Skip to main content

Table 3 Discrimination performance of radiomic features built by using the SVM, Decision tree, Random forest, X-Gradient boost and multivariable Logistic regression for the training and validation cohorts

From: CT radiomic features for predicting resectability of oesophageal squamous cell carcinoma as given by feature analysis: a case control study

Model

Discrimination

Accuracy

AUC ± SD

F-1score

The training cohort

 SVM

0.80

0.86 ± 0.03

0.81

 Decision tree

0.69

0.73 ± 0.06

0.71

 Random forest

0.73

0.80 ± 0.07

0.75

 X-Gradient boost

0.78

0.87 ± 0.06

0.81

 MLR

0.87

0.92 ± 0.04

0.93

The validation cohort

 SVM

0.79

0.82 ± 0.03

0.80

 Decision tree

0.69

0.66 ± 0.03

0.70

 Random forest

0.67

0.67 ± 0.03

0.68

 X-Gradient boost

0.79

0.84 ± 0.03

0.79

 MLR

0.86

0.87 ± 0.02

0.86

  1. SD standard deviation, SVM support vector machine, MLR multivariable logistic regression, AUC receiver operating characteristic curve
Back to article page

Cancer Imaging

ISSN: 1470-7330

Contact us