ArticleESMO real world data and digital oncology2025
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.
Article in ESMO real world data and digital oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05217381 (Retrospective Observational Study With Real-World Data to Assess Demographic and Clinicopathological Profiles, and Management of Breast Cancer Patients With Positive, Low or Negative Expression of HER2 in Spain), which is not on this map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Retrospective Observational Study With Real-World Data to Assess Demographic and Clinicopathological Profiles, and Management of Breast Cancer Patients With Positive, Low or Negative Expression of HER2 in Spain
Who cites it
2 citing papers in PubMed.
- AI-Enabled Real-World Evidence in Oncology: A Statistical Perspective for Regulatory Decisions.Therapeutic innovation & regulatory science · 2026Review
- ERBB2/HER2 landscape and prognostic impact: large-scale, real-world analysis across solid cancers.ESMO open · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
19 authors.
Funding
No grant is acknowledged in the PubMed record.
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: RosHER is a retrospective, observational, longitudinal, population-based, multicenter study (NCT05217381). An NLP tool extracted HER2 information from EHRs of adult patients with early, locally advanced, or Results: Between January 2022 and November 2023, 18 533 patients were selected from seven Spanish sites. A cut-off of ≥6 months was established between initial determination and relapse or progression. The artificial intelligence (AI)-based tool identified 510 patients with two documented HER2 determinations and 209 with HER2 expression by immunohistochemistry/ 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.