A scoring system to predict recurrence in breast cancer patients

Current breast cancer recurrence prediction models have limitations for clinical practice (statistical methodology, simplicity and specific populations). We therefore developed a new model that overcomes these limitations. This cohort study comprised 272 patients with breast cancer followed between 2003 and 2016. The main variable was time-to-recurrence (locoregional and/or metastasis) and secondary variables were its risk factors: age, postmenopause, grade, oestrogen receptor, progesterone receptor, c-erbB2 status, stage, multicentricity, diagnosis and treatment. A Cox model to predict recurrence was estimated with the secondary variables, and this was adapted to a points system to predict risk at 5 and 10 years from diagnosis. The model was validated internally by bootstrapping, calculating the C statistic and smooth calibration (splines). The system was integrated into a mobile application for Android. Of the 272 patients with breast cancer, 47 (17.3%) developed recurrence in a mean time of 8.6 ± 3.5 years. The system variables were: age, grade, multicentricity and stage. Validation by bootstrapping showed good discrimination and calibration. A points system has been developed to predict breast cancer recurrence at 5 and 10 years. •Many models have been published to predict breast cancer recurrence.•These mostly have methodological and clinical limitations in their use.•We developed a new points system that accurately predicts the risk of recurrence.•Our model followed the recommended statistical guidelines and is very simple to use.