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Related Concept Videos

Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT01:25

Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT

3
Calcium-Scoring CT ScanA calcium-scoring CT scan, also known as coronary artery calcium (CAC) scan, detects calcium deposits in the coronary arteries. This test assesses the risk of coronary artery disease (CAD), which can lead to cardiovascular events such as angina, heart failure, and sudden cardiac arrest.A calcium-scoring CT scan is generally recommended for individuals at intermediate risk of CAD without symptoms. It includes:Men aged 40-75 and women aged 50-75: Especially those with a...
3
  1. Home
  3. Research Domains
  5. Biomedical And Clinical Sciences
  7. Oncology And Carcinogenesis
  9. Predictive And Prognostic Markers
  11. A Risk Prediction Stratification For Non-mass Breast Lesions, Combining Clinical Characteristics And Imaging Features On Ultrasound, Mammography, And Mri.
  1. Home
  3. Research Domains
  5. Biomedical And Clinical Sciences
  7. Oncology And Carcinogenesis
  9. Predictive And Prognostic Markers
  11. A Risk Prediction Stratification For Non-mass Breast Lesions, Combining Clinical Characteristics And Imaging Features On Ultrasound, Mammography, And Mri.

Related Experiment Video

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

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A risk prediction stratification for non-mass breast lesions, combining clinical characteristics and imaging features on ultrasound, mammography, and MRI.

YaMie Xie1, Xiaoxiao Zhang2

  • 1Department of CT/MRI, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China.

Frontiers in Oncology
|November 1, 2024

View abstract on PubMed

Summary
This summary is machine-generated.
Keywords:
MRIclinical factorsmammographynon-mass BI-RADS

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A new Breast Imaging Reporting and Data System (BI-RADS) model integrates clinical and imaging data to improve risk prediction for non-mass breast lesions, enhancing diagnostic accuracy.

Area of Science:

  • Radiology
  • Oncology
  • Medical Imaging Analysis

Background:

  • Increasing mergers in imaging centers necessitate standardized guidelines for non-mass breast lesions (NMLs).
  • Current diagnostic approaches for NMLs lack comprehensive risk stratification.
  • Accurate assessment of NMLs is crucial for timely and appropriate patient management.

Purpose of the Study:

  • To develop and validate a novel Breast Imaging Reporting and Data System (BI-RADS) risk prediction and stratification system for NMLs.
  • To integrate clinical characteristics with multi-modal imaging features (ultrasound, mammography, MRI) for improved diagnostic accuracy.
  • To assist clinicians in interpreting imaging reports for NMLs.

Main Methods:

  • A cohort of 350 patients with NMLs was randomly assigned to training (70%) and testing (30%) sets.
ultrasound
  • Independent predictors were identified using LASSO logistic regression.
  • A predictive risk model was constructed using a nomogram and validated on both datasets.

    • Main Results:

    • Key predictors included age, lesion characteristics, calcification patterns, vascularity, size, enhancement patterns, distribution, time-intensity curve (TIC), and apparent diffusion coefficient (ADC) values.
    • The predictive model achieved high areas under the curve (AUC): 0.873 (training) and 0.877 (testing).
    • The model demonstrated strong positive predictive values across BI-RADS categories, particularly for BI-RADS 4C (80.84%) and BI-RADS 5 (97.33%).

    Conclusions:

    • The developed BI-RADS stratification system enhances diagnostic precision for NMLs.
    • Integration of multi-modal imaging and clinical data improves risk categorization.
    • This novel system offers a valuable tool for clinicians managing patients with NMLs.