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Related Concept Videos

Correlations02:20

Correlations

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Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
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Correlation and Causation01:27

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Statistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. An indirect relationship of the variables signifies a correlation, while a direct relationship shows causation. If it is determined that no connection exists between the variables, then the correlation is a coincidence.
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Correlation01:09

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In statistics, two variables are said to be correlated if the values of one variable are associated with the other variable. Depending on the relationship between two variables, correlation can be of three types– positive correlation, negative correlation, and zero correlation.
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
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Correlation and Regression00:53

Correlation and Regression

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In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
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Coefficient of Correlation01:12

Coefficient of Correlation

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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.
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
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Spearman's Rank Correlation Test01:20

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Spearman's rank correlation test, also known as Spearman's rho, is a nonparametric method for assessing the strength and direction of association between two variables. This test is particularly valuable when the data distribution is unknown or when the assumption of normality does not hold. Named after the English psychologist and statistician Dr. Charles Edward Spearman, it serves as the nonparametric counterpart to Pearson's correlation coefficient.
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Author Spotlight: Exploring Advanced Therapeutic Targets in Osteosarcoma Through Spatial Transcriptomics
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Tissue spatial correlation as cancer marker.

Masanori Takabayashi1,2, Hassaan Majeed2, Andre Kajdacsy-Balla3

  • 1Kyushu Institute of Technology, Department of Systems Design and Informatics, Iizuka, Fukuoka, Japan.

Journal of Biomedical Optics
|January 23, 2019
PubMed
Summary
This summary is machine-generated.

We developed a new intrinsic cancer marker using quantitative phase imaging. This method analyzes spatial autocorrelation length in tissue slides for faster, more accurate cancer diagnosis in histopathology.

Keywords:
biopsy slide diagnosisbreast cancerquantitative phase imaging

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Area of Science:

  • Histopathology
  • Biomedical Optics
  • Cancer Research

Background:

  • Cancer diagnosis relies on identifying cellular morphological alterations.
  • Current histopathology methods can be time-consuming.
  • Quantitative phase imaging offers high sensitivity to nanoscale changes.

Purpose of the Study:

  • To propose a novel intrinsic cancer marker for fixed tissue biopsy slides.
  • To develop a time-efficient method for calculating spatial autocorrelation length maps.
  • To evaluate the marker's diagnostic value for breast tissues.

Main Methods:

  • Utilizing quantitative phase imaging data from spatial light interference microscopy.
  • Calculating local spatial autocorrelation length from tissue phase images.
  • Implementing a novel, time-efficient method for correlation map computation.

Main Results:

  • Spatial autocorrelation length is sensitive to nanoscale cellular alterations indicative of carcinogenesis.
  • The proposed method enables efficient computation of correlation maps.
  • The marker demonstrated diagnostic value in distinguishing benign from malignant breast tissues.

Conclusions:

  • Spatial autocorrelation length serves as a potential intrinsic cancer marker in histopathology.
  • The developed method offers a faster alternative for generating correlation maps.
  • This approach holds promise for improved cancer diagnosis using quantitative phase imaging.