OBJECTIVES: HER-France is a French national web-based database focused on HER2 status in breast cancer and provided by 125 Pathology Laboratories (PL) since October 2011. It was developed for a local and national monitoring of HER2 but contains also additional data like the SBR grade, ER and PR status, and Ki67 index. The objective of this study is to allow PL to compare their actual HER2 rate, not only with the national data (obtained by aggregation of data of all participating labs) but also to their own expected HER2 rate estimated through the additional collected data. METHODS: To guarantee best pre-analytical conditions, only results on core-biopsies were taken into account. For the prediction model 30,777 sample results from 2014 were used. Different models were built such as penalized regression and random forest to predict HER2 positivity. To evaluate the performance of the models and choose the most suitable, the database was divided in two subsets: the training (70% of the database) and the test one (30%). The models were built on the training data and evaluate on the test dataset. An accuracy measure (AUC) was used to compare models, with AUC=1 indicating a perfect fitting, whereas AUC under 0.5 indicating a random guess. RESULTS: HER2 positivity rate was 12%. The most accurate model found was the penalised regression as the prediction accuracy (AUC=0.78) proved good ability for HER2 modeling. The significant factors associated with HER2 positivity were the higher SBR grade and Ki67, lower RO and RP status and ductal histological subtype. This study added Ki67 as a strong factor of HER2 positivity nearby histological subtype, grade and hormonal status as previously mentioned by Ruschoff et al. CONCLUSIONS: In the future, these results should be used to develop a tool integrated within Her-France functionalities in order to allow pathologists to better monitor their practices.