Overdiagnosis of lung cancer with low-dose computed tomography screening: meta-analysis of the randomised clinical trials

Abstract

In low-dose computed tomography (LDCT) screening for lung cancer, all three main conditions for overdiagnosis in cancer screening are present: 1) a reservoir of slowly or nongrowing lung cancer exists; 2) LDCT is a high-resolution imaging technology with the potential to identify this reservoir; and 3) eligible screening participants have a high risk of dying from causes other than lung cancer. The degree of overdiagnosis in cancer screening is most validly estimated in high-quality randomised controlled trials (RCTs), with enough follow-up time after the end of screening to avoid lead-time bias and without contamination of the control group. Nine RCTs investigating LDCT screening were identified. Two RCTs were excluded because lung cancer incidence after the end of screening was not published. Two other RCTs using active comparators were also excluded. Therefore, five RCTs were included: two trials were at low risk of bias, two of some concern and one at high risk of bias. In a meta-analysis of the two low risk of bias RCTs including 8156 healthy current or former smokers, 49% of the screen-detected cancers were overdiagnosed. There is uncertainty about this substantial degree of overdiagnosis due to unexplained heterogeneity and low precision of the summed estimate across the two trials.

Key points: Nine randomised controlled trials (RCTs) on low-dose computed tomography screening were identified; five were included for meta-analysis but only two of those were at low risk of bias.In a meta-analysis of recent low risk of bias RCTs including 8156 healthy current or former smokers from developed countries, we found that 49% of the screen-detected cancers may be overdiagnosed.There is uncertainty about the degree of overdiagnosis in lung cancer screening due to unexplained heterogeneity and low precision of the point estimate.If only high-quality RCTs are included in the meta-analysis, the degree of overdiagnosis is substantial.

Educational aims: To appreciate that low-dose computed tomography screening for lung cancer meets all three main conditions for overdiagnosis in cancer screening: a reservoir of indolent cancers exists in the population; the screening test is able to "tap" this reservoir by detecting biologically indolent cancers as well as biologically important cancers; and the population being screened is characterised by a relatively high competing risk of death from other causesTo learn about biases that might affect the estimates of overdiagnosis in randomised controlled trials in cancer screening.

Figure 1

Forest plot of the RR of the cumulative incidence of lung cancer (estimates >1 represent overdiagnosis). The meta-analysis includes all trials, regardless of bias assessment. Trials are listed alphabetically. M-H: Mantel-Haenszel; df: degrees of freedom.

Figure 2

Forest plot of estimates of overdiagnosis defined as the fraction of screen-detected lung cancers that represent overdiagnosis. Meta-analysis includes all trials, regardless of bias assessment. Trials are listed alphabetically. IV: inverse variance; df: degrees of freedom.

Figure 3

Forest plot of the RR of the cumulative incidence of lung cancer (estimates >1 represent overdiagnosis). The meta-analysis only includes low risk of bias trials. Trials are listed alphabetically. M-H: Mantel-Haenszel; df: degrees of freedom.

Figure 4

Forest plot of estimates of overdiagnosis defined as the fraction of screen-detected lung cancers that represent overdiagnosis. The meta-analysis only includes low risk of bias trials. Trials are listed alphabetically. IV: inverse variance; df: degrees of freedom.