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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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Quantitative analysis is a technique for measuring the amount of specific constituents in a sample. When the sample's composition is unknown, qualitative analysis is performed first to identify its components, which ensures that the correct substances are measured during the quantitative phase.
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A z score (or standardized value) is measured in units of the standard deviation. It tells you how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a zero z score. It is important to note that the mean of the z scores is zero, and the standard deviation is one.
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Introduction to z Scores01:05

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A z score (or standardized value) is measured in units of the standard deviation. It indicates how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a zero z score. It is important to note that the mean of the z scores is zero, and the standard deviation is one.
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z scores are the standardized values obtained after converting a normal distribution into a standard normal distribution. A z score is measured in units of the standard deviation. The z score tells you how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a z score of...
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Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
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Precision analysis of a quantitative CT liver surface nodularity score.

Andrew Smith1,2,3, Elliot Varney4, Kevin Zand4

  • 1Department of Radiology, University of Mississippi Medical Center, Jackson, MS, USA. andrewdennissmith@uabmc.edu.

Abdominal Radiology (New York)
|April 28, 2018
PubMed
Summary

A new software-based liver surface nodularity (LSN) score derived from CT scans demonstrates excellent precision. This quantitative imaging biomarker shows high repeatability, reproducibility, and agreement among observers for assessing liver disease.

Keywords:
Liver surface nodularityMetrologyPrecision analysisQuantitative CT

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Area of Science:

  • Radiology
  • Medical Imaging
  • Quantitative Biomarkers

Background:

  • Liver surface nodularity is a key indicator in chronic liver disease.
  • Accurate assessment of liver surface nodularity (LSN) is crucial for disease staging.
  • Current visual assessment methods can be subjective and lack precision.

Purpose of the Study:

  • To evaluate the precision of a novel software-based liver surface nodularity (LSN) score.
  • To assess the repeatability, reproducibility, and inter-observer agreement of the software-based LSN scoring method.
  • To compare the software-based LSN score with traditional visual assessment.

Main Methods:

  • An anthropomorphic CT phantom with simulated liver and fat components was used.
  • Phantom scans were performed on a single scanner with varied parameters and on 22 different scanners.
  • Abdominal CT images from 68 patients with chronic liver disease were analyzed by 12 readers.

Main Results:

  • The software-based LSN score exhibited excellent repeatability (ICC: 0.79-0.99) and reproducibility (ICC: 0.94-0.97) across different scanners and parameters.
  • Inter-observer agreement for the software-based method was excellent (ICC: 0.84), surpassing the visual-based method (ICC: 0.61).
  • Test-retest repeatability for the software-based LSN score was also excellent (ICC: 0.82).

Conclusions:

  • The software-based LSN score is a precise and reliable quantitative CT imaging biomarker.
  • This method offers excellent repeatability, reproducibility, and inter-observer agreement.
  • The software-based LSN score represents a significant advancement for objective liver disease assessment.