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Related Concept Videos

Imaging Studies for Cardiovascular System V: CT01:28

Imaging Studies for Cardiovascular System V: CT

674
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...
674

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Computer-Assisted Algorithm for Quantification of Fibrosis by Native Cardiac CT: A Pilot Study.

Diana Gonciar1, Alexandru-George Berciu2, Eva-Henrietta Dulf2,3

  • 12nd Department of Internal Medicine, Iuliu HaČ›ieganu University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania.

Journal of Clinical Medicine
|August 29, 2024
PubMed
Summary
This summary is machine-generated.

An automated algorithm using cardiac CT effectively identifies myocardial fibrosis in breast cancer patients, showing strong correlations with CMR markers. This novel approach offers a promising new imaging biomarker for fibrosis detection.

Keywords:
HER2+ Breast cancerartificial intelligencecardiac imagingmyocardial fibrosis

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Area of Science:

  • Cardiovascular Imaging
  • Artificial Intelligence in Medicine
  • Oncology Imaging

Background:

  • Advancements in AI offer potential for enhanced diagnosis and prognosis in cardiac imaging.
  • Novel imaging markers are sought for conditions like myocardial fibrosis.
  • Cardiac computed tomography (CT) is explored for automated fibrosis detection.

Purpose of the Study:

  • To develop and evaluate an automated, computer-aided algorithm for identifying myocardial fibrosis using native cardiac CT.
  • To compare the performance of this algorithm against cardiac magnetic resonance (CMR) markers of fibrosis.
  • To assess the algorithm's utility in a cohort of breast cancer patients with cardiac dysfunction.

Main Methods:

  • An automated algorithm was developed to detect myocardial fibrosis in cardiac CT images by analyzing pixel intensity (60-90 HU).
  • The study included early HER2+ breast cancer patients with left ventricular (LV) dysfunction and CMR-confirmed fibrosis.
  • Algorithm-derived fibrosis percentages were compared with CMR parameters like ECV fraction, native T1, LV-LGE, and global longitudinal strain (GLS).

Main Results:

  • The algorithm demonstrated high positive correlations with ECV fraction (r=0.59) and native T1 (r=0.6).
  • Very high positive correlations were found with LV-LGE (r=0.77) and LV-LGE/LV mass percentage (r=0.81).
  • A very high negative correlation was observed with GLS (r=-0.77), and an intraclass correlation coefficient of 1 (p<0.001) indicated excellent agreement.

Conclusions:

  • This pilot study introduces a novel, promising imaging marker for myocardial fibrosis.
  • The automated algorithm utilizing native cardiac CT shows significant potential for fibrosis detection.
  • This AI-driven approach may enhance the diagnosis and prognosis of cardiac conditions.