Table 2.
Accuracies of classification of brain tumors by SVM analysis
| Comparison | Instances classified in the test sets, n (%) |
|
|---|---|---|
| Correctly | Incorrectly | |
| GBM vs nonneoplastic controls | 31 (91.2) | 3 (8.8) |
| Metastasis vs nonneoplastic controls | 88 (98.9) | 1 (1.1) |
| GBM and metastasis vs nonneoplastic controls | 105 (97.2) | 3 (2.8) |
| GBM vs metastasis | 89 (95.7) | 4 (4.3) |
| GBM vs non-GBM (all others) | 102 (94.5) | 6 (5.5) |
| Metastasis vs nonmetastasis (all others) | 100 (92.6) | 8 (7.4) |
| Breast vs lung metastasis | 51 (68.9) | 23 (31.1) |
An automatically generated classifier (SVM with RBF kernel) is used for the classification. Cross-validation approach in which the specimens are split into the train and test groups is used for calculating the accuracies. Used as input data for the analysis were levels of miR-10b, miR-21, miR-125b, miR-141, miR-200a, miR-200b, and miR-200c in CSF.