Local relapse after breast-conserving therapy for ductal carcinoma in situ: a European single-center experience and external validation of the Memorial Sloan-Kettering Cancer Center DCIS nomogram.
Academic Article
Overview
abstract
PURPOSE: Adjuvant treatments after breast-conserving surgery (BCS) for ductal carcinoma in situ to prevent local relapse are considered standard of care. However, patient selection to prevent increased morbidity without proven survival benefit remains a challenge. To predict the risk of ipsilateral breast tumor relapse (IBTR) after BCS, the Memorial Sloan-Kettering Cancer Center (MSKCC) developed a nomogram. The aim of this study was to develop our own prediction model for IBTR and to provide an external validation of the MSKCC nomogram. METHODS: From 1973 to 2010, 467 patients were treated with BCS for ductal carcinoma in situ at the University Hospital Leuven. Clinicopathologic and treatment parameters of all patients were used to create a multivariable model. The predictive value of the model was evaluated using the concordance index (C-index) and concordance probability estimate (CPE). Multiple imputation was used to account for missing data to allow the MSKCC model to be tested on 467 patients. RESULTS: Median follow-up was 7.2 years, with 48 women who developed an IBTR. Omission of adjuvant endocrine therapy, younger age, and positive or close surgical margins were significantly associated with an increased risk of IBTR. The bootstrap-corrected C-index for 10-year prediction by our own model was 0.63 and the CPE was 0.61. The C-index and CPE for the 10-year relapse probabilities predicted by the MSKCC nomogram were 0.66 and 0.61, respectively. CONCLUSIONS: Despite the small number of events, the need for multiple imputation, and few patients without radiation, the MSKCC nomogram performance was somewhat better than our model. This shows that the MSKCC nomogram is externally valid. The MSKCC nomogram allows users to integrate the information from 10 different variables to provide a more precise risk stratification than the use of conventional single variables or hazard ratios.
