Table 1.
Performance metrics for the machine learning classifier
| Method | AUROC (95% CI) | Sensitivity (95% CI) | Specificity (95% CI) | PPV | NPV |
|---|---|---|---|---|---|
| Radiomics, all features | 0.79 (0.74-0.85) | 0.60 (0.49-0.70) | 0.84 (0.79-0.88) | 0.54 (0.43-0.64) | 0.87 (0.83-0.91) |
| Radiomics | 0.81 (0.76-0.87) | 0.67 (0.56-0.76) | 0.83 (0.78-0.87) | 0.57 (0.47-0.66) | 0.88 (0.84-0.92) |
| Radiomics + MGMT | 0.89 (0.85-0.93) | 0.72 (0.61-0.81) | 0.90 (0.86-0.94) | 0.71 (0.60-0.80) | 0.91 (0.86-0.94) |
| MGMT + age + sex* | 0.60 (0.53-0.66) | 0.67 (0.56-0.77) | 0.27 (0.22-0.33) | 0.23 (0.18-0.29) | 0.71 (0.61-0.80) |
Abbreviations: AUROC = area under the receiver operating characteristic; CI = confidence interval; MGMT = O6-methylguanine–DNA methyltransferase; NPV = negative predictive value; PPV = positive predictive value.
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Logistic regression classifier was used for clinical variables and fit using 3-fold cross-validation.
The positive class is that of pseudoprogression. Radiomic analysis was used to predict histopathologically confirmed pseudoprogression in glioblastoma patients. Results on testing sets used in the validation folds not used in the training are shown for all methods.