Cancer Screening Overview (PDQ®)–Health Professional Version

The purpose of this summary is to present an evidence-based approach used in the development of the screening summaries. Evaluating screening requires weighing risks and benefits. Monetary cost and cost-effectiveness are not considered when evaluating the evidence. Levels of evidence are assigned when evaluating screening tests.

In general, the benefit of cancer screening derives from detecting cancer in earlier and more treatable stages, and thereby, reducing mortality from cancer. In addition, for some cancer types and screening modalities, such as endoscopic screening for colorectal cancer and Papanicolaou (Pap) smears for cervical cancer, screening can also prevent the occurrence of cancer by identifying and removing cancer precursors. Screening may also reduce cancer morbidity when the treatment for earlier-stage cancer is associated with fewer side effects than the treatment for advanced cancers.

Known harms from screening include the following:[1]

Because screening tests themselves are generally noninvasive, immediate harms from the test are typically minor. Colonoscopy is an exception in that it is an invasive test. It also functions as a diagnostic follow-up examination for other colorectal cancer screening modalities, such as a fecal occult blood (FOB) test.

Commonly used screening tests, such as mammography for breast cancer and prostate-specific antigen (PSA) for prostate cancer, have false-positive rates per screen in the range of 5% to 10%; with repeat screening, cumulative false-positive rates for these tests are substantially higher.[2-4] Follow-up invasive diagnostic procedures, such as a prostate biopsy, are associated with low but non-negligible risks of complications. For screening tests such as colonoscopy or Pap smears where precursor lesions, in addition to invasive cancer, are targets of the screen, the definition of a false positive is modified from simply a positive screen in an individual without cancer. For example, for cervical cancer screening, a positive Pap smear with an eventual diagnosis of high-grade intraepithelial lesion (HSIL) would not be considered a false positive, since HSIL is a target lesion.

Overdiagnosis occurs when screening procedures detect cancers that would never become clinically apparent in the absence of screening. It is a special concern because identification of the cancer does not benefit the individual, while the side effects of diagnostic procedures and cancer treatment may cause significant harm. The overall harm of overdiagnosis is related to both the frequency of its occurrence, as well as to the downstream consequences of subsequent treatment. For example, in prostate cancer screening with PSA, there is a high rate of overdiagnosed disease and the harms of curative treatment, including impotence and urinary incontinence, are relatively common, serious, and long-lasting. Therefore, overdiagnosis is a major source of harms in PSA screening.[5] Some of the harms of overdiagnosis may be mitigated by strategies, such as active surveillance in prostate cancer, that attempt to defer immediate treatment in favor of following patients for any signs of worsening prognosis.

In general, overdiagnosis is more common in older individuals and those with otherwise limited life expectancy since the slowly growing lesions associated with overdiagnosed cancers have less time to become clinically apparent in such persons. Therefore, attempts have been made to discourage screening beyond certain age ranges; for example, most recommendations for mammography screening exclude women aged 75 or older and those with less than 10 years of life expectancy.[6]

In developing the cancer screening summaries, the PDQ Screening and Prevention Editorial Board uses the following definitions:

The PDQ does not issue clinical practice guidelines. Many public health organizations present guidelines for health care and screening activities. Their quality varies widely. Some rely on systematic reviews of evidence of variable quality. Some are influenced by the professional, financial, and intellectual interests of guideline authors and funders. These may conflict with the primary interest—the overall well-being of the patient.[7-9]

The highest quality guidelines as assessed by the Appraisal of Guidelines for Research and Evaluation (AGREE) are those based on the best systematic reviews as evaluated using the Assessment of Multiple Systematic Reviews (AMSTAR).

