Background Three tools are currently available to predict the risk of contralateral breast cancer (CBC). We aimed to compare the performance of the Manchester formula, CBCrisk, and PredictCBC in patients with invasive breast cancer (BC). Methods We analyzed data of 132,756 patients (4682 CBC) from 20 international studies with a median follow-up of 8.8 years. Prediction performance included discrimination, quantifed as a time-dependent Area-Under-the-Curve (AUC) at 5 and 10 years after diagnosis of primary BC, and calibration, quantifed as the expected-observed (E/O) ratio at 5 and 10 years and the calibration slope. Results The AUC at 10 years was: 0.58 (95% confdence intervals [CI] 0.57–0.59) for CBCrisk; 0.60 (95% CI 0.59–0.61) for the Manchester formula; 0.63 (95% CI 0.59–0.66) and 0.59 (95% CI 0.56–0.62) for PredictCBC-1A (for settings where BRCA1/2 mutation status is available) and PredictCBC-1B (for the general population), respectively. The E/O at 10 years: 0.82 (95% CI 0.51–1.32) for CBCrisk; 1.53 (95% CI 0.63–3.73) for the Manchester formula; 1.28 (95% CI 0.63–2.58) for PredictCBC-1A and 1.35 (95% CI 0.65–2.77) for PredictCBC-1B. The calibration slope was 1.26 (95% CI 1.01–1.50) for CBCrisk; 0.90 (95% CI 0.79–1.02) for PredictCBC-1A; 0.81 (95% CI 0.63–0.99) for PredictCBC-1B, and 0.39 (95% CI 0.34–0.43) for the Manchester formula. Conclusions Current CBC risk prediction tools provide only moderate discrimination and the Manchester formula was poorly calibrated. Better predictors and re-calibration are needed to improve CBC prediction and to identify low- and high-CBC risk patients for clinical decision-making.

Contralateral breast cancer · Risk prediction · Validation · Clinical decision-making,
Breast Cancer Research and Treatment
Department of Medical Microbiology and Infectious Diseases

Giardiello, D., Hauptmann, M, Steyerberg, E.W, Adank, M.A, Akdeniz, D., Blom, JC, & Schmidt, Marjanka K. (2020). Prediction of contralateral breast cancer: external validation of risk calculators in 20 international cohorts. Breast Cancer Research and Treatment, 181(2), 423–434. doi:10.1007/s10549-020-05611-8