Abstract
Introduction: The natural history of consolidation on computed tomography (CT) rarely includes invasive cancers, and evidence of the ideal timing for surgical intervention via long-term follow-up studies remains unknown. Methods: Between January 2012 and June 2017, pulmonary resection was undertaken in 293 clinical IA patients who were followed-up for > 6 months after the first detection of potential non-small-cell lung cancer (NSCLC) opacities. We evaluated the corresponding HRs and compared the recurrence risk with the CT follow-up duration. Results: HRs calculated for the longest intervals were compared between two patient subsets: a shorter-interval surgery group (SISG: 41.3%; mean follow-up interval, 13.5±5.3 months) and a longer-interval surgery group (58.7%; mean follow-up interval, 54.9±25.6 months). On Cox multivariate regression analyses, CT consolidation (ratio >0.5), an abnormal carcinoembryonic antigen and a triple-negative mutation showed an independent association with an unfavorable prognosis, as measured by disease-free survival after the first detection of potential NSCLC opacities. The longer-interval surgery group fared significantly better than the SISG in terms of 5-year overall survival after the first detection (99.3% vs 93.1%, P<0.01); the 3-year overall survival after the first detection was significantly shorter in the high-risk SISG (presence of two factors from the three) than that in the low-risk SISG (presence of 0 or one factor; 100% vs 73.3%, P<0.01). Conclusion: Our study indicates that the patients with potential NSCLC opacities who are able to wait for more than 2 years prior to pulmonary resection may be likely to have a favorable prognosis, whereas early judgment for surgical resection should be required for avoiding surgical delays.
| Original language | English |
|---|---|
| Pages (from-to) | 107-115 |
| Number of pages | 9 |
| Journal | Cancer Management and Research |
| Volume | 11 |
| DOIs | |
| Publication status | Published - 2019 |
| Externally published | Yes |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
All Science Journal Classification (ASJC) codes
- Oncology
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