The benefit of cancer screening tests like pap smears, colonoscopy, mammography and others are reported in two ways. The most common way is the relative risk reduction. This is a ratio of the risk in the screened group divided by the risk in the non-screened group. Relative risk does not take into account the baseline risk in the whole population. The other way of reporting benefit of a screening test is called absolute risk reduction. Absolute risk reduction is the risk in the non screened group minus the risk in the screened group. Relative risk reduction always looks a lot bigger than absolute risk reduction because it does not take into account the baseline risk. Absolute risk reduction is what you really want to know. Absolute risk reduction lets you know how much your risk is reduced by taking the screening test. It is always a lot lower than the relative risk reduction. Absolute risk reduction of the most common cancer screening tests is very low, usually 1% or less.
Here are some examples:
- Mammography: Relative breast cancer death risk reduction 30%; Absolute cancer death risk reduction 1%
- Colonoscopy: Relative colon cancer death risk reduction 50%; Absolute death risk reduction 0.15%
- Pap Smear: Relative cervical cancer death risk reduction 80%; Absolute cervical cancer risk reduction .08%
- PSA (test for prostate cancer): Relative risk reduction 64%; Absolute risk reduction .09%
Another number that can be helpful is called Number Needed to Screen (NNS). NNS is the number of people who need to be screened to prevent 1 death from the disease. NNS is just 1 divided by the absolute death risk reduction for the screening test. Here are the NNS’s for the examples above.
- Mammography: NNS 1/.01= 100 (this is mammograms every 2 years from age 50-75 so the the 100 patients means about 1100 mammograms).
- Colonoscopy: NNS 1/.15=667
- Pap Smear: NNS 1/.08=1,440
- PSA: NNS 1/.09 =1,111
Sensitivity and Specificity
Any test, including cancer screening tests have a certain sensitivity and specificity.
Sensitivity
The sensitivity of a test is the probability that the test will detect the disease if it is present. In other words it measures how likely it is to get a false negative test. The higher the sensitivity, the less likely the test will be negative if the person has the disease being tested for. It is expressed as a percentage.
Specificity
The specificity of a test is the probability that a person with a positive test will have the disease. In other words it measures how likely it is to get a false positive test. The higher the specificity, the more likely a person with a positive test will have the disease. It is also expressed as a percentage
An ideal test has both a high sensitivity and specificity. Lets look at the sensitivity and specificity of our cancer screening tests.
- Mammography: sensitivity 72%; specificity 98%
- Colonoscopy: sensitivity 85%; specificity 90%
- Pap Smear with HPV testing: sensitivity 95%; specificity 97%
- PSA: Sensitivity 30%; Specificity 91%
Bayes Formula
All of these cancer screening tests have high specificity but somewhat less sensitivity except for Pap smears with HPV testing, which have high sensitivity and high specificity. So why are the absolute death reductions so low? Part of it has to do with something called Bayes Formula. It turns out that the chance of a false positive has to do not just with the specificity, but also the frequency of the disease in the population being screened. If the frequency of the disease in the population being screened is low, then even with a test that has high specificity, the chance of a positive test being a false positive is higher than than the specificity would suggest. The frequency of all of the above cancers is low in any 1 year in the population so that means that false positive cancer screening screening tests are common. Below are population frequencies for each cancer per year.
- Breast cancer: annual prevalence in women 0.13%. Chance of a positive mammogram being a true positive: 28%. This means that a positive mammogram has 72% chance of being a false positive. On the other hand, a negative mammogram has an 8.7% chance of being a false negative, that is of missing a breast cancer
- Colorectal cancer: annual prevalence in population .03%. Chance of a positive colonoscopy being a true positive: 2.5%. That means that a colonoscopy that finds something only has a 2.5% chance of being cancer. On the other hand, a negative colonoscopy has only a .005% chance of being a false negative. That means a negative colonoscopy has only a tiny chance of missing a cancer.
