Table 1 Overview of related literature
Publication | n | Methods | Input | Performance |
|---|---|---|---|---|
Cai et al. [7] | 99 | random forest | automated lung and lesion segmentation, 40 clinical parameters | cv10 AUC = 0.96 |
Meng et al. [8] | 366 | CNN | CT, sex, age, severity grade, chronic disease status | test set AUC = 0.94 |
Ning et al. [9] | 1521 | DNN, CNN, logistic regression | CT, 130 clinical features | cv10 AUC = 0.86 |
Fu et al. [10] | 64 | SVM | CT-derived radiomics | leave-one-out AUC = 0.83 |
Li et al. [11] | 217 | CNN, logistic regression, SVM, decision tree, random forest | CT-derived radiomics | test set AUC = 0.86 |
Yue et al. [12] | 52 | logistic regression, random forest | CT-derived radiomics | independent test set AUC = 0.97 |
Wu et al. [13] | 725 | logistic regression | semi-automatically derived CT findings, 7 clinical features | independent test set AUC = 0.84 to 0.93 |
Fang et al. [14] | 1040 | CNN, RNN | CT, 61 clinical parameters | cv5 AUC = 0.92, domain adaptation AUC = 0.86 |
Wang et al. [15] | 1051 | CNN, DNN, random forest | CT, 15 clinical parameters | test set AUC = 0.81 to 0.83 |
Wang et al. [16] | 188 | logistic regression | CT-derived radiomics, 24 clinical parameters | test set AUC = 0.87 |
Revel et al. [17] | 10735 | logistic regression | manually-derived CT findings, 7 clinical parameters | AUC = 0.64 |
Lassau et al. [19] | 1003 | DNN | CT, 5 clinical parameters | test set AUC = 0.79 |
Shiri et al. [20] | 14339 | random forest | CT-derived radiomics | test set AUC = 0.83 |
Kienzle et al. [21] | 2476 | CNN | CT | test set F1 = 0.49 |
Duan et al. [23] | 44 | random forest | CT-derived radiomics | cv10 AUC = 0.99 to 1.00 |