Dosimetric evaluation of tandem-based cervical high-dose-rate brachytherapy treatment planning using American Brachytherapy Society 2011 recommendations

2016 ◽  
Vol 15 (3) ◽  
pp. 283-289 ◽  
Author(s):  
Manish K. Goyal ◽  
T. S. Kehwar ◽  
Jayanand Manjhi ◽  
Jerry L. Barker ◽  
Bret H. Heintz ◽  
...  
Keyword(s):  
Linear Regression ◽  
Dose Rate ◽  
Regression Line ◽  
High Dose Rate ◽  
Least Square ◽  
High Dose ◽  
Hdr Brachytherapy ◽  
Linear Regression Line ◽  
Significant Difference ◽  
Best Fit

AbstractPurposeThis study evaluated dosimetric parameters for cervical high-dose-rate (HDR) brachytherapy treatment using varying dose prescription methods.MethodsThis study includes 125 tandem-based cervical HDR brachytherapy treatment plans of 25 patients who received HDR brachytherapy. Delineation of high-risk clinical target volumes (HR-CTVs) and organ at risk were done on original computed tomographic images. The dose prescription point was defined as per International Commission in Radiation Units and Measurements Report Number 38 (ICRU-38), also redefined using American Brachytherapy Society (ABS) 2011 criteria. The coverage index (V100) for each HR-CTV was calculated using dose volume histogram parameters. A plot between HR-CTV and V100was plotted using the best-fit linear regression line (least-square fit analysis).ResultsMean prescribed dose to ICRU-38 Point A was 590·47±28·65 cGy, and to ABS Point A was 593·35±30·42 cGy. There was no statistically significant difference between planned ICRU-38 and calculated ABS Point A doses (p=0·23). The plot between HR-CTV and V100is well defined by the best-fit linear regression line with a correlation coefficient of 0·9519.ConclusionFor cervical HDR brachytherapy, dose prescription to an arbitrarily defined point (e.g., Point A) does not provide consistent coverage of HR-CTV. The difference in coverage between two dose prescription approaches increases with increasing CTV. Our ongoing work evaluates the dosimetric consequences of volumetric dose prescription approaches for these patients.

scholarly journals Foot Length as a Predictor of Weight in Children up to Five Years

2019 ◽  
Vol 31 (2) ◽  
pp. 39-44
Author(s):  
Md Shameem ◽  
Nazneen Akhter Banu ◽  
ANM Nurul Haque Bhuiyan ◽  
Ariful Islam
Keyword(s):  
Body Weight ◽  
Linear Regression ◽  
Regression Line ◽  
The Body ◽  
Critically Ill Children ◽  
Linear Regression Line ◽  
Foot Length ◽  
Measured Weight ◽  
Variable Weight

Weight measurement is essential for the management of pediatric patients to calculate the dose of the drugs. But it is not possible to move the child to a weighing scale for determination of body weight when the child is in a critical condition. The purpose of this study was to check if foot length correlates with child’s body weight in our situation and to devise a formula for prediction of weight based on foot– length observed. This Cross-sectional study was carried out in the Department of Pediatrics, Sir Salimullah Medical College, Mitford hospital, Dhaka over a period of 12 months between January 2008 and December 2008. A total of 300 children, between 0 day to five years, meeting the predefined eligibility criteria were included in the study. Using the available data, simple linear regression analysis was performed between the dependent variable weight and independent variable foot length. The estimated linear regression line was: Predicted weight (kg) = a+ [b× foot length]. Data were analyzed using correlation coefficient (r) between foot length and children’s weight. In this study correlation between foot length and weight (r) was 0.92(P<0.001) indicating a perfect linear relationship between them. In the present study determination of correlation (r2) was 0.85 meaning that 85% of the variability in weight might be explained by variation in foot length. The estimated linear regression line was: Predicted weight (kg) = - 4.64 + [1.12 X foot length], where- 4.64 was the intercept and 1.12 was the slope of the regression line. Comparison between measured weight and predicted weight revealed that94% of variation between measured weight and predicted weight was within ±2kg. More than half of the cases (58.3%) the above-mentioned variations were within ±1kg.  This study concluded, there was a strong correlation between foot length and weight in children up to five years. The body weight in children from 0 days up to the age of 5 years can be predicted from foot length. Prediction of weight simply by foot-length measurement could be a great help to the health care provider including doctors and health workers for drug dose calculation in critically ill children. TAJ 2018; 31(2): 39-44