References

1. Kramer BS: The science of early detection. Urol Oncol 22 (4): 344-7, 2004 Jul-Aug. [PUBMED Abstract]
2. Grubb RL, Pinsky PF, Greenlee RT, et al.: Prostate cancer screening in the Prostate, Lung, Colorectal and Ovarian cancer screening trial: update on findings from the initial four rounds of screening in a randomized trial. BJU Int 102 (11): 1524-30, 2008. [PUBMED Abstract]
3. Hubbard RA, Kerlikowske K, Flowers CI, et al.: Cumulative probability of false-positive recall or biopsy recommendation after 10 years of screening mammography: a cohort study. Ann Intern Med 155 (8): 481-92, 2011. [PUBMED Abstract]
4. Aberle DR, Adams AM, Berg CD, et al.: Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med 365 (5): 395-409, 2011. [PUBMED Abstract]
5. Fenton JJ, Weyrich MS, Durbin S, et al.: Prostate-Specific Antigen-Based Screening for Prostate Cancer: Evidence Report and Systematic Review for the US Preventive Services Task Force. JAMA 319 (18): 1914-1931, 2018. [PUBMED Abstract]
6. Siu AL; U.S. Preventive Services Task Force: Screening for Breast Cancer: U.S. Preventive Services Task Force Recommendation Statement. Ann Intern Med 164 (4): 279-96, 2016. [PUBMED Abstract]
7. Norris SL, Burda BU, Holmer HK, et al.: Author's specialty and conflicts of interest contribute to conflicting guidelines for screening mammography. J Clin Epidemiol 65 (7): 725-33, 2012. [PUBMED Abstract]
8. Ransohoff DF, Pignone M, Sox HC: How to decide whether a clinical practice guideline is trustworthy. JAMA 309 (2): 139-40, 2013. [PUBMED Abstract]
9. Lenzer J: Why we can't trust clinical guidelines. BMJ 346: f3830, 2013. [PUBMED Abstract]

The cancer screening summaries are based on various levels of published scientific evidence and collective clinical experience. The highest level of evidence is taken as mortality reduction in controlled, randomized clinical trials. The results of clinical studies, case-control studies, cohort studies, and other information are also considered in formulating the summaries. In addition, the incidence of cancer, stage distribution, treatment, and mortality rates are considered. The summaries are subject to modification as new evidence becomes available.

There are at least two requirements for screening to be efficacious:

These requirements are necessary but not sufficient to prove the efficacy of screening, which requires a decrease in cause-specific mortality. A cautionary tale is a Japanese screening program for childhood neuroblastoma that detected malignancy in ten times as many infants as in unscreened populations. But, a randomized trial showed that it did not yield any improvement in cause-specific mortality.[1] Other neuroblastoma screening projects confirmed the result.[2-4]

References

1. Yamamoto K, Hayashi Y, Hanada R, et al.: Mass screening and age-specific incidence of neuroblastoma in Saitama Prefecture, Japan. J Clin Oncol 13 (8): 2033-8, 1995. [PUBMED Abstract]
2. Bessho F: Effects of mass screening on age-specific incidence of neuroblastoma. Int J Cancer 67 (4): 520-2, 1996. [PUBMED Abstract]
3. Woods WG, Tuchman M, Robison LL, et al.: A population-based study of the usefulness of screening for neuroblastoma. Lancet 348 (9043): 1682-7, 1996 Dec 21-28. [PUBMED Abstract]
4. Soderstrom L, Woods WG, Bernstein M, et al.: Health and economic benefits of well-designed evaluations: some lessons from evaluating neuroblastoma screening. J Natl Cancer Inst 97 (15): 1118-24, 2005. [PUBMED Abstract]

Direct or assisted visual observation is the most widely available examination for the detection of cancer. It is useful in identifying suspicious lesions in the skin, retina, lip, mouth, external genitalia, and cervix.

The second most available detection procedure is palpation to detect lumps, nodules, or tumors in the breast, mouth, salivary glands, thyroid, subcutaneous tissues, anus, rectum, prostate, testes, ovaries and uterus, and enlarged lymph nodes in the neck, axilla, or groin.

Internal cancers require procedures and tests such as endoscopy, x-rays, magnetic resonance imaging, or ultrasound. Laboratory tests, such as the Pap smear or the FOB test have been employed for the detection of specific cancers.

Screening test performance is usually measured in terms of the following:

PPV and NPV are related to sensitivity and specificity by Bayes rule, where prior probability is prevalence of disease in the population being screened. PPV and, to a lesser extent, NPV are affected by the prevalence of disease in the population being screened. For a given sensitivity and specificity, the lower the prevalence, the lower the PPV and the higher the NPV.

