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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...
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Cardiovascular magnetic resonance imaging, or CMRI, is a non-invasive diagnostic test that employs a magnetic field and radiofrequency waves to create precise images of the heart and arteries. It provides comprehensive information about cardiac anatomy, function, perfusion, and tissue characterization without ionizing radiation.IndicationsCMRI diagnoses various heart conditions, including tissue damage from heart attacks, ischemic heart disease, myocarditis, aortic issues (tears, aneurysms,...
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Cardiac computed tomography (CT) scanning is an advanced cardiac imaging technique that utilizes CT technology, with or without intravenous (IV) contrast, to produce accurate cross-sectional virtual slices of specific areas of the heart, coronary circulation, and major blood vessels such as the aorta, pulmonary veins, and arteries. The computer processes these slices to generate three-dimensional images. Multidetector CT (MDCT) is a rapid form of CT scanning that captures multiple slices...
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The most common cardiovascular diagnostic test is an X-ray. It produces images of the heart, blood vessels, and adjacent structures.
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Updated: Dec 18, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Predicting Chronic Myocardial Ischemia Using CCTA-Based Radiomics Machine Learning Nomogram.

Zhen-Yu Shu1, Si-Jia Cui2, Yue-Qiao Zhang3

  • 1Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital of Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou, Zhejiang, China.

Journal of Nuclear Cardiology : Official Publication of the American Society of Nuclear Cardiology
|June 20, 2020
PubMed
Summary
This summary is machine-generated.

This study developed a radiomics nomogram using coronary computed tomography angiography (CCTA) to predict chronic myocardial ischemia (MIS). The tool accurately identifies high-risk coronary artery disease patients, offering a non-invasive diagnostic approach.

Keywords:
Radiomicscoronary CT angiographymachine learningmyocardial ischemianomogram

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Area of Science:

  • Cardiology
  • Radiology
  • Machine Learning
  • Medical Imaging Analysis

Background:

  • Coronary computed tomography angiography (CCTA) is a key non-invasive tool for assessing coronary artery disease (CAD).
  • Radiomics enables quantitative, non-invasive assessment of stenosis from CCTA images.
  • Current methods for diagnosing myocardial ischemia can be invasive or lack quantitative precision.

Purpose of the Study:

  • To develop and validate a CT-based radiomics machine learning model for predicting chronic myocardial ischemia (MIS).
  • To establish a non-invasive diagnostic tool for identifying patients with MIS using CCTA data.
  • To integrate radiomics features with clinical factors for improved MIS prediction.

Main Methods:

  • Retrospective analysis of CCTA and SPECT-myocardial perfusion imaging (MPI) data from 154 CAD patients.
  • Development of a radiomics signature using multivariate logistic regression after feature extraction from CCTA images.
  • Construction and validation of a radiomics nomogram incorporating radiomics signature and clinical factors, tested across training, test, and external validation sets.

Main Results:

  • The radiomics nomogram achieved high prediction accuracy for MIS: 0.839 (training), 0.832 (test), and 0.816 (validation).
  • The nomogram demonstrated superior diagnostic accuracy (0.824) compared to radiomics signature (0.736) and vascular stenosis (0.708).
  • Decision curve analysis confirmed the nomogram's clinical feasibility and utility in identifying high-risk MIS patients.

Conclusions:

  • A radiomics nomogram derived from CCTA images serves as an effective non-invasive tool for predicting MIS.
  • This approach aids in identifying high-risk patients with coronary artery disease who may have chronic myocardial ischemia.
  • The developed model offers a promising advancement in the non-invasive diagnosis and risk stratification of CAD patients.