Cumulative risk of false positive test in relation to breast symptoms in mammography screening: a historical

Deependra Singh1,2, Janne Pitkäniemi3, Nea Malila3,4

  • 1Finnish Cancer Registry, Unioninkatu 22, 00130, Helsinki, Finland. deependra.singh@cancer.fi.

Mammography has been found effective as the primary screening test for breast cancer. We estimated the cumulative probability of false positive screening test results with respect to symptom history reported at screen. A historical prospective cohort study was done using individual screening data from 413,611 women aged 50-69 years with 2,627,256 invitations for mammography screening between 1992 and 2012 in Finland. Symptoms (lump, retraction, and secretion) were reported at 56,805 visits, and 48,873 visits resulted in a false positive mammography result. Generalized linear models were used to estimate the probability of at least one false positive test and true positive at screening visits. The estimates were compared among women with and without symptoms history. The estimated cumulative probabilities were 18 and 6 % for false positive and true positive results, respectively. In women with a history of a lump, the cumulative probabilities of false positive test and true positive were 45 and 16 %, respectively, compared to 17 and 5 % with no reported lump. In women with a history of any given symptom, the cumulative probabilities of false positive test and true positive were 38 and 13 %, respectively. Likewise, women with a history of a 'lump and retraction' had the cumulative false positive probability of 56 %. The study showed higher cumulative risk of false positive tests and more cancers detected in women who reported symptoms compared to women who did not report symptoms at screen. The risk varies substantially, depending on symptom types and characteristics. Information on breast symptoms influences the balance of absolute benefits and harms of screening.

Related Concept Videos

Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
1.7K
Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT01:25

Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT

Calcium-Scoring CT ScanA calcium-scoring CT scan, also known as coronary artery calcium (CAC) scan, detects calcium deposits in the coronary arteries. This test assesses the risk of coronary artery disease (CAD), which can lead to cardiovascular events such as angina, heart failure, and sudden cardiac arrest.A calcium-scoring CT scan is generally recommended for individuals at intermediate risk of CAD without symptoms. It includes:Men aged 40-75 and women aged 50-75: Especially those with a...
714
Imaging Studies for Cardiovascular System III: X-Ray01:20

Imaging Studies for Cardiovascular System III: X-Ray

The most common cardiovascular diagnostic test is an X-ray. It produces images of the heart, blood vessels, and adjacent structures.
Definition and Purpose
An X-ray, or radiograph, is a non-invasive method that uses ionizing radiation to take images of internal structures. It is mainly used in cardiac imaging to examine the heart, lungs, and major blood vessels, aiming to identify abnormalities in the heart's size, shape, and position, such as heart failure, congenital defects, and vascular...
590
Receiver Operating Characteristic Plot01:15

Receiver Operating Characteristic Plot

A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
569
Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...
666
Errors In Hypothesis Tests01:14

Errors In Hypothesis Tests

When performing a hypothesis test, there are four possible outcomes depending on the actual truth (or falseness) of the null hypothesis and the decision to reject or not.
6.2K