The concepts of sensitivity, specificity, PPV, and NPV are complicated for blood tests that aim to detect many types of cancers from a single blood sample. These test are called multi-cancer detection (MCD) assays or multi-cancer early detection (MCED) assays. These concepts are addressed in the next section.

Cancer is rare when considering all cancers collectively and not just those that have existing screening tests with sufficient evidence of benefit. People having a multi-cancer detection (MCD) screening test may not have detectable disease at the time of screening and, therefore, will not benefit from early detection by these assays. Performance characteristics of MCD assays, along with the resulting cascade of care, are not fully understood, and therefore, the opportunity for harms is much greater with these MCD assays within the framework of what we know about cancer screening.

No MCD assay has been properly evaluated to show a mortality reduction in randomized clinical trials. Indeed, no such trials have been published. No MCD assay has been authorized by the U.S. Food and Drug Administration. However, some MCD assays are being marketed as laboratory developed tests (LDTs). These are assays that are designed, manufactured, and used within a single laboratory. Marketing is allowed under federal regulations, known as Clinical Laboratory Improvement Amendments (CLIA). Customers may be persuaded by the concept that earlier detection is good. It may be good, but there are also negative aspects. Potential negative aspects include overdiagnosis and false positives. For example, a test that detects cancer and indicates that it is most likely to be lung cancer is correct in a sense if the patient has cancer. But it is regarded to be a false positive unless the patient has lung cancer. These negative aspects may be more concerning and more complicated with MCD assays than with screening tests for specific tumor types. The benefits and harms of MCD assays are impossible to determine in the absence of randomized clinical trials that have mortality as an end point.

Cancer risk increases with advancing age, but some individuals have higher cancer risk, including the following:[1]

In general, the balance of benefits to harms is more favorable in higher risk individuals since they have a greater probability of benefiting from the screening while typically having a similar likelihood of experiencing the harms. Recommendations for screening, including the age to start and in some cases whether to screen at all, may differ by risk group. For example, lung cancer screening is recommended only for those with a substantial smoking history, and colorectal cancer screening is recommended to start at an earlier age for those with a family history of the disease.

References

1. van Es N, Le Gal G, Otten HM, et al.: Screening for Occult Cancer in Patients With Unprovoked Venous Thromboembolism: A Systematic Review and Meta-analysis of Individual Patient Data. Ann Intern Med 167 (6): 410-417, 2017. [PUBMED Abstract]

For more information about the recurrence of specific types of cancer, see the PDQ summaries on Adult Treatment.

For nearly all cancers, treatment options and survival are related to stage, characterized by the anatomical extent of disease, as defined by tumor size, invasion of lymph nodes, and distant metastases. It is assumed that detection of cancer at an earlier stage yields better outcomes.

However, the biologic cellular characteristics of cancer, such as grade, hormone sensitivity, and gene overexpression are also recognized as important predictors of cancer behavior. For example, high-grade cancer may be fast growing and quick to metastasize regardless of stage at the time of diagnosis. Therefore, detection of these cancers when they are small may not improve outcome. Randomized controlled trials are most definitive in determining screening benefits.

Improvements in cancer survival over time are difficult to interpret, even when based on data from tumor registries, such as the Surveillance, Epidemiology, and End Results (SEER) Program that include all cases in a given population. They may reflect the benefits of early detection or improved treatment or both, but they may also result from lead-time bias and overdiagnosis, both of which occur commonly with screening.

Lead-time bias results in longer estimated survival of people with screen-identified cancers because the survival calculation includes the time preceding what would have been the clinical diagnosis of the cancer in the absence of screening.

Overdiagnosis results from finding cancers that would never have become manifest clinically. By definition, these tumors have 100% cancer-specific survival. For example, autopsy series of older men show a high percentage of occult early prostate carcinomas.[1] The increased discovery of these cancers through screening, or as incidental findings of tests performed for other purposes (e.g., computed tomography scans), increases the number of cases, gives the appearance of a stage shift, and results in improved survival and/or cure rates, without affecting cause-specific mortality in the population. Examples of cancers with increased incidence, in recent decades, caused primarily by a rise in incidentally detected cases include renal and thyroid cancers.[2] An analysis of data reported by the SEER Program between 1950 and 1996 for 20 major cancers found little correlation between changes in 5-year survival rates and changes in mortality rates.[3] Thus, improvements in 5-year survival rates are largely due to earlier diagnosis and to overdiagnosis. Reductions in incidence rates for late-stage tumors represent a better measure of decreased cancer mortality due to screening than do 5-year survival trends.

