Table 2:

Diagnostic performance of machine-learning classification in training and validation datasets

	Tumor Type (Pathologic Diagnosis)		Diagnostic Performance
	SCC	IP	Diagnostic Performance
Model prediction for training dataset
SCC	16	2	Accuracy	90.9%^{^a}
SCC	16	2	Sensitivity	94.1%
IP	1	14	Specificity	87.5%
IP	1	14	PPV	88.9%
Total	17	16	NPV	93.3%
Model prediction for validation dataset
SCC	6	1	Accuracy	84.6%^{^a}
SCC	6	1	Sensitivity	85.7%
IP	1	5	Specificity	83.3%
IP	1	5	PPV	85.7%
Total	7	6	NPV	83.3%
Model prediction for entire cohort
SCC	22	3	Accuracy	89.1%
SCC	22	3	Sensitivity	91.7%
IP	2	19	Specificity	86.4%
IP	2	19	PPV	88.0%
Total	24	22	NPV	90.5%

Note:—NPV indicates negative predictive value; PPV, positive predictive value.
↵a With a 2-tailed test of population proportion, the accuracies for the training and validation datasets were not significantly different (P = .537).