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. 2020 Sep 21;27(12):1665–1678. doi: 10.1016/j.acra.2020.09.004

Table 6.

Comparison of Model Performance With and Without CT Features for Prediction of Clinical Outcomes

Clinical Outcomes Selected features Performance
With CT Features Without CT Features
Duration of hospitalization (≤4 weeks) %NLLV * (12) CV RMSE 0.878 0.916
Age (11)
Creatine_kinase_isoenzyme (7.9)
Hypertension (6.9)
PO2 (6.9)
Duration of oxygen inhalation (≤4 weeks) Age (15) CV RMSE 0.920 0.940
%NLLV * (11)
Creatine_kinase_isoenzyme (8.1)
PO2 (7.6)
Procalcitonin (6.7)
Duration of sputum nucleic acid test positive (≤4 weeks) Age (14) CV RMSE 0.901 NA
History_of_surgery (7.8)
Creatine_kinase_isoenzyme (7.0)
Need of ICU Age (23)
Procalcitonin (17) AUC 0.945 (0.941-0.948) 0.932 (0.927-0.936)
Hypertension (16) Specificity 0.843 0.845
%NLLV * (12) Sensitivity 0.884 0.879
C_reactive_protein (11) Accuracy 0.864 (0.858-0.870) 0.862 (0.856-0.868)
Duration of ICU (≤2 weeks) HIST_var_lesion * (5.9) CV RMSE 0.688 0.798
Blood_oxygen_saturation (5.3)
Prediction of prognosis (partial recovery vs prolonged recovery) Age (19)
%NLLV * (15) AUC 0.960 (0.957-0.963) 0.806 (0.800-0.813)
HIST_mad_residual * (12) Specificity 0.892 0.792
HIST_kurt_residual * (9.4) Sensitivity 0.907 0.659
HIST_quant_range_residual * (8.5) Accuracy 0.899 (0.895-0.904) 0.726 (0.719-0.733)

Features with suffix “residual” are those for the nonlesion lung. CT features are marked with asterisks. Numbers in the parenthesis are the importance of the features, obtained from Boruta's selection process. For regression problems, the importance was evaluated based on the mean increase in MSE. For classification problems, it was based on the mean decrease in accuracy.