Table 4. Diagnostic performances of the different reconstruction approaches based on CT-FFR.
| Parameter | HIR | MBIR | C-DLR | SR-DLR | P value | ||
|---|---|---|---|---|---|---|---|
| HIR vs. SR-DLR | MBIR vs. SR-DLR | C-DLR vs. SR-DLR | |||||
| Calcified-related stenoses (n=35) | |||||||
| TP | 14 | 11 | 14 | 14 | – | – | – |
| TN | 8 | 12 | 10 | 15 | – | – | – |
| FP | 12 | 8 | 10 | 5 | – | – | – |
| FN | 1 | 4 | 1 | 1 | – | – | – |
| Sensitivity (%) | 93 [79, 100] | 73 [50, 94] | 93 [79, 100] | 93 [79, 100] | >0.99 | 0.03 | >0.99 |
| Specificity (%) | 40 [19, 63] | 60 [38, 81] | 50 [28, 73] | 75 [55, 94] | 0.005 | 0.40 | 0.03 |
| PPV (%) | 54 [35, 73] | 58 [35, 80] | 58 [38, 78] | 74 [53, 94] | 0.03 | 0.10 | 0.08 |
| NPV (%) | 89 [63, 100] | 75 [53, 94] | 91 [70, 100] | 94 [80, 100] | 0.47 | 0.12 | 0.48 |
| Accuracy (%) | 63 [46, 77] | 66 [49, 80] | 69 [54, 83] | 83 [69, 94] | 0.01 | 0.09 | 0.04 |
| AUC | 0.67 [0.54, 0.80] | 0.67 [0.51, 0.83] | 0.72 [0.59, 0.85] | 0.84 [0.72, 0.96] | 0.004 | 0.07 | 0.04 |
| Stents (n=10) | |||||||
| TP | 4 | 3 | 5 | 5 | – | – | – |
| TN | 1 | 1 | 1 | 3 | – | – | – |
| FP | 4 | 4 | 4 | 2 | – | – | – |
| FN | 1 | 2 | 0 | 0 | – | – | – |
| Sensitivity (%) | 80 [33, 100] | 60 [0, 100] | 100 [100, 100] | 100 [100, 100] | >0.99 | 0.03 | >0.99 |
| Specificity (%) | 20 [0, 67] | 20 [0, 67] | 20 [0, 67] | 60 [0, 100] | 0.16 | 0.78 | 0.16 |
| PPV (%) | 50 [14, 86] | 43 [0, 83] | 56 [22, 89] | 71 [33, 100] | 0.27 | 0.35 | 0.29 |
| NPV (%) | 50 [0, 100] | 33 [0, 100] | 100 [100, 100] | 100 [100, 100] | 0.82 | 0.08 | >0.99 |
| Accuracy (%) | 50 [20, 80] | 40 [10, 70] | 60 [30, 90] | 80 [50, 100] | 0.15 | 0.17 | 0.36 |
| AUC | 0.50 [0.22, 0.78] | 0.40 [0.09, 0.71] | 0.60 [0.40, 0.80] | 0.80 [0.56, 1.00] | 0.17 | 0.26 | 0.31 |
| All (n=45) | |||||||
| TP | 18 | 14 | 19 | 19 | – | – | – |
| TN | 9 | 13 | 11 | 18 | – | – | – |
| FP | 16 | 12 | 14 | 7 | – | – | – |
| FN | 2 | 6 | 1 | 1 | – | – | – |
| Sensitivity (%) | 90 [75, 100] | 70 [48, 90] | 95 [83, 100] | 95 [83, 100] | >0.99 | 0.03 | >0.99 |
| Specificity (%) | 36 [17, 55] | 52 [32, 72] | 44 [24, 64] | 72 [53, 89] | <0.001 | 0.27 | 0.006 |
| PPV (%) | 53 [35, 69] | 54 [34, 73] | 58 [40, 74] | 73 [55, 89] | 0.008 | 0.06 | 0.02 |
| NPV (%) | 82 [56, 100] | 68 [47, 89] | 92 [73, 100] | 95 [83, 100] | 0.70 | 0.03 | 0.48 |
| Accuracy (%) | 60 [44, 73] | 60 [44, 73] | 67 [53, 80] | 82 [71, 93] | <0.001 | 0.01 | 0.006 |
| AUC | 0.63 [0.51, 0.75] | 0.61 [0.47, 0.75] | 0.70 [0.58, 0.81] | 0.84 [0.73, 0.94] | <0.001 | 0.009 | 0.007 |
The values for sensitivity, specificity, PPV, NPV, accuracy, and AUC are presented as [95% CI]. Bonferroni correction was applied for multiple comparisons, and a P value <0.05 was considered as statistically significant. AUC, area under the receiver operator characteristics curve; CI, confidence interval; CT-FFR, coronary computed tomography angiography-derived fractional flow reserve; C-DLR, conventional deep learning reconstruction; FP, false positive; FN, false negative; HIR, hybrid iterative reconstruction; MBIR, model-based iterative reconstruction; NPV, negative predictive value; PPV, positive predictive value; SR-DLR, super-resolution deep learning reconstruction; TN, true negative; TP, true positive.