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Estimating overdiagnosis in low-dose computed tomography screening for lung cancer: a cohort study
Giulia Veronesi1, Patrick Maisonneuve, Massimo Bellomi
1Division of Thoracic Surgery, European Institute of Oncology, Via Ripamonti 435, 20141 Milan, Italy. giulia.veronesi@ieo.it
Volume-doubling time (VDT) helps distinguish aggressive lung cancer from slow-growing or indolent cases detected during screening. Approximately 25% of incident lung cancers may be overdiagnosed, suggesting a need for less aggressive treatment strategies.
Area of Science:
- Oncology
- Radiology
- Pulmonology
Background:
- Lung cancer screening with low-dose computed tomography (LDCT) can lead to overdiagnosis and overtreatment of asymptomatic cancers.
- Volume-doubling time (VDT) is a potential metric to differentiate aggressive from indolent lung cancers detected during screening.
Purpose of the Study:
- To evaluate the utility of VDT in identifying overdiagnosed lung cancer cases detected through LDCT screening.
- To assess the relationship between VDT and lung cancer aggressiveness and mortality.
Main Methods:
- Retrospective analysis of VDT in a prospective LDCT lung cancer screening cohort.
- VDT was calculated for 175 patients with primary lung cancer and categorized as fast-growing (<400 days), slow-growing (400-599 days), or indolent (≥600 days).
Main Results:
- Of 120 subsequently detected cancers, 15.8% were new and fast-growing, while 84.2% were progressive.
- Lung cancer-specific mortality was significantly higher in patients with new, fast-growing cancers (9.2% per year) compared to slow-growing or indolent cancers (0.9% per year).
- A substantial proportion of incident lung cancers (approximately 25%) were classified as slow-growing or indolent.
Conclusions:
- Slow-growing or indolent lung cancers constitute a significant portion of screen-detected cases and may represent overdiagnosis.
- Minimally invasive treatments should be explored for slow-growing or indolent lung cancers to reduce overtreatment.
- VDT estimation, particularly for new cancers, has limitations due to reliance on single measurements and assumptions.
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