Intra- and peritumoral MRI radiomics assisted in predicting radiochemotherapy response in metastatic cervical lymph nodes of nasopharyngeal cancer

Table 3 Diagnostic performance of radiomic models in the training and validation sets

Models	Cohorts	AUC	Sen	Spe	ACC
Intra	Training	0.910 (0.837, 0.958)	0.839 (0.716, 0.923)	0.891 (0.764, 0.963)	0.863 (0.780, 0.922)
Intra	Validation	0.737 (0.580, 0.859)	0.625 (0.405, 0.812)	0.895 (0.668, 0.986)	0.744 (0.588, 0.864)
Peri	Training	0.887 (0.809, 0.941)	0.714 (0.577, 0.827)	0.934 (0.821, 0.986)	0.814 (0.724, 0.883)
Peri	Validation	0.794 (0.643, 0.902)	0.375 (0.187, 0.594)	0.895 (0.668, 0.986)	0.605 (0.444, 0.750)
Intra + Peri	Training	0.934 (0.867, 0.974)	0.946 (0.851, 0.988)	0.782 (0.636, 0.890)	0.873 (0.791, 0.930)
Intra + Peri	Validation	0.774 (0.621, 0.887)	0.750 (0.532, 0.902)	0.737 (0.488, 0.908)	0.744 (0.588, 0.864)
Clinical-radiomic	Training	0.941 (0.877, 0.978)	0.929 (0.827, 0.980)	0.848 (0.711, 0.936)	0.892 (0.815, 0.944)
Clinical-radiomic	Validation	0.783 (0.631, 0.894)	0.667 (0.446, 0.843)	0.842 (0.604, 0.966)	0.744 (0.588, 0.864)

The 95% confidence interval was shown in parentheses. AUC the area under the curve, Intra intratumoral, Peri peritumoral, Sen sensitivity, Spe specificity, ACC accuracy

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