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 Experiment Videos

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

Constance D Lehman1, Sarah F Mercaldo1, Shadi Azam2

  • 1Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit St. WAC 240, Boston, MA 02114.

Radiology
|June 23, 2026
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

Longitudinal Research02:20

Longitudinal Research

Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...

You might also read

Related Articles

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

Sort by
Same author

Associations of alcohol use with expression of stromal markers in benign breast biopsy samples.

British journal of cancer·2026
Same author

Impact of AI-based Slab Reconstruction Technology on the Diagnostic Accuracy of Screening Digital Breast Tomosynthesis.

Radiology·2026
Same author

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

JAMA network open·2026
Same author

Performance of an image-only deep learning breast cancer risk model with the addition of a polygenic risk score.

British journal of cancer·2026
Same author

The impact of social drivers of health on delayed time to breast cancer surgery.

British journal of cancer·2026
Same author

Evaluating the Performance and Clinical Utility of AI-driven Diagnostic Tools in Radiology.

Radiology·2025
Same journal

Separating Signal from Noise: When Deep Learning Clarifies Physiology in MR Lymphangiography of Lower Extremity.

Radiology·2026
Same journal

Congratulations to the 2026 Editorial Fellows.

Radiology·2026
Same journal

Ten-year Longitudinal Relationship between Spinal Degenerative Lesions in Axial Spondyloarthritis at MRI and Radiography in the DESIR Cohort.

Radiology·2026
Same journal

Standardized Knee Meniscus MRI Reporting: An Interdisciplinary Delphi Consensus.

Radiology·2026
Same journal

Mind the Gap: Cardiac MRI and the Future of Heart Failure Risk Prediction.

Radiology·2026
Same journal

Beyond Static Prediction: Tracking AI Breast Cancer Risk over Time.

Radiology·2026
See all related articles

Artificial intelligence (AI) deep learning (DL) breast cancer risk scores from mammograms increase over time in women who develop cancer, unlike stable scores in cancer-free women. This dynamic change supports AI risk scores as biomarkers for personalized breast cancer screening and prevention.

Area of Science:

  • Radiology and Medical Imaging
  • Artificial Intelligence in Healthcare
  • Oncology and Cancer Research

Background:

  • Artificial intelligence (AI)-based deep learning (DL) models offer individualized 5-year breast cancer risk estimates from screening mammograms.
  • While validated for static risk prediction, the temporal behavior of these AI risk scores is not well-established.

Purpose of the Study:

  • To investigate the longitudinal changes in image-based AI deep learning (DL) risk scores.
  • To determine if risk score trajectories differ between women who develop breast cancer and those who remain cancer-free.

Main Methods:

  • Retrospective cohort study of 158,807 screening mammograms from 54,014 women (2009-2019).
  • Comparison of 817 women diagnosed with breast cancer within one year against cancer-free controls.

Related Experiment Videos

  • Validated image-only DL model used to generate continuous 5-year risk scores; linear mixed-effects models analyzed score trajectories.

    • Main Results:

    • In women who developed cancer, median AI risk scores increased from 2.1 (6 years pre-diagnosis) to 6.6 (index exam).
    • Cancer-free women exhibited stable scores (1.8-2.2) over time.
    • Longitudinal analysis showed significant score increases in the cancer group (slope 1.13/year) versus minimal change in controls (slope 0.09/year).

    Conclusions:

    • AI-based breast cancer risk scores derived from mammograms demonstrate dynamic evolution over time.
    • Diverging score trajectories between cancer and cancer-free groups highlight their potential as dynamic biomarkers.
    • These findings support the use of AI risk scores for risk-adaptive screening and prevention strategies.