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Screening for Lung Cancer.

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Radiologic clinics of North America 📖 저널 OA 0% 2026 Vol.64(3) p. 453-464 Lung Cancer Diagnosis and Treatment
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PubMed DOI OpenAlex 마지막 보강 2026-04-29
OpenAlex 토픽 · Lung Cancer Diagnosis and Treatment Radiomics and Machine Learning in Medical Imaging COVID-19 diagnosis using AI

Klug M, Szabari MV, Panjwani BC, Fintelmann FJ

📝 환자 설명용 한 줄

Lung cancer screening with low-dose computed tomography (CT) reduces mortality but requires integrated, system-based implementation.

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↓ .bib ↓ .ris
APA Maximiliano Klug, Margit V. Szabari, et al. (2026). Screening for Lung Cancer.. Radiologic clinics of North America, 64(3), 453-464. https://doi.org/10.1016/j.rcl.2026.01.015
MLA Maximiliano Klug, et al.. "Screening for Lung Cancer.." Radiologic clinics of North America, vol. 64, no. 3, 2026, pp. 453-464.
PMID 42020070 ↗

Abstract

Lung cancer screening with low-dose computed tomography (CT) reduces mortality but requires integrated, system-based implementation. Review of evolving screening strategies highlighting risk-prediction models, imaging advances, and Lung CT Screening Reporting and Data System (Lung-RADS) as a standardized framework linking findings to management. Current low-dose chest CT requires comparing with earliest CT to detect change; Lung-RADS categories guide management with increasing complexity. AI enhances nodules detection and risk stratification but requires radiologist oversight; computer-aided detection supports consistency and longitudinal tracking. Implementation demands scalable infrastructure, targeted outreach, and continuous quality improvement to maximize survival benefits and equity.

🏷️ 키워드 / MeSH 📖 같은 키워드 OA만

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