Table 4 Performance of SE-CrossT models based on WSIs, US and multimodal according to validation and test cohorts

From: Cross-modal deep learning model for predicting pathologic complete response to neoadjuvant chemotherapy in breast cancer

SE-CrossT

ACC

AUC

SENS

SPEC

PPV

NPV

Validation cohort (n = 49)

 WSIs

0.857 (0.783–0.890)

0.810 (0.777–0.819)

0.730 (0.678–0.750)

0.788 (0.726–0.818)

0.751 (0.680–0.779)

0.751 (0.712–0.781)

 US

0.878 (0.811–0.880)

0.837 (0.806–0.855)

0.883 (0.819–0.901)

0.882 (0.815–0.890)

0.859 (0.803–0.887)

0.879 (0.816–0.884)

 Multimodal

0.918 (0.838–0.924)

0.851 (0.793–0.869)

0.896 (0.833–0.918)

0.888 (0.861–0.895)

0.866 (0.819–0.891)

0.885 (0.830–0.896)

Test cohort 1 (n = 51)

 WSIs

0.776 (0.746–0.792)

0.703 (0.649–0.745)

0.727 (0.647–0.763)

0.738 (0.689–0.759)

0.724 (0.680–0.725)

0.729 (0.680–0.737)

 US

0.823 (0.751–0.829)

0.817 (0.775–0.836)

0.859 (0.805–0.869)

0.867 (0.821–0.882)

0.833 (0.783–0.870)

0.855 (0.810–0.865)

 Multimodal

0.882 (0.854–0.920)

0.851 (0.791–0.856)

0.878 (0.828–0.893)

0.855 (0.826–0.868)

0.840 (0.775–0.864)

0.864 (0.789–0.884)

  1. Data in parentheses are 95% confidence intervals.
  2. ACC accuracy, AUC area under the receiver, SENS sensitivity, SPEC specificity, PPV positive predictive value, NPV negative predictive value.