Data Sheet 1_A radiomics–deep learning nomogram integrating intratumoral and peritumoral DCE-MRI features for pCR prediction in breast cancer.pdf

The nomogram was evaluated using area under the curve (AUC), calibration, and decision curve analysis (DCA).</p>Results<p>The 6-mm peritumoral margin consistently yielded the numerically highest AUC across all classifiers in the training set. However, in the held-out test set, the AUC difference between Model-TP6 and the intratumoral-only model was not statistically significant (DeLong P = 0.358) and should therefore be interpreted as a numerical trend rather than a statistically confirmed advantage.

The integrated nomogram achieved the numerically highest test-set AUC (0.883, 95% CI: 0.827-0.940). Although most pairwise DeLong comparisons did not reach statistical significance, it showed a favorable combination of discrimination, calibration, and decision-curve net benefit within this internal cohort.</p>Conclusions<p>This nomogram synergizes radiomics with DL representations, offering an exploratory, preliminary pretreatment tool for stratifying NST candidates.

However, in this retrospective single-center study, the model was developed and evaluated using an internal split without independent external validation, and its performance may therefore be optimistic and not generalizable to other institutions or imaging settings. Prospective multicenter external validation is essential before any clinical application.</p>