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

Glassware Calibration01:11

Glassware Calibration

Accurate calibration of glassware, such as volumetric flasks, pipettes, and burettes, is essential to ensure accurate measurements in the analytical laboratory. Calibration helps maintain consistency across measurements and prevents errors arising from inaccurate volumes.
Volumetric flasks: Volumetric flasks are designed to prepare aqueous solutions of precise volumes accurately with a calibration line on the neck. To calibrate a volumetric flask, it is important to fill it with distilled...

You might also read

Related Articles

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

Sort by
Same author

Design of a cluster randomized multi-level intervention to decrease barriers to minority cancer patient referral and enrollment to cancer clinical trials: The ACT WONDER<sup>2</sup>S study.

Contemporary clinical trials·2025
Same author

A Formative Evaluation of Interventions to Enhance Clinical Trial Diversity Guided by the Socioecological Model.

Cancers·2025
Same author

An examination of factors associated with disparities in clinical trial eligibility guided by the Socioecological Model.

Cancer·2025
Same author

Large language models in cancer: potentials, risks, and safeguards.

BJR artificial intelligence·2025
Same author

Suicide Risk Screening for Head and Neck Cancer Patients: An Implementation Study.

Applied clinical informatics·2024
Same author

Reproductive risk factors for breast cancer and association with novel breast density measurements among Hispanic, Black, and White women.

Breast cancer research and treatment·2023
Same journal

Deep Learning for Opportunistic Vertebral Fracture Detection on Routine Thoraco-abdominal Computed Tomography: A Systematic Review and Hierarchical Summary Receiver Operating Characteristic Meta-analysis of Patient-level Diagnostic Test Accuracy.

Academic radiology·2026
Same journal

"Where are They Now?": A Single Institution's 10-Years Experience with an Integrated Nuclear Radiology Fellowship.

Academic radiology·2026
Same journal

Dual-layer Spectral Detector CT Quantitative Parameters for Predicting Tumor Budding Grade and Prognosis in Stage Ⅱ Colorectal Cancer.

Academic radiology·2026
Same journal

Promotion from Associate Professor to Full Professor Should Not Be Monolithic: A National Bibliometric Study by Radiology Subspecialty.

Academic radiology·2026
Same journal

Technological Lag of Digitization for Patient Image Transfer.

Academic radiology·2026
Same journal

Prognostic Value of Coronary Sinus Flow and Aortic Pressure Gradient Quantified by 4D Flow CMR in AMI.

Academic radiology·2026
See all related articles

Related Experiment Video

Updated: Jun 3, 2026

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
15:48

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

Calibrated measures for breast density estimation.

John J Heine1, Ke Cao, Dana E Rollison

  • 1H. Lee Moffitt Cancer Center & Research Institute, Cancer Prevention & Control Division, Tampa, FL 33612, USA. john.heine@moffitt.org

Academic Radiology
|March 5, 2011
PubMed
Summary
This summary is machine-generated.

Spatial variation in mammograms, a new measure of breast density, shows a stronger association with breast cancer risk than standard methods. This automated approach may improve risk assessment and inform future breast density measurement standards.

More Related 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

Non-Destructive Evaluation of Regional Cell Density Within Tumor Aggregates Following Drug Treatment
10:13

Non-Destructive Evaluation of Regional Cell Density Within Tumor Aggregates Following Drug Treatment

Published on: June 21, 2022

Related Experiment Videos

Last Updated: Jun 3, 2026

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
15:48

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

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

Non-Destructive Evaluation of Regional Cell Density Within Tumor Aggregates Following Drug Treatment
10:13

Non-Destructive Evaluation of Regional Cell Density Within Tumor Aggregates Following Drug Treatment

Published on: June 21, 2022

Area of Science:

  • Radiology and Medical Imaging
  • Oncology
  • Biostatistics

Background:

  • Breast density is a known breast cancer risk factor.
  • Spatial variation within mammograms may also indicate risk.
  • Current methods for measuring breast density include the standard percentage of breast density (PD).

Purpose of the Study:

  • To investigate spatial variation in calibrated mammograms as a breast cancer risk factor.
  • To compare this variation measure with other breast density metrics.
  • To explore the relationship between different breast density measures.

Main Methods:

  • A matched case-control analysis was performed using full field digital mammography (FFDM) images.
  • Spatial variation in calibrated FFDM images was assessed, normalized for acquisition techniques.
  • Three breast density measures were compared: calibrated average, calibrated variation, and standard PD.

Main Results:

  • The calibrated variation measure demonstrated a stronger association with breast cancer risk compared to standard PD and calibrated average measures.
  • Risk estimates (odds ratios) for the highest quartile of calibrated variation were 11.3, versus 6.5 for PD and 4.4 for calibrated average.
  • All three measures were highly correlated and showed an inverse relationship with breast area.

Conclusions:

  • The calibrated variation measure is a viable automated method for assessing breast density.
  • Different breast density measures capture distinct attributes of mammographic data.
  • This research may contribute to developing standardized methods for breast density measurement.