Skip to main content

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