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Related Concept Videos

Cancer Survival Analysis01:21

Cancer Survival Analysis

334
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...
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Actuarial Approach01:20

Actuarial Approach

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The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
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Kaplan-Meier Approach01:24

Kaplan-Meier Approach

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The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
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Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

162
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...
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  1. Home
  3. Research Domains
  5. Biomedical And Clinical Sciences
  7. Oncology And Carcinogenesis
  9. Predictive And Prognostic Markers
  11. Breast Cancer Screening Interval: Effect On Rate Of Late-stage Disease At Diagnosis And Overall Survival.
  1. Home
  3. Research Domains
  5. Biomedical And Clinical Sciences
  7. Oncology And Carcinogenesis
  9. Predictive And Prognostic Markers
  11. Breast Cancer Screening Interval: Effect On Rate Of Late-stage Disease At Diagnosis And Overall Survival.

Related Experiment Video

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
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Breast Cancer Screening Interval: Effect on Rate of Late-Stage Disease at Diagnosis and Overall Survival.

Margarita L Zuley1, Andriy I Bandos2, Stephen W Duffy3

  • 1Department of Radiology, Division of Breast Imaging, School of Medicine & University of Pittsburgh Medical Center, University of Pittsburgh, Pittsburgh, PA.

Journal of Clinical Oncology : Official Journal of the American Society of Clinical Oncology
|August 21, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

Annual mammography screening significantly reduces late-stage breast cancer diagnoses and improves overall survival. This study supports annual screening for women aged 40 and older for better breast cancer outcomes.

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Area of Science:

  • Oncology
  • Radiology
  • Public Health

Background:

  • Screening mammography's impact on breast cancer outcomes remains debated.
  • Real-world data is crucial for understanding screening interval effectiveness.

Purpose of the Study:

  • To evaluate the association between different screening mammography intervals and late-stage breast cancer diagnosis.
  • To assess the impact of screening intervals on overall survival (OS).

Main Methods:

  • Retrospective analysis of 8,145 patients with breast cancer and prediagnosis screening history (2004-2019).
  • Categorization of screening intervals: annual (≤15 months), biennial (>15-≤27 months), and intermittent (>27 months).
  • Primary endpoint: late-stage cancer (TNM stage IIB+); Secondary endpoint: OS, analyzed using multivariable logistic and proportional hazards regression.

Main Results:

  • Late-stage cancer diagnosis rates increased with longer screening intervals: 9% (annual), 14% (biennial), 19% (intermittent) (P < .001).
  • Biennial and intermittent screening were associated with substantially worse OS compared to annual screening.
  • These trends persisted across age, race, and menopausal status subgroups.

Conclusions:

  • Annual mammographic screening is linked to a reduced risk of late-stage breast cancer and improved overall survival.
  • The findings support the recommendation of annual screening mammography for women aged 40 and older.