Table 2.
Results for accuracy to predict grade of PNETs.
| Study | TP | FP | FN | TN | SE | SPE | AUC | SE | 95%CI |
|---|---|---|---|---|---|---|---|---|---|
| Benedetti G(2021) (26) | 16 | 1 | 9 | 13 | 0.64[0.43-0.82] | 0.93[0.66-1.00] | 0.80 | 0.08 | 0.71-1.00 |
| Bian Y(2020) (27) | 49 | 19 | 3 | 33 | 0.94[0.84-0.99] | 0.63[0.49-0.76] | 0.86 | 0.04 | 0.84-0.85 |
| Bian Y(2020-MRI) (28) | 15 | 6 | 3 | 18 | 0.83[0.59-0.96] | 0.75[0.53-0.90] | 0.74 | 0.09 | 0.52-0.88 |
| Bian Y(2021-MRI) (29) | 39 | 19 | 22 | 77 | 0.64[0.51-0.76] | 0.80[0.71-0.88] | 0.78 | 0.04 | 0.70-0.85 |
| Canellas R(2018) (30) | NA | NA | NA | NA | NA | NA | 0.65 | NA | NA |
| Choi TW(2018) (31) | NA | NA | NA | NA | NA | NA | 0.774 | NA | NA |
| Gao X(2019) (32) | NA | NA | NA | NA | NA | NA | 0.893 | 0.007 | 0.886-0.912 |
| Gu DS(2019) (33) | 13 | 2 | 2 | 17 | 0.87[0.60-0.98] | 0.89[0.67-0.99] | 0.90 | 0.05 | 0.80-1.00 |
| Guo C(2018) (21) | 22 | 2 | 2 | 11 | 0.92[0.73-0.99] | 0.85[0.55-0.98] | 0.96 | 0.05 | 0.77-0.97 |
| Guo C(2019) (34) | 29 | 0 | 2 | 17 | 0.94[0.79-0.99] | 1.00[0.80-1.00] | 0.99 | 0.01 | 0.97-1.00 |
| Liu C(2022) (35) | 13 | 4 | 3 | 21 | 0.81[0.54-0.96] | 0.83[0.36-1.00] | 0.85 | 0.06 | 0.71-0.94 |
| Li W(2021) (36) | 13 | 2 | 13 | 23 | 0.50[0.30-0.70] | 0.92[0.74-0.99] | 0.70 | 0.08 | 0.54-0.85 |
| Liang WJ(2019) (37) | NA | NA | NA | NA | NA | NA | 0.891 | 0.028 | 0.772-0.961 |
| Luo Y(2020) (22) | 11 | 1 | 2 | 5 | 0.85[0.55-0.98] | 0.83[0.36-1.00] | 0.81 | 0.04 | 0.71-0.88 |
| Ohki K(2021) (38) | 21 | 3 | 1 | 8 | 0.95[0.77-1.00] | 0.73[0.39-0.94] | 0.86 | 0.01 | 0.86-0.90 |
| Onofrio MD(2019) (39) | 14 | 12 | 3 | 71 | 0.82[0.57-0.96] | 0.86[0.76-0.92] | 0.92 | 0.04 | 0.78-0.92 |
| Pulvirenti A(2021) (40) | 7 | 6 | 8 | 24 | 0.47[0.21-0.73] | 0.80[0.61-0.92] | NA | NA | NA |
| Ricci C(2021) (41) | 25 | 8 | 4 | 31 | 0.86[0.68-0.96] | 0.79[0.64-0.91] | 0.91 | 0.00 | 0.90-0.92 |
| Wang X(2022) (42) | 31 | 2 | 6 | 17 | 0.84[0.68-0.94] | 0.89[0.67-0.99] | 0.88 | 0.01 | 0.87-0.89 |
| Zhao ZR(2020) (43) | 16 | 2 | 2 | 20 | 0.89[0.65-0.99] | 0.91[0.71-0.99] | 0.88 | 0.07 | 0.70-0.96 |
| Zhou RQ(2019) (44) | NA | NA | NA | NA | NA | NA | 0.85 | NA | NA |
| Chiti G(2022) (45) | NA | NA | NA | NA | NA | NA | 0.82 | 0.09 | 0.62-1.00 |
| Mori M(2022) (46) | 6 | 8 | 3 | 14 | 0.67[0.30-0.93] | 0.64[0.41-0.83] | 0.72 | 0.07 | 0.59-0.85 |
| Park YJ(2023) (47) | 4 | 1 | 1 | 4 | 0.80[0.28-0.99] | 0.80[0.28-0.99] | 0.83 | 0.04 | 0.73-0.93 |
| Javed AA(2023) (48) | 39 | 6 | 6 | 18 | 0.87[0.73-0.95] | 0.75[0.53-0.90] | 0.80 | 0.01 | 0.70-0.90 |
| Zhu HB(2023) (49) | 57 | 11 | 8 | 37 | 0.88[0.77-0.95] | 0.77[0.63-0.88] | 0.86 | 0.06 | 0.79–0.94 |
TP, true positive; FP, false positive; TN, true negative; FN, false negative; SE, sensitivity; SPE, specificity; AUC, area under the curve; SE, standard error; 95%CI, 95% confidence interval; NA, not applied.