Including the Ductal Carcinoma-In-Situ (DCIS) Score in the Development of a Multivariable Prediction Model for Recurrence After Excision of DCIS.

INTRODUCTION: Individual prediction of local recurrence (LR) risk after breast-conserving surgery (BCS) for ductal carcinoma-in-situ (DCIS) is needed to identify women at low risk, for whom radiotherapy may be omitted. PATIENTS AND METHODS: Three predictive models of LR-clinicopathologic factors (CPF) alone; CPF + estrogen receptor (ER) + human epidermal growth factor receptor 2 (HER2); and CPF + DCIS score (DS)-were developed among 1102 cases of DCIS in patients with complete covariate and outcome data. Categorizations of discrete variables and transformations of continuous variables were examined in Cox models; 2-way interactions and interactions with time were assessed. Internal validation was performed by bootstrapping. Individual predicted 10-year LR risks were computed from covariate values, estimated regression parameters, and estimated baseline survival function. Accuracy was assessed by c statistics and calibration plots.

RESULTS: The strongest prediction model incorporated CPF + DS. The c statistics for CPF + DS, CPF + ER + HER2, or CPF-alone models were 0.7025, 0.6879, and 0.6825, respectively. The CPF + DS model was better calibrated at predicting low (≤ 10%) individual 10-year LR risks after BCS alone than those incorporating CPF + ER + HER2 or CPF alone, evidenced by c statistics and plots of observed by predicted risks. Among women aged ≥ 50 with no adverse CPF, the CPF + DS model identified the greatest proportion of women (62.3%) with predicted individual 10-year LR ≤ 10% without radiotherapy compared to the CPF + ER + HER2 (50.9%) or CPF alone (46.5%) models.

CONCLUSION: Individual prediction of LR incorporating DS is more accurate and identifies a higher proportion of women with low predicted risk of LR after BCS alone, for whom radiotherapy may be omitted.