Table 5.
The performance of the multimodal ACNN model for predicting triple negative from non-triple negative breast cancers in two subgroups with different T stages.
| Datasets | Subgroups | AUC | Sensitivity (%) | Specificity (%) | Accuracy (%) | F1-score |
|---|---|---|---|---|---|---|
| Internal validation cohort | T1 stage (n=39) | 0.957 (0.891-1.000) | 100 (45.41-100) | 88.57 (73.45-96.06) | 89.74 (75.85-96.51) | 0.89 |
| non-T1 stage (n=46) | 0.985 (0.958-1.000) | 100 (59.56-100) | 89.74 (75.85-96.51) | 91.30 (79.14-97.10) | 0.91 | |
| Test cohort A | T1 stage (n=44) | 0.958 (0.897-1.000) | 100 (59.56-100) | 94.59 (81.37-99.43) | 95.45 (84.04-99.58) | 0.95 |
| non-T1 stage (n=49) | 0.961 (0.903-1.000) | 100 (55.72-100) | 86.05 (72.36-93.82) | 87.76 (75.39-94.64) | 0.87 | |
| Test cohort B | T1 stage (n=39) | 0.957 (0.889-1.000) | 100 (45.41-100) | 82.86 (66.94-92.28) | 84.62 (69.89-93.14) | 0.85 |
| non-T1 stage (n=56) | 0.932 (0.868-0.996) | 93.33 (68.16-100) | 85.37 (71.17-93.50) | 87.50 (76.07-94.12) | 0.84 |
Note. —95% confidence intervals are included in brackets.
ACNN, assembled convolutional neural network; AUC, area under the receiver operating characteristic curve.