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. 2023 Mar 24;58:101899. doi: 10.1016/j.eclinm.2023.101899

Table 2.

Performances of SVM stacking models for predicting pCR to NAC in various molecular subtypes and cohorts.

Molecular subtype Cohort AUC (95% CI) ACC (%) SEN (%) SPE (%) PPV (%) NPV (%)
HR+/HER2− PC 0.959 (0.908–1.00) 97.68 85.71 99.34 94.73 98.05
VC-1 0.904 (0.817–0.991) 93.28 75.00 95.76 70.58 96.58
VC-2 0.908 (0.776–1.00) 94.54 71.42 97.91 83.33 95.91
VC-3 0.882 (0.779–0.985) 82.94 75.00 83.76 32.14 97.02
HER2+ PC 0.974 (0.955–0.993) 91.93 95.83 89.47 85.18 97.14
VC-1 0.896 (0.842–0.949) 85.62 85.10 86.36 89.88 80.28
VC-2 0.929 (0.881–0.978) 86.31 88.13 83.33 89.65 81.08
VC-3 0.920 (0.876–0.965) 87.66 83.54 92.00 91.66 84.14
TNBC PC 0.958 (0.906–1.00) 92.00 91.66 92.30 91.66 92.30
VC-1 0.873 (0.735–1.00) 87.75 90.90 81.25 90.90 81.25
VC-2 0.901 (0.755–1.00) 85.00 85.71 84.61 75.00 91.66
VC-3 0.837 (0.725–0.949) 82.45 68.42 89.47 76.47 85.00

PC, primary cohort; VC, validation cohort; AUC, the area under curve; ACC, accuracy; SEN, sensitivity; SPE, specificity; NPV, negative predictive value; PPV, positive predictive value; 95% CI, 95% confidence interval; HR, hormone receptor; HER2, human epidermal growth factor receptor 2; TNBC, triple negative breast cancer; pCR, pathological complete response.