Human epidermal growth factor receptor 2 (HER2) expression dynamics between diagnosis and recurrence in patients with breast cancer using artificial intelligence and electronic health records: the RosHER study

Abstract

Background: Human epidermal growth factor receptor 2 (HER2) is a treatment target in breast cancer (BC), driving therapeutic strategies. Changes over time in HER2 expression have been described and understanding of these fluctuations is crucial for personalized medicine. We aimed to assess HER2 expression dynamics using real-world data and natural language processing (NLP) from electronic health records (EHRs). Material and methods: metastatic BC, who were initially diagnosed between 2005 and 2021. The primary endpoint was to evaluate HER2 dynamics in HER2 status and expression between initial diagnosis and recurrence or progression using NLP. The secondary endpoints were description of baseline clinicopathological characteristics and treatment patterns. Results: hybridization. Overall discordances were 10.6% in HER2 status and 34.0% in HER2 expression. HER2-zero expression switched to HER2-low (23.2%), but not HER2-positive (0%); HER2-low expression converted to HER2-zero (32.0%) and HER2-positive (7.0%); finally HER2-positive expression switched to HER2-low (20.8%) and HER2-zero (15.1%). Conclusions: This is the first study using NLP to evaluate HER2 discordances, which need to be further investigated. Improving AI methods and implementing similar EHR structures among hospitals would increase the success in clinical data extraction.

Ähnliche Arbeiten