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Related Concept Videos

Apparent Weight01:09

Apparent Weight

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True weight is the measure of the gravitational force acting on an object. However, if the object accelerates, its measured weight is different from its true weight. Similar observations can be made when the object is submerged in water. An object's weight in water is its apparent weight, which is equal to the difference between its true weight and the buoyant forces.
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Diffusion is the passive movement of substances down their concentration gradients—requiring no expenditure of cellular energy. Substances, such as molecules or ions, diffuse from an area of high concentration to an area of low concentration in the cytosol or across membranes. Eventually, the concentration will even out, with the substance moving randomly but causing no net change in concentration. Such a state is called dynamic equilibrium, which is essential for maintaining overall...
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The coefficient of variation measures the dispersion of the data points or distribution around the mean. Using the coefficient of variation, we can compare two data series with drastically different means or different units of measurement. The coefficient of variation for a sample and a population is expressed as a percentage of the ratio of standard deviation to the mean.
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The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
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The confidence coefficient is also known as the confidence level or degree of confidence. It is the percent expression for the probability, 1-α, that the confidence interval contains the true population parameter assuming that the confidence interval is obtained after sufficient unbiased sampling; for example, if the CL = 90%, then in 90 out of 100 samples the interval estimate will enclose the true population parameter. Here α is the area under the curve, distributed equally under...
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Since all objects on the Earth's surface move through a circle every 24 hours, there must be a net centripetal force on each object, directed towards the center of that circle. The points of the north and south poles are the only exception to this rule.
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Diagnostic performance of apparent diffusion coefficient parameters for glioma grading.

Qun Wang1, JiaShu Zhang1, Xinghua Xu1

  • 1Department of Neurosurgery, Chinese PLA General Hospital, 28 Fuxing Road, Haidian District, Beijing, 100853, China.

Journal of Neuro-Oncology
|March 26, 2018
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Minimum apparent diffusion coefficient (ADC) values from diffusion tensor imaging effectively differentiate grade II and III gliomas. A predictive equation using ADC also shows high diagnostic performance for glioma grading.

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Area of Science:

  • Neuroimaging
  • Radiology
  • Oncology

Background:

  • Gliomas are primary brain tumors with distinct grades (II and III) that influence prognosis and treatment.
  • Accurate differentiation between glioma grades is crucial for appropriate clinical management.
  • Diffusion tensor imaging (DTI) offers quantitative metrics that may aid in distinguishing tumor subtypes.

Purpose of the Study:

  • To evaluate the diagnostic performance of fractional anisotropy (FA) and apparent diffusion coefficient (ADC) parameters from DTI in differentiating grade II and III gliomas.
  • To assess the utility of a predictive diagnostic equation derived from these parameters.

Main Methods:

  • Retrospective review of 60 patients with suspected gliomas who underwent ADC image-guided stereotactic biopsy.
  • Quantitative measurement of FA and ADC values.
  • Receiver operating characteristic (ROC) curve analysis to determine sensitivity, specificity, accuracy, and area under the curve (AUC).

Main Results:

  • Significant differences in minimum ADC values were observed between grade III and II gliomas.
  • Minimum ADC values demonstrated a high diagnostic performance (AUC = 0.87) for differentiating glioma grades at an optimal cut-off.
  • The predictive diagnostic equation showed superior sensitivity (90.5%) and NPV (95.1%) compared to minimum ADC alone.

Conclusions:

  • Minimum ADC values derived from DTI are valuable for differentiating grade II and III gliomas.
  • A predictive diagnostic equation incorporating ADC parameters may further enhance diagnostic accuracy in glioma grading.
  • These quantitative imaging biomarkers can assist clinicians in treatment planning for glioma patients.