Currently Viewing
Biomarker Tests
April 13, 2014 – Aimee Swartz
AACR update: New immunotherapy drug shows benefit in lung cancer
April 09, 2014 – Debu Tripathy
The other option: Surrogacy after cancer
March 26, 2014 – Guest
So much to celebrate
March 25, 2014 – Guest
Adding surgery in metastatic kidney cancer could improve survival
March 21, 2014 – Elizabeth Whittington
Inflammation and fatigue
March 21, 2014 – Kathy LaTour
The Little Couple has cancer
March 20, 2014 – Kathy LaTour
Increased screening leads to decrease in colorectal cancer
March 18, 2014 – Elizabeth Whittington
Lynch Syndrome Awareness Day: March 22
March 18, 2014 – Elizabeth Whittington

Biomarker Tests

Biomarker tests must strike a balance between sensitivity and specificity.

BY Aimee Swartz
PUBLISHED April 13, 2014

Screening tests confirm or disprove a healthcare provider’s suspicion that a patient has cancer so that a medical decision can then be made. But in order for a test to be considered useful, it must be able to correctly distinguish between a person who has the disease and a person who does not. Ideally, a test would be 100 percent accurate; however, while tests in the clinic are usually quite accurate, there are still some errors to account for. The following measures help determine a test’s usefulness:

Accuracy is the likelihood that a positive test result indicates disease or that a negative test result excludes disease. The accuracy of a test is determined by how sensitive and specific it is.

Sensitivity measures the percentage of people with cancer correctly identified as having cancer. If a biomarker test is not sensitive enough, the results may suggest a person does not have cancer when in fact he or she does. This is called a “false negative.” If a test is highly sensitive, it will identify most people with the disease—that is, it will result in few false-negative results.

Specificity measures the percentage of people who do not have cancer correctly identified as being free of disease. If a biomarker test is not specific enough, the results may suggest a person has cancer when in fact he or she does not. This is called a “false positive.” If a test is highly specific, only a small number of people will test positive for the disease who do not have it—in other words, it will result in few false-positive results.

Positive Predictive Value is the percentage of patients who test positive for a disease who actually have the disease.

Negative Predictive Value is the percentage of patients who test negative for a disease who do not actually have the disease.

[Read how biomarkers are changing the face of cancer screening and diagnosis.]

Be the first to discuss this article on CURE's forum. >>
Talk about this article with other patients, caregivers, and advocates in the General Discussions CURE discussion group.

Related Articles

1
×

Sign In

Not a member? Sign up now!
×

Sign Up

Are you a member? Please Log In