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Table 2 Prognosis prediction performance when robust and/or non-redundant features are used in the analysis for All data

From: Impact of feature selection methods and subgroup factors on prognostic analysis with CT-based radiomics in non-small cell lung cancer patients

Constructed model

Total number of features

Training dataset

Test dataset

C-index

Hazard ratio (95%CI)

C-index

Hazard ratio (95%CI)

Radiomic model

     

FS1

23

0.63*

1.55 (1.30–1.85)

0.60

0.95 (0.82–1.10)

FS2

28

0.64*

3.96 (2.43–6.45)

0.61*

1.87 (0.88–3.99)

FS3

9

0.62*

1.84 (0.17–2.19)

0.60*

1.06 (0.01–2.08)

Combined model

     

FS1 + clinical

31

0.64*

2.22 (0.58–3.22)

0.62*

1.20 (0.45–2.87)

FS2 + clinical

36

0.65*

4.75 (2.99–7.56)

0.63*

2.24 (1.13–4.36)

FS3 + clinical

17

0.64*

2.62 (0.90–3.96)

0.62

0.94 (0.19–2.32)

  1. FS Feature Selection, CI confidence interval
  2. FS1: a method to select only robust features using test–retest and multiple segmentation
  3. FS2: a method of excluding one of the correlated features from the analysis as redundant based on the correlation coefficients calculated by Pearson's correlation analysis for all features
  4. FS3: a method that combined FS1 and FS2
  5. *P value < 0.05
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Radiation Oncology

ISSN: 1748-717X

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