Table 5.

Areas under the ROC curve (linear discriminant analysis classifier, 632+ bootstrap training/testing) in the tasks of predicting pCR and post-NAC LN status, respectively, for the entire dataset and subsets $\mathrm{A}+\mathrm{B}$ combined: AUC values for features that outperformed random guessing ($\mathrm{AUC}=0.5$) before correcting statistical significance for multiple comparisons (regular font) and AUC values for features that remained significantly better than random guessing after correcting for multiple comparisons (18 comparisons, see text for $p$-values) (bold font).

	Pre-NAC LN feature in prediction of pCR		Pre-NAC LN feature in prediction of post-NAC LN-status
Feature	Entire dataset ($N=158$)	Subsets $\mathrm{A}+\mathrm{B}$ ($N=122$)	Entire dataset ($N=158$)	Subsets $\mathrm{A}+\mathrm{B}$ ($N=122$)
${\mathrm{S}}_1$	0.73 [0.58; 0.82]	—	—	—
${\mathrm{S}}_2$	0.71 [0.51; 0.79]	—	—	—
${\mathrm{S}}_3$	—	—	—	—
${\mathrm{G}}_1$	—	—	—	—
${\mathrm{G}}_2$	—	—	—	—
${\mathrm{G}}_3$	0.73 [0.62; 0.80]	0.77 [0.61; 0.85]	—	—
${\mathrm{M}}_2$	—	—	—	—
${\mathrm{M}}_3$	—	—	—	—
${\mathrm{T}}_{11}$	—	—	—	—
${\mathrm{K}}_9$	—	—	—	—
${\mathrm{E}}_4$	—	—	—	—
${\mathrm{B}}_4$	0.73 [0.53; 0.81]	0.78 [0.60; 0.87]	0.68 [0.51; 0.76]	0.69 [0.55; 0.76]
${\mathrm{B}}_6$	0.79 [0.69; 0.85]	0.82 [0.70; 0.88]	0.68 [0.51; 0.77]	—
${\mathrm{B}}_7$	0.80 [0.61; 0.82]	0.74 [0.55; 0.83]	0.71 [0.62; 0.79]	0.72 [0.62; 0.77]
${\mathrm{B}}_8$	—	—	—	—
${\mathrm{B}}_9$	0.69 [0.53; 0.77]	0.71 [0.51; 0.80]	0.70[0.59; 0.78]	0.71 [0.60; 0.77]
${\mathrm{B}}_{11}$	—	0.75 [0.52; 0.84]	0.66 [0.53; 0.75]	0.66 [0.52; 0.74]
${\mathrm{B}}_{12}$	—	—	—	—