- Cervical Cancer: annual prevalence in population 0.0077%. Chance of a positive pap smear being cancer: 0.24%. That means that 99.86% of positive pap smears with HPV testing will not be cervical cancer. On the other hand the chance that a negative pap smear with HPV testing will be a false negative is .00041%. Obviously a negative pap smear with HPV has an infinitesimally small chance of missing a cervical cancer. Although the chance of finding a cervical cancer is very low, the pap smear with HPV also finds precancerous changes in the cervix. Treatment of these precancerous cells prevents cervical cancer from developing. That is a big reason why the prevalence of cervical cancer is so low.
- Prostate Cancer: annual prevalence in men .66%. Chance of a positive PSA (>4) being a true positive 2.18%. That means a PSA of >4.0 has a 98% chance that no prostate cancer is present. On the other hand a PSA of <4.0 has a 5.1% chance of missing a prostate cancer.
The somewhat lower sensitivity of mammography, colonoscopy and especially PSA means that false negatives are fairly common, for these tests.
The combination of false positives, false negatives and low prevalence of these cancers in the population all contribute to the small absolute death risk reduction for all four of these cancer screening tests. For patients at substantially higher risk, such as strong family history of breast or colon cancer, the screening tests perform much better, because the high risk population has a much greater disease prevalence than the general population.
Over Diagnosis
Another problem with cancer screening tests is over diagnosis. Over diagnosis means that a positive test finds a cancer, but the cancer grows so slowly or spontaneously disappears so that it never would have caused any symptoms in the person. Over diagnosis then leads to unnecessary treatment. So let’s look at the over diagnosis rate for our four cancer screening tests.
- Mammography: Over diagnosis rate for women 40 and over is 12%. This means the 12% of women diagnosed with breast cancer by mammography will be treated for cancer unnecessarily.
- Colonoscopy: The over diagnosis problem with colonoscopy results from the removal of polyps. All visible polyps are removed during colonoscopy. The polyps that have some chance of turning into cancer are called adenomatous polyps. Only 8% of these turn into invasive colon cancer over 10 years. That suggests that 92% of the adenomatous polyps removed at colonoscopy would never turn into cancer. Removal of all adenomatous polyps does prevent some colon cancers. It is not possible to know at the time of removal which polyps are going to progress. The cost of prevention of some colorectal cancers is substantial over diagnosis.
- Pap Smear with HPV: Overdiagnosis of precancerous cervical lesions is high. We now know that cervical cancer is caused almost exclusively by the HPV virus. On the other hand, women often clear an HPV infection on their own without treatment. This is particularly the case with young women, which is why pap smears and HPV testing are not recommended before age 21. Precancerous cervical lesions are graded CIN1-CIN3, CIN3 being the most severe. Overdiagnosis rates are higher for the lower grade lesions, which most often clear on their own. The figures for over diagnosis over women’s lifetime were 70.6% for CIN1+, 63.2% for CIN2+, and 50.0% CIN3+.
- PSA test for prostate cancer: Low grade prostate cancer is common as men age. Many of these cancers would never cause symptoms during the lifetime of the men. Current estimates are that 60% of prostate cancers detected by PSA would never cause symptoms or death from prostate cancer. Treatment of prostate cancer often results in permanent urinary incontinence and/or sexual dysfunction. This very large over diagnosis and therefore unnecessary treatment is why PSA testing is so controversial. There are certain populations of men who are at high risk of aggresive prostate cancer and these men are probably the only ones who should have routine PSA testing. Here is a link to a risk calculator for prostate cancer: PCPT Risk Calculator.
Bottom Line
Despite the high specificity of cancer screening tests, Bayes Formula shows that false positive tests will be more frequent than true positive tests. For mammograms, colonoscopy and PSA the somewhat low sensitivity means that there will be some false negative tests. In other words, they will miss a few cancers. Pap smear with HPV has the lowest chance of missing a cancer. Over diagnosis is a problem with all cancer screening tests, resulting in unnecessary treatment. This is particularly a problem for breast cancer and especially prostate cancer. The low absolute death risk reduction values and the over diagnosis problems for these tests do not mean you should not be screened, especially if you are in a higher risk population due to family history or other causes of higher cancer risk. All of these screening tests save lives, just not as many as the relative risk values suggest. The vast majority of people will not benefit from these tests and some will be harmed by unnecessary treatment, but a small but substantial number will have their lives saved.