In order to understand and interpret findings from screening trials, one must be familiar with certain underlying concepts associated with screening. First, are the concepts of incidence and prevalence. In order for screening to lead to significant public health benefits, the target disease should have high incidence and prevalence rates in the screened population. Incidence refers to the number of cancer cases that develop during a defined period
of time, and is expressed as cases per year per 100,000 individuals in the population.7
Prevalence is the number of cancers that exist in a defined population at a given point
in time, and is commonly expressed as cancers per 100,000 individuals in the population.7
Second, an effective screening test should detect disease at an early stage, while the individual is asymptomatic and while cure may be possible with treatment.5
In other words, the test must lead to a decreased mortality rate. The test itself should be safe, inexpensive, and possess sufficient sensitivity (able to identify individuals with
disease), and specificity (able to identify individuals without
disease). To bring about a decrease in the mortality rate, an effective treatment must be available to those diagnosed following a positive screen. Effective screening tests, in conjunction with effective treatment then, have the ability to effect a change in the natural history of the disease in a positive manner.
Third, the benefits of screening must outweigh the risks.7,8
For instance, the incidence of false positives and false negatives must be evaluated against potential benefits. False positives can result in unnecessary surgeries, treatments, anxiety, and public health costs. False negatives, on the other hand, can lead to undetected disease which progresses beyond the benefits of available interventions.
Fourth, survival and mortality are two inter-related but often misunderstood concepts that are important in understanding the relative effectiveness of lung cancer screening techniques. Survival rates reflect the number of individuals alive at a given time relative to their diagnosis. Although frequently reported in observational screening studies, survival rates alone are not an adequate measure of screening benefit. The measure can be misleading because of several confounding biases, lead-time bias, length bias, and over-diagnosis. It is important to note, however, that survival is
appropriately used to compare the benefits of one form of treatment or intervention to another.9
Lead-time refers to the period of time from cancer detection to the time symptoms would have occurred had the individual not been screened ().10
Essentially the survival rate is artificially lengthened with the addition of the lead-time. In effect, earlier detection prolongs survival independent of a delay in death. For an example, if two individuals (one screened, one not screened) die of lung cancer at the same age, the screened individual’s survival time is lengthened because his cancer was detected earlier (lead-time), while he was still asymptomatic. The unscreened individual’s cancer would have gone undiagnosed until symptoms occurred. The screened individual appears to have a longer survival time because of the addition of the lead-time, but the mortality is the same. Such findings run counter to the often held view that cancer is a consistently progressive disease. Research in screening has found that cancer encompasses a wide range of biologic behavior: some cancers progress rapidly to death, some more slowly, and some not at all.11
Length bias refers to the tendency of a screening test to detect indolent, rather than aggressive tumors ().10
Slow growing cancers are more likely to have a prolonged pre-symptomatic period, allowing greater opportunity for detection. This extended period does not necessarily represent an actual improvement in survival, but rather reflects the underlying behavior of the cancer itself. The individual with an indolent disease would in many instances die from other causes first.
FIGURE 1 Lead-time bias. Lung-cancer-specific survival is measured from the time of diagnosis of lung cancer to the time of death. Screening may appear to prolong survival even though death may not be delayed. Effective screening tests should detect disease before (more ...)
FIGURE 2 Length bias. Screening detects more biologically indolent cancers than aggressive cancers due to a prolonged pre-symptomatic phase. This variability in cancer progression may cause it to appear as though there is a screening-related improvement in survival. (more ...)
A related concept is overdiagnosis, which is of particular interest in CT screening trials because advanced technology allows identification of many non-cancerous abnormalities, including some benign lesions ().10
In fact, recent studies indicate that 20% to 60% of helical CT scans of smokers and former smokers will show abnormalities.12–14
Most abnormalities are generally scars from smoking, areas of inflammation, or other non-cancerous conditions. The downstream effects of over-diagnosis cannot be overstated. Individuals may be subjected to the morbidity associated with chemotherapy or surgical interventions for cancers that would otherwise not have become symptomatic or progressive. A relevant example from a different cancer is the Quebec Neuroblastoma Screening Project, conducted from 1989 to 1994. Researchers first identified the rate of death due to neuroblastoma, then compared that rate with the rates in several unscreened control populations born during the same period.15,16
The results of the study indicated that screening infants for neuroblastoma did not appear to reduce mortality compared to the control groups due to overdiagnosis. Similarly, overdiagnosis is of particular concern for helical CT, because it can detect far more abnormalities than chest X-rays.
FIGURE 3 Overdiagnosis. Screening detects cancer that might remain subclinical (non-lethal) before death from other causes. Adapted from US DHHS10.
Finally, mortality is the number of disease-specific deaths relative to the total number of individuals screened.7
The goal of lung cancer screening is to reduce mortality, thus an effect on mortality is vital to the validation of potential screening methods.