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. 2022 Jun 1;13:913703. doi: 10.3389/fimmu.2022.913703

Table 3.

Performance measurements generated by clinical model, DL models and radiomics models trained on different sequences in the internal test dataset.

Models AUC (95% CI) P-value Accuracy Specificity Sensitivity
Clinical Variables 0.840 (0.774-0.973) 0.047* 0.905 0.914 0.857
Radiomics_T1WI 0.773 (0.686-0.930) 0.036* 0.833 0.882 0.625
Radiomics_T2WI 0.786 (0.686-0.930) 0.038* 0.833 0.906 0.600
Radiomics_FLAIR 0.823 (0.632-0.897) 0.004* 0.786 0.929 0.500
Radiomics_DWI 0.803 (0.686-0.930) 0.014* 0.833 0.906 0.600
Radiomics_Combined 0.889 (0.715-0.946) 0.029* 0.857 0.936 0.636
DL_T1WI 0.721 (0.659-0.914) 0.035* 0.810 0.838 0.600
DL_T2WI 0.747 (0.560-0.861) 0.032* 0.738 0.806 0.333
DL_FLAIR 0.771 (0.659-0.914) 0.014* 0.810 0.903 0.546
DL_DWI 0.805 (0.686-0.930) 0.011* 0.833 0.861 0.667
DL_Combined 0.845 (0.715-0.946) 0.019* 0.857 0.886 0.714
Fusion 0.963 (0.874-0.999) Na 0.976 1.000 0.900

*P < 0.05. The paired Student t-test was used to compare the prediction performance of the prognosis in patients with anti-NMDAR encephalitis between the fusion model and all the other models (The fusion model is the reference).

AUC, area under the receiver operating characteristic curve; CI, confidence interval; DL, deep learning; anti-NMDAR, anti-N-methyl-D-aspartate receptor; Na, Not available.