• Jiaci Tong, Dewei Tan, Jing Ma, Ye Hu, and Man Li.
• Department of Oncology & Department of Breast Surgery, The Second Hospital of Dalian Medical University, Dalian, China.
• Medicine (Baltimore). 2021 Nov 19; 100 (46): e27595e27595.

AbstractThe main purpose of this study was to build a prediction model for patients with contralateral breast cancer (CBC) using competing risks methodology. The aim is to help clinicians predict the probability of CBC in breast cancer (BC) survivors.We reviewed data from the Surveillance, Epidemiology, and End Results database of 434,065 patients with BC. Eligible patients were used to quantify the association between the development of CBC and multiple characteristics of BC patients using competing risk models. A nomogram was also created to facilitate clinical visualization and analysis. Finally, the stability of the model was verified using concordance index and calibration plots, and decision curve analysis was used to evaluate the clinical utility of the model by calculating the net benefit.Four hundred thirty-four thousand sixty-five patients were identified, of whom 6944 (1.6%) developed CBC in the 10 years follow-up. The 10-year cumulative risk of developing CBC was 2.69%. According to a multivariate competing risk model, older patients with invasive lobular carcinoma who had undergone unilateral BC surgery, and whose tumor was better differentiated, of smaller size and ER-negative/PR-positive, had a higher risk of CBC. The calibration plots illustrated an acceptable correlation between the prediction by nomogram and actual observation, as the calibration curve was closed to the 45° diagonal line. The concordance index for the nomogram was 0.65, which indicated it was well calibrated for individual risk of CBC. Decision curve analysis produced a wide range of risk thresholds under which the model we built would yield a net benefit.BC survivors remain at high risk of developing CBC. Patients with CBC have a worse clinical prognosis compared to those with unilateral BC. We built a predictive model for the risk of developing CBC based on a large data cohort to help clinicians identify patients at high risk, which can then help them plan individualized surveillance and treatment.Copyright © 2021 the Author(s). Published by Wolters Kluwer Health, Inc.

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