Magnetic resonance imaging-based deep-learning radiomics score for survival prediction and risk stratification in pediatric hepatoblastoma receiving surgical resection

Table 1 Demographic characteristics of hepatoblastoma patients receiving surgical resection in the training and testing cohorts, n (%)

Variable	All subjects (n = 106)	Training set (n = 74)	Testing set (n = 32)	P value
Age, median (IQR)	2.1 (0.6)	2.1 (0.6)	2.0 (0.6)	0.397
Gender				0.478
Male	84 (79.2)	60 (81.1)	24 (75.0)
Female	22 (20.8)	14 (18.9)	8 (25.0)
Histological subtype				0.938
Mixed epithelial/mesenchymal	58 (54.7)	41 (55.4)	17 (53.1)
Fetal	44 (41.5)	30 (40.5)	14 (43.8)
Epithelial mixed	4 (3.8)	3 (4.1)	1 (3.1)
PRETEXT stage				0.275
I	3 (2.8)	1 (1.4)	2 (6.3)
II	52 (49.1)	35 (47.3)	17 (53.1)
III	51 (48.1)	38 (51.4)	13 (40.6)
Maximum tumor size, median (IQR), cm	5.1 (4.0)	5.1 (3.1)	5.5 (4.4)	0.964
Multifocality				0.870
Absent	65 (61.3)	45 (60.8)	20 (62.5)
Present	41 (38.7)	29 (39.2)	12 (37.5)
Local lymph node involvement				0.904
Absent	103 (97.2)	72 (97.3)	31 (96.9)
Present	3 (2.8)	2 (2.7)	1 (3.1)
Serum AFP concentration				0.419
≤ 1000 ng/mL	50 (47.2)	33 (44.6)	17 (53.1)
> 1000 ng/mL	56 (52.8)	41 (55.4)	15 (46.9)
DLBR score1, median (IQR)	0.002 (0.049)	0.002 (0.050)	0.002 (0.058)	0.625

¹Deep learning-based radiomics score was generated based on deep learning features related to specific events in hepatoblastomas.

PRETEXT: 2017 PRE-Treatment EXTent of tumor; AFP: Alpha-fetoprotein; DLBR: Deep learning-based radiomics; IQR: Interquartile range.