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

Table 3 Time-dependent area under the receiver operating characteristic curve value of clinical variables, deep learning-based radiomics score, and integrated nomogram models for prognostication of event-free survival in the training and testing cohorts of hepatoblastoma patients receiving surgical resection

Models	Training set			Testing set
	1-year AUC (95%CI)	3-year AUC (95%CI)	5-year AUC (95%CI)	1-year AUC (95%CI)	3-year AUC (95%CI)	5-year AUC (95%CI)
Clinical variables
PRETEXT stage	0.615 (0.496-0.733)	0.631 (0.526-0.736)	0.625 (0.522-0.729)	0.653 (0.523-0.784)	0.649 (0.530-0.768)	0.639 (0.521-0.756)
Serum AFP concentration	0.586 (0.465-0.708)	0.632 (0.528-0.736)	0.624 (0.521-0.728)	0.635 (0.503-0.766)	0.663 (0.549-0.777)	0.650 (0.536-0.765)
DLBR score	0.539 (0.406-0.672)	0.589 (0.471-0.707)	0.609 (0.493-0.724)	0.588 (0.430-0.746)	0.615 (0.480-0.750)	0.641 (0.509-0.772)
Clinical model	0.607 (0.484-0.730)	0.637 (0.533-0.742)	0.631 (0.528-0.734)	0.650 (0.516-0.783)	0.660 (0.544-0.777)	0.649 (0.533-0.765)
Integrated nomogram	0.606 (0.471-0.742)	0.654 (0.537-0.771)	0.660 (0.544-0.775)	0.661 (0.509-0.814)	0.678 (0.548-0.808)	0.685 (0.556-0.813)

PRETEXT: 2017 PRE-Treatment EXTent of tumor; AFP: Alpha-fetoprotein; DLBR: Deep learning-based radiomics; CI: Confidence interval; AUC: Area under the receiver operating characteristic curve.