Deep learning radiomic analysis in the prediction of MYCN status and survival outcome in children with neuroblastoma

Table 2 Diagnostic performance of clinical predictors, deep learning-based signature, and integrated nomogram model for prediction of MYCN amplification

Model	Training cohort (n = 72)				Testing cohort (n = 31)
	AUC (95%CI)	Accuracy	Sensitivity	Specificity	AUC (95%CI)	Accuracy	Sensitivity	Specificity
Histological differentiation	0.560 (0.495-0.625)	47.2 (34/72)	62.5 (5/8)	45.3 (29/64)	0.433 (0.337-0.529)	48.4 (15/31)	25.0 (1/4)	51.9 (14/27)
Infiltrating across midline	0.603 (0.536-0.669)	65.3 (47/72)	62.5 (5/8)	65.6 (42/64)	0.581 (0.485-0.678)	61.3 (19/31)	50.0 (2/4)	63.0 (17/27)
Calcification	0.518 (0.450-0.587)	88.9 (64/72)	1.3 (1/8)	98.4 (63/64)	0.505 (0.408-0.603)	87.1 (27/31)	0.0 (0/4)	100.0 (27/27)
DL-based signature	0.958 (0.929-0.987)	91.7 (66/72)	87.5 (7/8)	92.2 (59/64)	0.803 (0.723-0.883)	77.4 (24/31)	75.0 (3/4)	77.8 (21/27)
Nomogram model1	0.959 (0.930-0.988)	95.8 (69/72)	87.5 (7/8)	96.9 (62/64)	0.819 (0.740-0.898)	74.2 (23/31)	75.0 (3/4)	74.1 (20/27)

¹The nomogram model integrated significant clinical predictors and the deep learning-based signature to predict MCYN amplification.

AUC: Area under the receiver operating characteristic curve; DL: Deep learning; CI: Confidence interval.