Table 4.

Results of machine learning models for predicting local lung fibrosis

	Coxnet				Gradient boost
Features	number of features	CCI train-set	CCI cross-valid	CCI test-set	Number of features	CCI train-set	CCI cross-valid	CCI test-set
Clinical/dosimetric	3§	0.71 p < 0.005	0.68 ± 0.11	0.65 p = 0.04*	3§	0.73 p < 0.005	0.64 ± 0.12	0.62 n.s
Radiomics	10	0.79 p < 0.005	0.64 ± 0.13	0.58 n.s	2†	0.75 p < 0.005	0.72 ± 0.11	0.59 p = 0.02*
Combined	4 + 7	0.74 p < 0.005	0.67 ± 0.12	0.66 p = 0.03*	0 + 2†	0.72 p < 0.005	0.72 ± 0.11	0.59 p = 0.02*

CCI concordance index, means ± standard deviation are shown, p-values: significance level of the model risk score in univariate Cox regression analysis

^§Age/ GTV_MeanDose/LungD_1ml

^†wavelet_HLH_glcm_MCC/wavelet_HLL_glcm_MCC (= GrayLevelCo-occurrence matrix maximal correlation coefficient)