Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Cancer Survival Analysis01:21

Cancer Survival Analysis

870
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...
870
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

631
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
631
Relative Risk01:12

Relative Risk

2.5K
Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
2.5K
Hazard Ratio01:12

Hazard Ratio

786
The hazard ratio (HR) is a widely used measure in clinical trials to compare the risk of events, such as death or disease recurrence, between two groups over time. It reflects the ratio of hazard rates—the instantaneous risk of the event occurring—between a treatment group and a control group. This measure provides valuable insights into the relative effectiveness of a treatment by assessing how the risk of an event differs between the two groups.
For example, in a clinical trial...
786

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Longitudinal Analysis of Changes in Deep Learning Image-based Breast Cancer Risk Scores over Time.

Radiology·2026
Same author

"Enhancing Patient Understanding of Radiology Reports Through LLM-Generated Summaries, Clickable Terms, and AI Videos".

Journal of the American College of Radiology : JACR·2026
Same author

Review of breast arterial calcifications for cardiovascular disease risk prediction.

NPJ cardiovascular health·2026
Same author

Correction: Cè et al. Decoding Radiomics: A Step-by-Step Guide to Machine Learning Workflow in Hand-Crafted and Deep Learning Radiomics Studies. <i>Diagnostics</i> 2024, <i>14</i>, 2473.

Diagnostics (Basel, Switzerland)·2026
Same author

An Intensity-Based Cropping Approach for Fast, Interpretable, and Robust Localization of the Knee Joint in Radiographs.

Journal of imaging informatics in medicine·2026
Same author

A Deep Learning Breast Cancer Risk Model for Precise Supplemental Screening.

JAMA network open·2026
Same journal

Correction for: "High-Resolution 3-T MRI of the Fingers: Review of Anatomy and Common Tendon and Ligament Injuries".

AJR. American journal of roentgenology·2026
Same journal

June 2026: Artificial Intelligence (AI) for Future Breast Cancer Prediction, AI for Fracture Detection, CEUS for O-RADS Ultrasound, and More.

AJR. American journal of roentgenology·2026
Same journal

Deep Learning Reconstruction: Making Fast Musculoskeletal MRI Work in Practice.

AJR. American journal of roentgenology·2026
Same journal

Histotripsy and the Quest for Clarity: An Innovative Technology Awaiting Clinical Proof.

AJR. American journal of roentgenology·2026
Same journal

The Intestinal Stroke Pathway: Redefining the Radiologist's Role in Mesenteric Ischemia.

AJR. American journal of roentgenology·2026
Same journal

Utilization of Emergency Department (ED) Neuroimaging From 2016 to 2025: Analysis in Over 3 Million ED Encounters from the Multicenter Cosmos EHR Dataset.

AJR. American journal of roentgenology·2026
See all related articles
  1. Home
  3. Risk Stratification For Breast Cancer Screening: Ajr Expert Panel Narrative Review.
  1. Home
  3. Risk Stratification For Breast Cancer Screening: Ajr Expert Panel Narrative Review.

Related Experiment Video

Integrating Augmented Reality Tools in Breast Cancer Related Lymphedema Prognostication and Diagnosis
06:03

Integrating Augmented Reality Tools in Breast Cancer Related Lymphedema Prognostication and Diagnosis

Published on: February 6, 2020

6.1K

Risk Stratification for Breast Cancer Screening: AJR Expert Panel Narrative Review.

Cody Schopf1, Susan M Domchek2, Jeffrey A Tice3

  • 1University of Washington, Department of Radiology and Fred Hutchinson Cancer Center, Seattle, USA.

AJR. American Journal of Roentgenology
|May 6, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Personalized breast cancer screening, guided by risk prediction tools, can optimize benefits and reduce harms. This review examines current models, guidelines, and future directions for risk-stratified screening strategies.

More Related Videos

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

9.9K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

898

Related Experiment Videos

Integrating Augmented Reality Tools in Breast Cancer Related Lymphedema Prognostication and Diagnosis
06:03

Integrating Augmented Reality Tools in Breast Cancer Related Lymphedema Prognostication and Diagnosis

Published on: February 6, 2020

6.1K
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

9.9K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

898

Area of Science:

  • Radiology and Oncologic Imaging
  • Medical Informatics
  • Public Health

Background:

  • Early breast cancer detection significantly reduces mortality.
  • Screening effectiveness is enhanced by aligning strategies with individual cancer risk profiles.
  • Risk prediction tools aid in assessing patient-specific cancer risks.

Purpose of the Study:

  • To review breast cancer risk stratification methods and prediction models.
  • To analyze current societal guidelines on risk assessment tool utilization.
  • To propose a practical framework for integrating risk assessment into clinical practice.

Main Methods:

  • Narrative review of breast cancer risk stratification.
  • Overview of commonly used clinical risk prediction models.
  • Analysis of existing professional society guidelines.

    • Main Results:

    • Variability exists in professional society recommendations for risk assessment.
    • Uncertainty persists regarding standardized risk assessment approaches and tool implementation.
    • Deep learning models show potential for future risk stratification.

    Conclusions:

    • Aligning screening with individual risk profiles improves benefit-harm balance.
    • Standardized risk assessment and clear guidelines are needed for effective implementation.
    • Future research, including deep learning, can refine breast cancer screening strategies.