Why is the estimated result of AUC in roc.test() inconsistent with the original result?

Question

I made two ROC curves and one of the results is as follows:

> roc1 <- roc(cData$`<60`$resule, cData$`<60`$SOFA1)
Setting levels: control = survival, case = death
Setting direction: controls <cases
> roc1

Call:
roc.default(response = cData$`<60`$resule, predictor = cData$`<60`$SOFA1)

Data: cData$`<60`$SOFA1 in 26 controls (cData$`<60`$resule survival) <18 cases (cData$`<60`$resule death).
Area under the curve: 0.7692

Another result is as follows:

    > roc2 <- roc(cData$`<60`$resule, cData$`<60`$SOFA7)
    Setting levels: control = survival, case = death
    Setting direction: controls <cases
    > roc2



Call:
roc.default(response = cData$`<60`$resule, predictor = cData$`<60`$SOFA7)

Data: cData$`<60`$SOFA7 in 26 controls (cData$`<60`$resule survival) <2 cases (cData$`<60`$resule death).
Area under the curve: 0.9327

The result obtained after comparing through the roc.test() of the pROC package is:

> roc.test(roc1,roc2)

DeLong's test for two correlated ROC curves

data: roc1 and roc2
Z = 0.54339, p-value = 0.5869
alternative hypothesis: true difference in AUC is not equal to 0
sample estimates:
AUC of roc1 AUC of roc2
  0.9615385 0.9326923

The AUC of roc1 was 0.7692 and the AUC of roc2 was 0.9327, but why after comparing with roc.test(), the AUC becomes 0.9615385 0.9326923 respectively?