Table 3.

Combination of Salivary Biomarkers for Pancreatic Cancer Selected by Logistic Regression Model

Biomarker combination	AUC (95% CI)	Sensitivity	Specificity	cv.err
Pancreatic cancer vs. Healthy control (KRAS, MBD3L2, ACRV1 and CDKL3)	0.973 (0.895 to 0.997)	0.933	1	0.067
Pancreatic cancer vs. Chronic pancreatitis (CDKL3, MBD3L2, KRAS)	0.981 (0.907 to 0.997)	0.967	0.967	0.067
Pancreatic cancer vs. non-cancer (KRAS, MBD3L2, ACRV1 and DPM1)	0.971 (0.911 to 0.994)	0.9	0.95	0.033

The logistic regression model was built based on the validated mRNA biomarkers for distinguishing pancreatic cancer from healthy controls, pancreatic cancer from chronic pancreatitis, and pancreatic cancer from the non-cancer group. The best models for each comparison, providing the highest discriminatory power with the simplest combination, are shown with the symbol of each biomarker. The sensitivity and specificity for each model was obtained by identifying the cutoff point in the predicted probabilities from the logistic regression that maximized the sum of the sensitivity plus specificity. In general, these cutoff points correspond well with the proportion of cancer patients evaluated in each model. Abbreviations: 95% CI: 95% Confidence interval; cv.err: cross validation error rate.