Article Figures & Data
Figures
- FIG 1.
IDH status forest plot of included studies with an AUC.
- FIG 2.
1p19q status forest plot of included studies with an AUC.
Tables
First Author and Year Derived Aim Key Findings Fukuma 201922 To integrate CNN deep learning features with conventional radiomic features Conventional radiomic features: accuracy (mean ± 95% CI) = 71.7% ± 8.3%; AUC (± 95% CI) = 0.718 ± 0.139CNN features: accuracy = 69.6% ± 5.6%; AUC = 0.619 ± 0.132CNN and conventional radiomic features: accuracy = 73.1% ± 9.4%; AUC = 0.699 ± 0.145 Gihr 202023 To determine if intensity features relate to IDH status Entropy, a second-order histogram parameter of the ADC volume was significant: IDH-mut versus IDH wild-type, mean ± SD = 5.5 ± 0.63 vs 4.75 ± 0.69; P = .0144 Jakola 201824 To determine if texture features can predict IDH status on FLAIR Homogeneity and volume could classify IDH status with an AUC = 0.940 (85% sensitivity, 100% specificity) using the generalized linear model Kim 202025 To determine if DWI- and DSC perfusion-based image integration with standard imaging (T1WI postcontrast and FLAIR) can improve classification Integration increased the AUC (95% CI) = 0.747 (0.663–0.832); (53.6% sensitivity and 86.7% specificity) from 0.705 (0.613–0.796) (43.9% sensitivity and 88.8% specificity) compared with conventional MR imaging radiomics Li 201727 To determine if integration of deep learning features into the radiogenomic pipeline improves classification Conventional radiomics produced an AUC = 0.85 (sensitivity of 82.9%, specificity of 73.5%)CNN deep learning–derived features plus conventional radiomic features with feature selection produced an AUC = 0.95 (sensitivity of 94.4%; specificity of 86.7%) Lu 201828 To determine the best ML classifier Linear SVM classified IDH status with an AUC = 0.936 (sensitivity of 85.7%, specificity of 93.0%) Park 202029 To determine if DTI improves classification when added to conventional radiomics Addition of DTI radiomic features to conventional imaging radiomics increased the AUC (95% CI) = 0.900 (0.855–0.945) from 0.835 (0.773–0.896) Ren 201930 To compare radiomic, VASARI, and radiomic plus VASARI features derived from FLAIR, ADC, eADC, and CBF Radiomics: AUC (95% CI) = 0.931 (0.842–1); sensitivity of 100%, specificity of 85.71%VASARI: AUC = 0.843 (sensitivity of 91.67%; specificity of 61.90%)Radiomics plus VASARI: AUC = 0.888 (0.786–0.989); sensitivity of 94.44% and specificity of 71.43% Yu 201732 To classify using the improved genetic algorithm for feature selection and leave-one-out cross-validation method in WHO grade II LGG Using the proposed method and the SVM ML classifier, an AUC = 0.71 (sensitivity = 56% and specificity = 74%) was achieved Zhou 201734 To determine if VASARI annotations were superior to standard radiomic classification analysis IDH classification through texture features found an AUC (± 95% CI) = 0.79 ± 0.02; sensitivity 90%, specificity of 89%IDH classification through VASARI features, AUC = 0.73 ± 0.02; sensitivity of 69%, specificity of 69% Zhang 201833 To classify by conventional radiomics AUC = 0.830 (sensitivity = 82%, specificity = 92%) using SVM Note:—eADC indicates exponential ADC.
First Author and Year Derived Aim Key Findings Han 202035 To determine if clinical and standard imaging factors improve classification The AUC (95% CI) = 0.753 (0.654–0.852) for clinical plus radiomic features versus AUC = 0.760 (0.663-0.857) for just radiomic features; radiomic features were superior to clinical features alone, AUC = 0.627 (0.551–0.703) Kocak 202026 To determine the best ML classifier The neural network produced the highest AUC (95% CI) = 0.869 (0.751–0.981); sensitivity of 87.5%, specificity of 75.8% Lu 201828 To determine the best ML classifier Classification occurred with an AUC = 0.92 (sensitivity of 88.5%, specificity of 86.2%) using quadratic SVM Shofty 201831 To determine the best ML classifier Classification occurred with an AUC = 0.87 (sensitivity of 92%, specificity of 83%) using ensemble bagged trees classifier Zhou 201734 To determine if VASARI annotations were superior to standard radiomic analysis for classification Texture features classified with an AUC (± 95% CI) = 0.96 ± 0.01; sensitivity of 90% ± 2%, specificity of 89% ± 2%VASARI features classified with an AUC = 0.78 ± 0.02; sensitivity of 72% ± 3%, specificity of 67% ±3% Fukuma 201922 To determine if integration of CNN deep learning with radiomic features improved classification Conventional radiomic features (± 95% CI): accuracy = 59.0 ± 9.0%; AUC = 0.656 ± 0.113CNN features: accuracy = 84.0 ± 9.3%; AUC = 0.868 ± 0.099CNN and conventional radiomic features: accuracy = 79.8 ± 11.0%; AUC = 0.861 ± 0.116
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