Related Experiment Video
Updated: Jun 2, 2026
05:28
Clinical Imaging of Microwave Mammography
Published on: November 14, 2025
Explaining the difference in prognosis between screen-detected and symptomatic breast cancers
P C Allgood1, S W Duffy, O Kearins
1CR-UK Centre for Epidemiology, Mathematics and Statistics, Wolfson Institute for Preventive Medicine, Charterhouse Square, London WC1M 6BQ, UK. p.allgood@qmul.ac.uk
British Journal of Cancer
|May 5, 2011
Summary
Screen-detected breast cancer offers a survival advantage, primarily due to earlier detection of smaller tumors and lymph node involvement. This finding highlights the impact of screening on improving outcomes for invasive breast cancer patients.
Area of Science:
- Oncology
- Public Health
- Epidemiology
Background:
- Analysis of 10-year survival data for 19,411 women aged 50-64 with invasive breast cancer in the UK.
- Comparison of survival rates between screen-detected and symptomatic cases.
Purpose of the Study:
- To estimate the survival advantage of screen-detected breast cancer.
- To attribute this advantage to prognostic variables or specific survival differences.
Main Methods:
- Studied tumor size, histological grade, and Nottingham Prognostic Index in narrow categories.
- Investigated prognostic factor distribution in screen-detected vs. symptomatic tumors.
- Adjusted for lead time bias.
Main Results:
- Unadjusted 10-year survival: 85.5% (screen-detected) vs. 62.8% (symptomatic).
- Adjusted survival for screen-detected cases: 79.3% after lead time bias adjustment.
- Small survival differences within narrow prognostic categories.
Conclusions:
- Lead time, tumor size, and node status explained 97% of the survival advantage.
- A small proportion of the advantage may be due to length bias or overdiagnosis.
Related Concept Videos
01:21Cancer Survival Analysis
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
01:13Sensitivity, 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...
Sensitivity is the...
02:07Tumor Progression
Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
Colon cancer is one of the best-documented examples of tumor progression. Early mutation in the APC gene in colon cells causes a small growth on the colon wall called a polyp. With time, this polyp grows into a benign, pre-cancerous tumor. Further...
Colon cancer is one of the best-documented examples of tumor progression. Early mutation in the APC gene in colon cells causes a small growth on the colon wall called a polyp. With time, this polyp grows into a benign, pre-cancerous tumor. Further...
01:20Comparing the Survival Analysis of Two or More Groups
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...