Abstract
560 Background: Optimization of axillary staging in patients converting from cN+ to ycN0 through PST is needed. The aim of this analysis was to develop a nomogram predicting the probability of ypN+ after PST based on clinical/pathological parameters. Methods: Patients converting from cN+ to ycN0 through PST from a prospective study (SENTINA arm C) were included. Univariate/multivariate analyses were carried out for 14 clinical/pathological parameters to predict ypN+ using logistic regression models. Odds ratios and 95% confidence intervals were reported. Model performance was assessed by leave-one-out cross-validation (LOOCV at .5 cut-offs) and ROC analyses. Calculations were performed using the SAS Software (Version 9.4). Results: 553 patients were assessed. Stepwise backward variable selection based on a multivariate analysis of all significant parameters resulted in a model (5M, Table, N = 369 evaluable) including ER (3.81; 2.25-6.44), multifocality (2.22; 1.26-3.92), LVI (9.16; 4.68-17.90), detection of SLN after PST (.50; .26-.95) and ycT (1.03; 1.01-1.06). In LOOCV, this model had an area under the curve of .81. Multivariate analysis of parameters available preoperatively showed an association between ypN0/ypN+, ER and ycT. Full subset selection resulted in a model (2M, N = 414) containing only ER (4.36; 2.80, 6.81) and ycT (1.04; 1.02, 1.07). Conclusions: A prediction model including parameters evaluable before/after definitive surgery resulted in a nomogram with acceptable accuracy. Limitation to parameters evaluable before surgery (i.e. ER, ycT) showed reduced accuracy that was comparable/superior to accuracy of using individual parameters. Since tumor biology was the strongest parameter in our models, we hypothesize that modern tumor biologic parameters such as gene expression profiling might optimize prediction of axillary status after PST improving patient counseling. [Table: see text]
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