ArticleESMO real world data and digital oncology2026
Applying clinical natural language processing to lung cancer in Spain: a terminology-based panel approach.
Article in ESMO real world data and digital oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- From electronic health records to real-world evidence: Savana Next-Generation Registry digital solution.ESMO real world data and digital oncology · 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
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: We present a novel methodological framework for developing and validating a terminology-based Disease Panel to identify and characterize patients with metastatic non-small-cell lung cancer (mNSCLC) in a Spanish cohort by applying clinical natural language processing (cNLP) to electronic health records (EHRs). Materials and methods: The mNSCLC Disease Panel was built from standardized vocabularies (Systematized Nomenclature of Medicine-Clinical Terms and Anatomical Therapeutic Chemical) enriched with curated alternative expressions such as synonyms, acronyms, and abbreviations. Terms were organized by clinical relevance and refined through clinical expert annotation. Using EHRead® (Medsavana S.L., Madrid, Spain), a cNLP pipeline, clinical concepts from the Disease Panel were extracted from Spanish EHRs. Performance was validated by clinical experts through the assessment of precision, recall, and F1 scores. Results: The Disease Panel comprised 268 terms (mean of alternative expressions 8.4, range 1-41), including 125 (46.6%) demographic and clinical characteristics, 115 (42.9%) treatments, and 28 (10.5%) outcomes. The mean ± standard deviation (SD) precision across all terms was 0.97 ± 0.04, with values of 0.98 ± 0.04 for characteristics, 0.97 ± 0.04 for treatments, and 0.96 ± 0.06 for outcomes. For key terms, the mean ± SD precision, recall, and F1 score were 0.94 ± 0.05, 0.91 ± 0.08, and 0.92 ± 0.05, respectively. Conclusions: This is the first validated terminology-based Disease Panel specifically designed for mNSCLC and integrated into a cNLP pipeline. It reliably identifies key clinical features with excellent extraction performance, supporting scalable real-world evidence generation. This approach offers a robust alternative to manual chart review or 'International Classification of Diseases' data/claims data in oncology research.
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.