. Author manuscript; available in PMC: 2020 Dec 1.

Published in final edited form as: J Magn Reson Imaging. 2019 Apr 13;50(6):1893–1904. doi: 10.1002/jmri.26749

Table 2:

Results of the optimal radiomics features selected by using SVM-RFE and LASSO with a non-linear SVM classifier, respectively, for TFTY BCa recurrence prediction in both cohorts

Selection approach	Feature size	Cohort	^*Sen	^*Spe	^*Acc	AUC	95% CI		p-value
Selection approach	Feature size	Cohort	^*Sen	^*Spe	^*Acc	AUC	Lower	Upper	p-value
SVM-RFE	32	Training	84.00%	80.00%	82.00%	0.8593	0.8425	0.8810	<< 0.01
SVM-RFE	32	Validation	77.78%	73.83%	75.52%	0.8216	0.8130	0.8301	<< 0.01

LASSO	21	Training	73.74%	71.08%	72.41%	0.7504	0.7364	07613	< 0.05
LASSO	21	Validation	55.56%	75.00%	66.67%	0.7222	0.7003	0.7328	< 0.05

Sen, Spe and Acc indicate average sensitivity, specificity and accuracy obtained by using the selected radiomics features and a non-linear SVM classifier.