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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.