Relative survival compares the observed all-cause survival of a cohort of cancer patients with the expected all-cause survival of a comparable (age-, sex-, and race-matched) population. The relative survival rates of patients with early-stage cancer may be inflated artifactually by a screening test because people who choose to be screened are often more health conscious than the general population, engaging in a range of healthy lifestyle behaviors in addition to screening. For example, a report of SEER data showed a 10-year relative survival rate of more than 100% in patients with early-stage prostate, thyroid, and breast cancers, and ductal carcinoma in situ and melanoma. These five tumors are frequently diagnosed by screening in the United States.[4] This phenomenon is sometimes termed healthy screenee bias.

References

1. Woolf SH: Screening for prostate cancer with prostate-specific antigen. An examination of the evidence. N Engl J Med 333 (21): 1401-5, 1995. [PUBMED Abstract]
2. Glasziou PP, Jones MA, Pathirana T, et al.: Estimating the magnitude of cancer overdiagnosis in Australia. Med J Aust 212 (4): 163-168, 2020. [PUBMED Abstract]
3. Welch HG, Schwartz LM, Woloshin S: Are increasing 5-year survival rates evidence of success against cancer? JAMA 283 (22): 2975-8, 2000. [PUBMED Abstract]
4. Marcadis AR, Marti JL, Ehdaie B, et al.: Characterizing Relative and Disease-Specific Survival in Early-Stage Cancers. JAMA Intern Med 180 (3): 461-463, 2020. [PUBMED Abstract]

Findings from studies employing various study designs are used to support a given summary. The strongest design is the randomized controlled trial, although it is impractical to conduct such a trial addressing every question in the field of screening. For each summary of evidence statement, the associated strength of study designs is listed. There are five study designs that are generally used in judging the evidence. In order of strength of design, the five levels are as follows:

Experimental trials are designed to correct for or eliminate the following biases: selection, lead-time, length, and healthy volunteer. The highest level of evidence and greatest benefit would be mortality reduction in a randomized controlled trial. Such evidence is not available for many situations because of the sample size, expense, and duration required. Case-control and cohort studies provide indirect evidence for the effectiveness of screening, but they can be limited by selection bias and healthy volunteer bias. For example, individuals who undergo screening have been shown to have lower mortality from causes unrelated to that screening than do those who did not undergo screening, likely caused by better overall health behavior profiles. Thus, observed differences in survival or mortality by screening history could be caused by these other factors and not the actual screening.[1] New screening modalities may identify more cancers but must be evaluated to determine whether their adoption produces a decrease in cancer-specific mortality versus an increase in lead time bias and overdiagnosis. While randomized trials can answer this question, they may be cost-prohibitive and impractical.[2]

Ecological studies can demonstrate association between screening and improvement in cancer stage and survival, with the adoption of cervical cancer screening as an excellent example.[3] Ecological studies are also valuable to assess the benefits of breast cancer screening, beyond the information gathered in randomized controlled studies, which were conducted over relatively short durations and in an era preceding major developments in cancer treatment.

Descriptive uncontrolled studies on the basis of the experience of individual physicians, hospitals, and non–population-based registries may yield useful information. The performance characteristics of various detection tests, such as sensitivity, specificity, and PPVs are generally first reported in such descriptive studies. The first evidence that screening may be successful is an increase in the incidence of early cancers and a decreased incidence of late-stage metastatic cancers (stage shift); later, a reduction in deaths may occur. These descriptive studies do not establish efficacy because of the absence of an appropriate control group and because they do not address the question whether early initiation of treatment affects patient outcomes (for more information about the Japanese neuroblastoma screening program, see the Potential Benefits and Harms section).

The opinions of authorities may be useful, but may suffer the same weaknesses described earlier. For PDQ's position regarding clinical practice guidelines, see the Scientific Basis for Summary Development section.

