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

Table 1 The demographic and clinical characteristics between training and testing cohorts, n (%)

Characteristic	All subjects (n = 103)	Training cohort (n = 72)	Testing cohort (n = 31)	P value
Age, median (IQR), months	24 (17)	24 (17)	25 (18)	0.701
Gender				0.905
Male	81 (78.6)	57 (79.2)	24 (77.4)
Female	22 (21.4)	15 (20.8)	7 (22.6)
MYCN amplification				0.518
Non-amplified	91 (88.3)	64 (88.9)	27 (87.1)
Amplified	12 (11.7)	8 (11.1)	4 (12.9)
INRGSS stage				0.706
L1	47 (45.6)	33 (45.8)	14 (45.2)
L2	56 (54.4)	39 (54.2)	17 (54.8)
Histological differentiation				0.288
Differentiated	38 (36.9)	26 (36.1)	12 (38.7)
Undifferentiated or poorly differentiated	65 (63.1)	46 (63.9)	19 (61.3)
Infiltrating across the midline				0.880
Absence	54 (52.4)	38 (52.8)	16 (51.6)
Presence	49 (47.6)	34 (47.2)	15 (48.4)
Calcification				0.960
Absence	32 (31.1)	22 (30.6)	10 (32.3)
Presence	71 (68.9)	50 (69.4)	21 (67.7)
DL-based signature, median (IQR)	-0.243 (0.154)	-0.244 (0.153)	-0.243 (0.229)	0.311
EFS, median (IQR), month	72 (78)	71 (78)	72 (78)	0.437

IQR: Interquartile range; INRGSS: International Neuroblastoma Risk Grouping Staging System; DL: Deep learning; EFS: Event-free survival.

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.

Table 3 Kaplan-Meier analysis of histologically identified and nomogram model-predicted MYCN amplification for the prediction of long-term survival in neuroblastomas

Variable	Subgroup	Training database		Testing database		Log rank test
		Mean survival time	95%CI	Mean survival time	95%CI
Histological MYCN amplification1	Non-amplified	83.476	79.401-87.550	85.458	79.308-91.609	0.031
	Amplified	63.938	52.568-75.308	82.538	66.945-98.130
Predicted MYCN amplification2	Non-amplified	85.504	81.349-89.659	83.366	76.510-90.222	0.002
	Amplified	60.239	50.833-69.644	89.230	78.847-99.614

¹The histological MYCN amplification was identified by postoperative histopathological specimens.

²The predicted MYCN amplification was derived from two categories from the nomogram model-predicted probabilities based on the optimal Youden index of receiver-operating characteristic analysis, where the lower group indicated non-amplified MYCN status and the higher group indicated amplified MYCN status.

CI: Confidence interval.