Performance of the classifier in the validation cohort.

<p><b>(A)</b> ROC curve of the predictive classifier in the validation cohort for patients with PR or CR on first line systemic treatment (n = 38) compared to patients with PD (n = 15). Included in the classifier are miR-92a-3p, miR-92b-3p and four clinicopathological covariates. On the x-axis the false positive rate (1-specificity) is depicted, on the y-axis the sensitivity is depicted. The AUC of the model for predicting treatment response without miRs is 0.85, compared to 0.90 when including miR-92a-3p and miR-92b-3p to the model, this difference is not significant (p = 0.12). <b>(B)</b> ROC curve of the predictive classifier in the validation cohort for patients with SD on first line systemic treatment (n = 28) compared to patients with PD (n = 15). Included in the classifier are miR-30a-5p and therapy regimen. On the x-axis the false positive rate (1-specificity) is depicted, on the y-axis the sensitivity is depicted. The AUC of the model for predicting SD without miRs is 0.69, compared to 0.72 when including miR-30a-5p to the model, this difference is not significant (p = 0.37). <b>(C)</b> ROC curve of the predictive classifier in the validation cohort for patients with PR or CR on first line systemic treatment (n = 38) compared to patients with SD (n = 28). Included in the classifier are miR-17-5p, miR-92a-3p, miR-92b-3p and miR-98-5p and differentiation grade of the primary tumor. On the x-axis the false positive rate (1-specificity) is depicted, on the y-axis the sensitivity is depicted. The AUC of the model for predicting treatment response without miRs is 0.79, which increased significantly to 0.90 when including miR-17-5p, miR-92a-3p, miR-92b-3p and miR-98-5p to the model (p = 0.02).