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The LEAP Study: A Multicenter Biospecimen and Imaging Resource for Lung Cancer Screening.

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Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology 📖 저널 OA 44% 2022: 1/3 OA 2023: 0/1 OA 2024: 6/8 OA 2025: 25/40 OA 2026: 26/75 OA 2022~2026 2026 Vol.35(1) p. 39-48
Retraction 확인
출처

PICO 자동 추출 (휴리스틱, conf 3/4)

유사 논문
P · Population 대상 환자/모집단
841 participants were enrolled who underwent LDCT: 2,841 at baseline, 2,097 at year 1 (74%), and 1,779 at year 2 (63%).
I · Intervention 중재 / 시술
three annual rounds of LDCT-LCS, with blood specimens collected at each time point
C · Comparison 대조 / 비교
추출되지 않음
O · Outcome 결과 / 결론
[CONCLUSIONS] The LEAP cohort provides a resource with a longitudinal database of participant data, LDCT imaging, and matched blood specimens for biomarker validation, aiming to address unmet clinical needs in LCS. [IMPACT] LEAP provides longitudinal biospecimens linked with clinical follow-up and LDCT imaging for biomarker validation in the context of imaging findings and lung cancer diagnosis.

Dennison JB, Gendarme S, Kettner NM, Godoy MCB, Philley JV, Antonoff MB

📝 환자 설명용 한 줄

[BACKGROUND] Blood-based biomarkers could improve the effectiveness of lung cancer screening (LCS) with low-dose CT (LDCT) through more accurate lung cancer risk stratification and nodule malignancy r

🔬 핵심 임상 통계 (초록에서 자동 추출 — 원문 검증 권장)
  • 연구 설계 cohort study

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↓ .bib ↓ .ris
APA Dennison JB, Gendarme S, et al. (2026). The LEAP Study: A Multicenter Biospecimen and Imaging Resource for Lung Cancer Screening.. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology, 35(1), 39-48. https://doi.org/10.1158/1055-9965.EPI-25-1022
MLA Dennison JB, et al.. "The LEAP Study: A Multicenter Biospecimen and Imaging Resource for Lung Cancer Screening.." Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology, vol. 35, no. 1, 2026, pp. 39-48.
PMID 41191414 ↗

Abstract

[BACKGROUND] Blood-based biomarkers could improve the effectiveness of lung cancer screening (LCS) with low-dose CT (LDCT) through more accurate lung cancer risk stratification and nodule malignancy risk assessment. The Lung Cancer, Early Detection, Assessment of Risk, and Prevention (LEAP) study aims to establish a reference set to validate promising cancer biomarkers in the context of LCS.

[METHODS] This prospective international cohort study (United States, France, and Spain) enrolled individuals with elevated risk of developing lung cancer based on National Comprehensive Cancer Network Guidelines into an LCS program. Participants underwent three annual rounds of LDCT-LCS, with blood specimens collected at each time point. Questionnaires and medical chart reviews were utilized to determine participants' cancers status.

[RESULTS] Between 2014 and 2019, 2,841 participants were enrolled who underwent LDCT: 2,841 at baseline, 2,097 at year 1 (74%), and 1,779 at year 2 (63%). Rates at baseline, year 1, and year 2 were positive scans (13%, 7.5%, and 7.5%, respectively), false positives (12%, 6.8%, and 6.9%, respectively), and screen-detected lung cancer (0.9%, 0.9%, and 0.7%, respectively). After 5 years of follow-up, 74 lung cancer cases were detected: 55% stage I, 11% stage II, 16% stage III, and 12% stage IV. The LEAP biobank collected 6,586 blood specimens, including 126 (lung) and 201 (other) prediagnostic samples within 5 years of diagnosis.

[CONCLUSIONS] The LEAP cohort provides a resource with a longitudinal database of participant data, LDCT imaging, and matched blood specimens for biomarker validation, aiming to address unmet clinical needs in LCS.

[IMPACT] LEAP provides longitudinal biospecimens linked with clinical follow-up and LDCT imaging for biomarker validation in the context of imaging findings and lung cancer diagnosis.

🏷️ 키워드 / MeSH 📖 같은 키워드 OA만

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