Table 3.
Summary of the results evaluated in the reviewed studies.
| Study and year | Method | Algorithm | Dataset/HGG-LGG | MRI sequence | Best performance | Limitation | |||
|---|---|---|---|---|---|---|---|---|---|
| AUC | DA (%) | Sen (%) | Spe (%) | ||||||
| Cho et al. 2018 | Classic machine learning | Multiple algorithms | WHO II–IV (n = 285)/210-75 | T1, T1-C, T2, T2- FLAIR | 0.94 | 92.92 | 97.86 | 79.11 | No dataset separation information for training and testing cohort. Sample imbalance size between LGG and HGG. |
|
| |||||||||
| Tian et al. 2018 | Classic machine learning | SVM | WHO II–IV gliomas (n = 153)/111-42 | Multiparametric | 0.99 | 96.80 | 96.40 | 97.30 | Sample imbalance sample size between LGG and HGG. |
|
| |||||||||
| Hashido et al. 2018 | Classic machine learning | Logistic regression | WHO II–IV (n = 46)/31-15 | ASL, PWI (DSC) | 0.96 | NA | 89.30 | 92.90 | Small sample size. Small sample size used in the training set. Large feature number than the total sample size. |
|
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| Vamvakas et al. 2019 | Classic machine learning | SVM | WHO I–IV (n = 40) 20-20 | Multiparametric | 0.96 | 95.50 | 95 | 96 | Small sample size. |
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| Zhao et al. 2020 | Classic machine learning | RF | WHO II-III gliomas (n = 36) 17-19 | T1-C, T2- FLAIR | 0.86 | 78.10 | 78.30 | 77.80 | Small sample size. Large feature number compared to the total sample size. |