Research Article
Diagnostic Accuracy of Machine Learning-Based Radiomics in Grading Gliomas: Systematic Review and Meta-Analysis
Table 5
Result of multiple subgroup analysis of machine learning-based radiomics for grading gliomas.
| Subgroup | Study number | Patient number | Sensitivity | Specificity | PLR | NLR | Diagnostic odds ratio |
| All combined | 5 | 629 | 0.96 (0.93–0.98) | 0.90 (0.85–0.93) | 9.53 (3.55–25.57) | 0.07 (0.02–0.20) | 153.85 (32.36–731.44) |
| Populations | >100 | 2 | 507 | 0.98 (0.95–0.99) | 0.90 (0.85–0.94) | 12.099 (1.37–107.12) | 0.03 (0.02–0.06) | 393.81 (80.89–1917.3)_ | <100 | 3 | 122 | 0.88 (0.78–0.95) | 0.90 (0.77–0.96) | 7.89 (2.21–28.15) | 0.14 (0.05–0.39) | 65.13 (7.84–540.95) |
| Sequence | Single (CS or advanced) | 2 | 262 | 0.96 (0.93–0.98) | 0.81 (0.72–0.88) | 4.61 (3.14–6.77) | 0.09 (0.02–0.44) | 66.75 (10.33–431.19) | Multiple (CS and advanced) | 3 | 367 | 0.96 (0.91–0.99) | 0.97 (0.92–0.99) | 30.391 (11.585–79.726) | 0.04 (0.017–0.09) | 774.25 (202.54–2959.77) |
| Feature number | ≥Sample size | 2 | 82 | 0.85 (0.72–0.94) | 0.85 (0.69–0.95) | 5.71 (1.39–23.46) | 0.18 (0.07–0.52) | 33.76 (3.36–339.14) | <Sample size | 3 | 547 | 0.97 (0.95–0.99) | 0.90 (0.85–0.94) | 13.48 (2.56–71.12) | 0.03 (0.02–0.06) | 369.98 (19.68–6956.0) |
| Training and testing set | Training set | 3 | 331 | 0.94 (0.88–0.97) | 0.95 (0.89–0.98) | 12.91 (2.02–82.22) | 0.09 (0.02–0.47) | 154.56 (7.30–3276.9) | Training + testing set | 2 | 298 | 0.97 (0.94–0.99) | 0.81 (0.72–0.89) | 5.32 (2.55–11.09) | 0.05 (0.1–0.22) | 176.99 (63.76–491.30) |
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