ESMO real world data and digital oncology · 2026
Applying clinical natural language processing to lung cancer in Spain: a terminology-based panel approach.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Ospina-Serrano A, Azkárate A, Ramírez-Peinado S, Rashid A, Vollan H-K, Jones S, Menke S, Martín R, González F, Posso M, Taberna M
Аннотация
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.
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.