et al. 2018 [ ] , , Three-phasic CECT , 68 RCC patients , , , , , 5.9 (3.3–8.1) , 5.9 (2.0–12.3) , ANN and ML-based SVM classifiers , 275 texture features extracted: , Distinguishing the three main RCC subtypes.

Techniques: Imaging, Biomarker Discovery, Selection, Filtration, Diffusion-based Assay, Variant Assay, Transformation Assay, Comparison

Summary of studies applying radiomics and gene expression-based models to predict progression in ccRCC.

Journal: Cancers

Article Title: Radiomics Applications in Renal Tumor Assessment: A Comprehensive Review of the Literature

doi: 10.3390/cancers12061387

Figure Lengend Snippet: Summary of studies applying radiomics and gene expression-based models to predict progression in ccRCC.

Article Snippet: Kocak et al. 2018 [ ] , , Three-phasic CECT , 68 RCC patients , , , , , 5.9 (3.3–8.1) , 5.9 (2.0–12.3) , ANN and ML-based SVM classifiers , 275 texture features extracted: , Distinguishing the three main RCC subtypes.

Techniques: Expressing, Functional Assay, RNA Sequencing, Microarray, Gene Expression, Histopathology, Construct