Early Lung Cancer Action Project: overall design and findings from baseline screening

来自 Elsevier

阅读量:

86

摘要:

SummaryBackground The Early Lung Cancer Action Project (ELCAP) is designed to evaluate baseline and annual repeat screening by low-radiation-dose computed tomography (low-dose CT) in people at high risk of lung cancer. We report the baseline experience. Methods ELCAP has enrolled 1000 symptom-free volunteers, aged 60 years or older, with at least 10 pack-years of cigarette smoking and no previous cancer, who were medically fit to undergo thoracic surgery. After a structured interview and informed consent, chest radiographs and low-dose CT were done for each participant. The diagnostic investigation of screen-detected non-calcified pulmonary nodules was guided by ELCAP recommendations, which included short-term high-resolution CT follow-up for the smallest non-calcified nodules. Findings Non-calcified nodules were detected in 233 (23% [95% CI 21-26]) participants by low-dose CT at baseline, compared with 68 (7% [5-9]) by chest radiography. Malignant disease was detected in 27 (2·7% [1·8–3·8]) by CT and seven (0·7% [0·3–1·3]) by chest radiography, and stage I malignant disease in 23 (2·3% [1·5–3·3]) and four (0·4% [0·1–0·9]), respectively. Of the 27 CT-detected cancers, 26 were resectable. Biopsies were done on 28 of the 233 participants with non-calcified nodules; 27 had malignant non-calcified nodules and one had a benign nodule. Another three individuals underwent biopsy against the ELCAP recommendations; all had benign non-calcified nodules. No participant had thoracotomy for a benign nodule. Interpretation Low-dose CT can greatly improve the likelihood of detection of small non-calcified nodules, and thus of lung cancer at an earlier and potentially more curable stage. Although false-positive CT results are common, they can be managed with little use of invasive diagnostic procedures.

展开

DOI:

10.1016/S0140-6736(99)06093-6

年份:

1999

通过文献互助平台发起求助,成功后即可免费获取论文全文。

相似文献

参考文献

引证文献

辅助模式

0

引用

文献可以批量引用啦~
欢迎点我试用!

关于我们

百度学术集成海量学术资源,融合人工智能、深度学习、大数据分析等技术,为科研工作者提供全面快捷的学术服务。在这里我们保持学习的态度,不忘初心,砥砺前行。
了解更多>>

友情链接

百度云百度翻译

联系我们

合作与服务

期刊合作 图书馆合作 下载产品手册

©2025 Baidu 百度学术声明 使用百度前必读

引用