THE VARIANCES OF THE MEANS AND THE VARIANCE OF THE SLOPE OF THE LINE OF RELATION OF A LINEAR, COMPOSITE, BIVARIATE DISTRIBUTION

1942 ◽  
Vol 20a (1) ◽  
pp. 6-9
Author(s):  
F. Charnley
Keyword(s):  
Linear Regression ◽  
Correlation Coefficient ◽  
Regression Line ◽  
Bivariate Distribution ◽  
Linear Regression Line ◽  
Linear Composite ◽  
Composite Distribution

The variances of the means and the variance of the slope of the line of relation of a linear, composite, bivariate distribution, in which the variances and correlation coefficient of the component populations remain constant, are analogous to the corresponding variances of a linear regression line. The variances of the means are respectively, [Formula: see text], and the variance of the slope of the line of relation is, [Formula: see text] where [Formula: see text] are the variances of the component populations, [Formula: see text] is the vertical variance of the composite distribution around the line of relation, N is the total number of measures, and [Formula: see text] is the variance of the composite distribution around the Y-axis.

An Acoustic Analysis of the Development of CV Coarticulation

1999 ◽  
Vol 42 (5) ◽  
pp. 1080-1096 ◽  
Author(s):  
Harvey M. Sussman ◽  
Celeste Duder ◽  
Eileen Dalston ◽  
Antonina Cacciatore
Keyword(s):  
Linear Regression ◽  
Acoustic Analysis ◽  
Regression Line ◽  
Stop Consonant ◽  
Female Child ◽  
Natural Speech ◽  
Single Female ◽  
Linear Regression Line ◽  
Locus Equation ◽  
Numerical Index

This study analyzed stop consonant-vowel productions from babbling to meaningful speech in a single female child spanning the period from age 7 months to age 40 months. A total of 7,888 utterances (3,103 [bV], 3,236 [dV], and 1,549 [gV]) were analyzed to obtain frequencies at F2 onset and F2 at vocalic center for each utterance. A linear regression line (“locus equation”) was fit to the cluster of F2 coordinates per stop place category produced during each month. The slope of the regression lines provided a numerical index of vowel-induced coarticulation on consonant productions. Labial, alveolar, and velar CV productions followed distinct articulatory paths toward adult-like norms of coarticulation. Inferences about the gradual emergence of segmental independence of the consonant and vowel in the three stop place environments were made from locus equation scatterplots and mean F2 onset and F2 midvowel frequencies obtained across babbling, early words, and natural speech.

scholarly journals Linear Regression Line based Partial Face Recognition

2015 ◽  
Vol 130 (11) ◽  
pp. 1-5
Author(s):  
Naveena M. ◽  
G. Hemantha ◽  
P. Nagabhushan
Keyword(s):  
Face Recognition ◽  
Linear Regression ◽  
Regression Line ◽  
Linear Regression Line

scholarly journals La distancia más corta. El método de los mínimos cuadrados.

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Manuel Molina
Keyword(s):  
Linear Regression ◽  
Least Squares ◽  
Regression Line ◽  
Regression Method ◽  
Regression Coefficients ◽  
Least Squares Regression ◽  
Linear Regression Line ◽  
Shortest Distance ◽  
The Way

El método de los mínimos cuadrados se utiliza para calcular la recta de regresión lineal que minimiza los residuos, esto es, las diferencias entre los valores reales y los estimados por la recta. Se revisa su fundamento y la forma de calcular los coeficientes de regresión con este método. ABSTRACT The shortest distance. Least squares regression. Least squares regression method is used to calculate the linear regression line that minimizes residuals, that is, the differences among real values and those estimated by the line. Its basis and the way of calculating the regression coefficients with this method are reviewed.

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