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Clinical Application of Expert Software Based on Six Tumour Biomarkers to Stratify the Risk of Lung Cancer in a Pulmonary Rapid Diagnosis Unit.

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Archivos de bronconeumologia 2026 Vol.62(4) p. 226-235
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출처

Molina R, Trapé J, Garrido A, Salas E, Domínguez Gutiérrez de Ceballos RM, Gundín S, León Justel A, García de Burgos M, Piedad Rivas J, Julián Ansón MÁ, Matos Garrido M, Lorenzo García S, Marrades R, Múgica Atorrasagasti N, Luque Crespo E, López-Ramírez C, Gutiérrez Herrero FG, González-Fernández C, Villalta Robles V, González Cocaño MC, Ramos Álvarez M, Carvalho Marta J, Jiménez García C, Cabezón Vicente R, Pavón Masa M, de la Maleta Úbeda RS, Bernadich O, Estrada Trigueros G, Orejas A, Santana Astudillo JC, Trapé-Úbeda J, Barco-Sánchez A

ℹ️ 이 논문은 무료 전문이 아직 없습니다. 코퍼스 전체의 43.8%는 무료 가능 (통계 →) · 🏥 기관 EZproxy로 시도

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[OBJECTIVES] Diagnostic tools that stratify lung cancer (LC) risk can help prioritize care for patients at the highest risk and optimize time and procedures to achieve the final diagnosis.

🔬 핵심 임상 통계 (초록에서 자동 추출 — 원문 검증 권장)
  • Sensitivity 75.5%
  • Specificity 89.0%

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APA Molina R, Trapé J, et al. (2026). Clinical Application of Expert Software Based on Six Tumour Biomarkers to Stratify the Risk of Lung Cancer in a Pulmonary Rapid Diagnosis Unit.. Archivos de bronconeumologia, 62(4), 226-235. https://doi.org/10.1016/j.arbres.2025.12.007
MLA Molina R, et al.. "Clinical Application of Expert Software Based on Six Tumour Biomarkers to Stratify the Risk of Lung Cancer in a Pulmonary Rapid Diagnosis Unit.." Archivos de bronconeumologia, vol. 62, no. 4, 2026, pp. 226-235.
PMID 41605770 ↗

Abstract

[OBJECTIVES] Diagnostic tools that stratify lung cancer (LC) risk can help prioritize care for patients at the highest risk and optimize time and procedures to achieve the final diagnosis. We have previously demonstrated that six tumour biomarkers (TBs) - CEA, CYFRA 21.1, CA 15-3, SCC Ag, ProGRP, and NSE - can help assess LC risk. We developed expert software that combines these TBs with clinical and imaging data to estimate LC risk.

[METHODS] The diagnostic accuracy of this expert software was evaluated in a multicentre study. We prospectively recruited 2005 individuals referred to 12 reference hospitals in Spain and Portugal for suspicion of LC. The six TBs were determined and the expert software was applied to all patients and correlated with the final diagnosis.

[RESULTS] A final diagnosis of LC was made in 1392 patients. The expert software yielded 87.7% sensitivity, 75.5% specificity, 89.0% positive predictive value and 73.0% negative predictive value. Sensitivity increased with tumour size and extension. The software also provides histological information, correctly predicting cancer in 98.4% of small-cell LC and 93.2% of non-small-cell LC, which correlates with the histological diagnosis of 90% and 91.2%, respectively.

[CONCLUSIONS] The expert software developed provides excellent diagnostic accuracy for diagnosing LC. Accordingly, this software can help stratify the risk of LC and prioritize the evaluation of patients at higher risk, optimizing procedures based on risk and knowledge of the most likely histological type, and providing a valuable tool for risk stratification and clinical decision support, particularly in Rapid Diagnostic Units.

🏷️ 키워드 / MeSH 📖 같은 키워드 OA만

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🏷️ 같은 키워드 · 무료전문 — 이 논문 MeSH/keyword 기반