Magnetic resonance imaging-based deep learning model for prediction of the neoadjuvant chemotherapy response and survival prognosis in adolescents with osteosarcoma

Table 3 Performance of the deep learning-based signature and integrated nomogram model in prediction of overall survival for adolescents and young adults with osteosarcoma in the training and testing cohorts

Model	Time	Training cohort		Testing cohort
		C-index (95%CI)	Brier score (95%CI)	C-index (95%CI)	Brier score (95%CI)
Nomogram model1	3-year	0.773 (0.638-0.908)	14.9 (9.3-20.5)	0.688 (0.421-0.955)	16.1 (2.9-29.4)
	5-year	0.855 (0.725-0.984)	16.6 (9.3-23.9)	0.831 (0.527-1.000)	20.8 (5.7-35.8)
DL-based signature2	3-year	0.773 (0.638-0.908)	14.9 (9.3-20.5)	0.688 (0.421-0.955)	17.0 (3.2-30.9)
	5-year	0.855 (0.725-0.984)	16.8 (9.4-24.3)	0.831 (0.527-1.000)	21.9 (6.4-37.3)

¹The nomogram model was constructed by prognostic clinical variables selected by multivariate Cox regression analysis on overall survival and the deep learning signature originated from diagnosis prediction.

²The deep learning signature was based on the best deep learning-radiological model considering AUC of the testing cohort from histological diagnosis identification.

C-index: Harrell’s concordance index; CI: Confidence interval; DL: Deep learning.