Publication Abstract

Authors: Albert JM, Liu DD, Shen Y, Pan IW, Shih YC, Hoffman KE, Buchholz TA, Giordano SH, Smith BD

Title: Nomogram to predict the benefit of radiation for older patients with breast cancer treated with conservative surgery.

Journal: J Clin Oncol 30(23):2837-43

Date: 2012 Aug 10

Abstract: PURPOSE: The role of radiation therapy (RT) after conservative surgery (CS) remains controversial for older patients with breast cancer. Guidelines based on recent clinical trials have suggested that RT may be omitted in selected patients with favorable disease. However, it is not known whether this recommendation should extend to other older women. Accordingly, we developed a nomogram to predict the likelihood of long-term breast preservation with and without RT. METHODS: We used Surveillance, Epidemiology, and End Results-Medicare data to identify 16,092 women age 66 to 79 years treated with CS between 1992 and 2002, using claims to identify receipt of RT and subsequent mastectomy. Time to mastectomy was estimated using the Kaplan-Meier method. Cox proportional hazards models determined the effect of covariates on mastectomy-free survival (MFS). A nomogram was developed to predict 5- and 10-year MFS, given associated risk factors, and bootstrap validation was performed. RESULTS: With a median follow-up of 7.2 years, the overall 5- and 10-year MFS rates were 98.1% (95% CI, 97.8% to 98.3%) and 95.4% (95% CI, 94.9% to 95.8%), respectively. In multivariate analysis, age, race, tumor size, estrogen receptor status, and receipt of RT were predictive of time to mastectomy and were incorporated into the nomogram. Nodal status was also included given a significant interaction with RT. The resulting nomogram demonstrated good accuracy in predicting MFS, with a bootstrap-corrected concordance index of 0.66. CONCLUSION: This clinically useful tool predicts 5- and 10-year MFS among older women with early breast cancer using readily available clinicopathologic factors and can aid individualized clinical decision making by estimating predicted benefit from RT.