References

1. Pierre-Victor D, Pinsky PF: Association of Nonadherence to Cancer Screening Examinations With Mortality From Unrelated Causes: A Secondary Analysis of the PLCO Cancer Screening Trial. JAMA Intern Med 179 (2): 196-203, 2019. [PUBMED Abstract]
2. Irwig L, Houssami N, Armstrong B, et al.: Evaluating new screening tests for breast cancer. BMJ 332 (7543): 678-9, 2006. [PUBMED Abstract]
3. Hakama M, Miller AB, Day NE, eds.: Screening for cancer of the uterine cervix. International Agency for Research on Cancer, 1986.

Another approach to formulating data about cancer screening is modeling. Models can generate information about cancer screening in circumstances where empiric evidence does not exist. A number of probabilistic and computer simulation models have been developed to do the following:

Simulation modeling from the National Cancer Institute's Cancer Intervention and Surveillance Modeling Network (CISNET) program is a major effort in this area. Models have been developed to investigate the impact of screening for various cancers, as per the following examples:

However, caution is necessary in interpreting model findings. Models are only as good as the assumptions upon which they are based, particularly those assumptions about the natural history of the target disease. Many models are complex, especially regarding the interaction of components and the generation of results, and multiple modeling efforts applied to the same screening scenario often give a wide range of quantitative results. In addition, models often produce results that are extrapolations beyond the range of the data input to the models.

References

1. Etzioni R, Gulati R, Tsodikov A, et al.: The prostate cancer conundrum revisited: treatment changes and prostate cancer mortality declines. Cancer 118 (23): 5955-63, 2012. [PUBMED Abstract]
2. Mandelblatt JS, Stout NK, Schechter CB, et al.: Collaborative Modeling of the Benefits and Harms Associated With Different U.S. Breast Cancer Screening Strategies. Ann Intern Med 164 (4): 215-25, 2016. [PUBMED Abstract]
3. de Koning HJ, Meza R, Plevritis SK, et al.: Benefits and harms of computed tomography lung cancer screening strategies: a comparative modeling study for the U.S. Preventive Services Task Force. Ann Intern Med 160 (5): 311-20, 2014. [PUBMED Abstract]

Guidelines for cancer screening increasingly mention the importance of individuals making informed decisions about participating in screening and sharing in decision making. Unbiased and balanced information about the potential benefits and harms of cancer prevention, screening, and treatment plays an important role in the informed decision-making process by the patient.

In a nationwide survey of informed decision-making during patient-provider discussions about colorectal, breast, and prostate cancer screening, patients considered themselves informed but often were not knowledgeable about the risks and benefits of screening. Patients reported that they were usually not asked about their preferences for cancer screening. Although more than 90% of the discussions addressed the advantages of screening, 30% or fewer addressed the disadvantages of screening.[1]

For many cancer screening decisions, shared decision-making is suggested, whereby the provider helps the patient make an informed, values-based choice from among two or more medically reasonable alternatives.[2,3] This is especially important when screening presents potential harms and limited benefits. There are three components of shared decision-making:[4]

Patient decision aids can be useful, as they encourage patients to interpret evidence in the context of their own goals and concerns and to make decisions with their physicians. Decision aids are available in leaflets, booklets, videos, and websites, and they may include patient stories. The International Patient Decision Aid Standards (IPDAS) Collaboration has developed a method for evaluating the quality of decision aids.[5]

A Cochrane review of 115 randomized controlled trials of shared decision-making supported by decision aids indicated that, in general, decision aids improve patient knowledge about options and risks; reduce decisional conflict related to feeling uninformed or unclear about personal values; and stimulate patients to take more active roles in decision-making. In some cases, decision aids have also been noted to reduce the number of patients choosing major elective invasive surgery over more conservative options, and in fewer patients choosing cancer screening. The effect of using decision aids may lengthen or shorten the duration of the consultation.[6]

After using a decision aid that included information about breast cancer overdetection, more women met the threshold for adequate overall knowledge about screening benefits and risks. Women whose decision aids included information about overdetection were less enthusiastic about screening and were less likely to participate.[7]

