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Table 7 Single and multiple linear inverse regression models to predict reference values as dependent variable (n = 164, P <0.0001 for all models and slope estimates)

From: A methodology to ensure and improve accuracy of Ki67 labelling index estimation by automated digital image analysis in breast cancer tissue

Variable

R-square

Intercept estimate

Intercept P

Slope estimate

Slope standardized estimate

Single regression models:

     

Ki67-DIA-2

0.90

-3.1183

0.0165

1.1878

0.9494

Ki67-DIA-2 < 40*

0.75

-4.3913

0.0085

1.1472

0.8688

Ki67-DIA-1

0.89

5.0453

<0.0001

1.1309

0.9447

Ki67-DIA-0

0.86

6.8232

<0.0001

1.2916

0.9278

Ki67-VE-median

0.86

8.3195

<0.0001

0.8572

0.9302

Multiple regression model

0.91

-0.3245

0.8096

  

Ki67-DIA-2

   

0.8068

0.6448

Ki67-VE-median

   

0.2985

0.3239

  1. *Ki67-DIA-2 < 40 - represents a regression model for Ki67-DIA-2 with only Ki67-Count less than 40% cases included in the analysis (n = 92). DIA, digital image analysis; VE, visual estimate.