Preoperative prediction of lymphovascular and perineural invasion in gastric cancer using spectral computed tomography imaging and machine learning

Table 3 Performance of the individual nomogram and single independent factors

	AUC (95%CI)	Sensitivity	Specificity	PPV	NPV	Accuracy
Nomogram
Training cohort	0.918 (0.866-0.954)	85.9	83.3	89.2	78.6	84.9
Validation cohort	0.874 (0.784-0.936)	87.5	75.9	87.5	75.9	83.5
CT-EMVI
Training cohort	0.762 (0.691-0.824)	58.5	93.9	93.9	58.5	72.1
Validation cohort	0.735 (0.628-0.825)	64.3	82.8	87.8	54.5	70.6
CT-T
Training cohort	0.793 (0.725-0.851)	78.3	80.3	86.5	69.7	79.1
Validation cohort	0.816 (0.717-0.892)	83.9	79.3	88.7	71.9	82.4
VP-70kev
Training cohort	0.888 (0.831-0.931)	84.9	71.2	82.6	74.6	79.7
Validation cohort	0.804 (0.703-0.882)	92.9	55.2	80.0	80.0	80.0
EP-NIC
Training cohort	0.824 (0.758-0.877)	82.1	62.1	77.7	68.3	74.4
Validation cohort	0.824 (0.726-0.898)	85.7	58.6	80.0	68.0	76.5

CT-EMVI: Computed tomography-detected extramural vein invasion; VP-70 keV: Single-energy computed tomography value of 70 keV in the venous phase; EP-NIC: Ratio of the standardized iodine concentration in the equilibrium phase; AUC: Area under the receiver operating characteristic curve; CI: Confidence interval; PPV: Positive predictive value; NPV: Negative predictive value.