References

1. Hoffman RM, Lewis CL, Pignone MP, et al.: Decision-making processes for breast, colorectal, and prostate cancer screening: the DECISIONS survey. Med Decis Making 30 (5 Suppl): 53S-64S, 2010 Sep-Oct. [PUBMED Abstract]
2. O'Connor AM, Llewellyn-Thomas HA, Flood AB: Modifying unwarranted variations in health care: shared decision making using patient decision aids. Health Aff (Millwood) Suppl (Variation): VAR63-72, 2004. [PUBMED Abstract]
3. Charles C, Gafni A, Whelan T: Shared decision-making in the medical encounter: what does it mean? (or it takes at least two to tango). Soc Sci Med 44 (5): 681-92, 1997. [PUBMED Abstract]
4. Coulter A, Collins A: Making Shared Decision-Making a Reality: No Decision About Me, Without Me. The King's Fund, 2011. Also available online. Last accessed August 17, 2023.
5. Elwyn G, O'Connor A, Stacey D, et al.: Developing a quality criteria framework for patient decision aids: online international Delphi consensus process. BMJ 333 (7565): 417, 2006. [PUBMED Abstract]
6. Stacey D, Hawker G, Dervin G, et al.: Decision aid for patients considering total knee arthroplasty with preference report for surgeons: a pilot randomized controlled trial. BMC Musculoskelet Disord 15: 54, 2014. [PUBMED Abstract]
7. Hersch J, Barratt A, Jansen J, et al.: Use of a decision aid including information on overdetection to support informed choice about breast cancer screening: a randomised controlled trial. Lancet 385 (9978): 1642-52, 2015. [PUBMED Abstract]

Disease-specific mortality, the most widely accepted end point in randomized clinical trials of cancer screening, assumes that the cause of death can be accurately determined and that the screening and subsequent treatments have negligible effects on other causes of death. By contrast, all-cause mortality depends only on the date and accurate ascertainment of death. Because cancer deaths generally comprise only a small fraction of all deaths in a screening trial, the statistical power to detect a significant reduction in all-cause mortality in any single trial, or even in some meta-analyses, is typically low. Nevertheless, all-cause mortality should be considered in conjunction with disease-specific mortality to reduce the possibility that a major effect from screening is hidden by misclassification in cause of death. The National Lung Screening Trial of low-dose computed tomography lung cancer screening did demonstrate a significant reduction in all-cause mortality and a meta-analysis of flexible sigmoidoscopy trials also showed a significant all-cause mortality benefit.[1,2] In contrast, meta-analyses of mammography screening trials have failed to demonstrate a significant reduction in all-cause mortality.[3]

References

1. Aberle DR, Adams AM, Berg CD, et al.: Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med 365 (5): 395-409, 2011. [PUBMED Abstract]
2. Tinmouth J, Vella ET, Baxter NN, et al.: Colorectal Cancer Screening in Average Risk Populations: Evidence Summary. Can J Gastroenterol Hepatol 2016: 2878149, 2016. [PUBMED Abstract]
3. Nelson HD, Fu R, Cantor A, et al.: Effectiveness of Breast Cancer Screening: Systematic Review and Meta-analysis to Update the 2009 U.S. Preventive Services Task Force Recommendation. Ann Intern Med 164 (4): 244-55, 2016. [PUBMED Abstract]

Several measures of risk are used in cancer research. Absolute risk or absolute rate measures the actual cancer risk or rate in a population or subgroup (e.g., U.S. population, or White or Black individuals). For example, the Surveillance, Epidemiology, and End Results (SEER) Program reports risk and rate of cancer in specific geographic areas of the United States.

Rates are often adjusted (e.g., age-adjusted rates) to allow a more accurate comparison of rates over time or among groups. The purpose of the adjustment is to make the groups more alike with respect to important characteristics that may affect the conclusions. For example, when the SEER Program compares cancer rates over time in the United States, the rates are adjusted to one age distribution because cancer rates are usually higher in older age groups.

Relative risk (RR) compares the risk of developing cancer among those who have a particular characteristic or exposure with those who do not. RR is expressed as a ratio of risks or rates. If the RR is one, the risk of both groups is the same; if the RR is greater than one, the exposure or characteristic is associated with a higher cancer risk; if the RR is less than one, it is associated with a lower cancer risk. RR is often used in clinical trials of cancer prevention and screening to estimate the reduction in cancer risk or risk of death, respectively.

An odds ratio (OR) is often used as an estimate of the RR. It, too, indicates whether there is an association between an exposure or characteristic and cancer. It compares the odds of an exposure or characteristic among cancer cases with the odds among a comparison group without cancer.

Uncertainty in an estimated OR (or RR) is sometimes presented as a confidence interval (CI), which represents the range of values for the OR (or RR) that is plausible based on the observed study data. If the CI range contains 1, it indicates that the observed data would not be unusual if the two groups truly do not differ in their odds (or risk) of experiencing the event.

Risk or rate difference (or excess risk) compares the actual cancer risk or rate among at least two groups of people, based on an important characteristic or exposure, by subtracting the risks or rates from one another (e.g., subtracting lung cancer rates among nonsmokers from that of cigarette smokers estimates the excess risk of lung cancer due to smoking). This can be used in public health to estimate the number of cancer cases that could be avoided if an exposure were reduced or eliminated in the population.

Population-attributable risk measures the proportion of cancers that can be attributed to a particular exposure or characteristic. It combines information about the RR of cancer associated with a particular exposure and the prevalence of that exposure in the population, and estimates the proportion of cancer cases in a population that could be avoided if an exposure were reduced or eliminated.

The number needed to screen (NNS) is a metric of screening efficiency defined as the number of people that must participate in a screening program for one death to be prevented over a defined time interval. NNS estimates are typically derived from screening trial data. For screening modalities that can prevent cancer, as well as detect it earlier, such as endoscopic screening for colorectal cancer, an NNS to prevent one incident cancer is also a useful metric.

Average life-years saved estimates the number of years of life that an intervention saves, on average, for an individual who receives the intervention. This reflects mortality reduction and life extension (or avoidance of premature deaths).

When overdiagnosis occurs with screening, absolute and relative measures of risk calculated from studies with participants whose disease was screen-detected must be carefully interpreted. If the chance of diagnosis as a result of screening (either due to screening itself or diagnostic workup of a positive screen) is positively correlated with a given factor, risk measures will be inflated relative to those calculated from unscreened populations. The degree of inflation depends on prevalence of screening and the degree of correlation. As screening is adopted in population settings, trend data for risk will be affected similarly.[1]

Many of the groundbreaking observational studies in cancer etiology were performed before cancer screening was widely adopted. Given the extensive uptake of screening for certain cancers, recently conducted observational etiologic studies include many participants whose disease was detected through screening. This may skew the results of these trials.

For example, assume that men with blue eyes are more likely to participate in prostate-specific antigen screening and more willing to undergo prostate biopsy. Despite the absence of a biologic association between blue eyes and prostate cancer, more cancers will be detected in blue-eyed men because they are screened. Since many prostate cancers can be safely left undiagnosed and untreated, many of these cancers in blue-eyed men represent overdiagnosis.

When overdiagnosis occurs due to screening, and when screening behavior or willingness to seek diagnostic evaluation is correlated with risk factors, relative risk measures generated from these studies may be over-stated and results may be misleading.

References

1. Tangen CM, Goodman PJ, Till C, et al.: Biases in Recommendations for and Acceptance of Prostate Biopsy Significantly Affect Assessment of Prostate Cancer Risk Factors: Results From Two Large Randomized Clinical Trials. J Clin Oncol 34 (36): 4338-4344, 2016. [PUBMED Abstract]

The PDQ cancer information summaries are reviewed regularly and updated as new information becomes available. This section describes the latest changes made to this summary as of the date above.

Editorial changes were made to this summary.

This summary is written and maintained by the PDQ Screening and Prevention Editorial Board, which is editorially independent of NCI. The summary reflects an independent review of the literature and does not represent a policy statement of NCI or NIH. More information about summary policies and the role of the PDQ Editorial Boards in maintaining the PDQ summaries can be found on the About This PDQ Summary and PDQ® Cancer Information for Health Professionals pages.