Radiomics analysis with three-dimensional and two-dimensional segmentation to predict survival outcomes in pancreatic cancer

Table 2 Univariate Cox proportional hazards model for overall survival (hazard ratio < 1 indicates better prognosis)

SSF	Variable	Hazard ratio	95% CI	P value
0	Kurtosis	1.176	1.017-1.361	0.029
0	Mean HU	0.963	0.935-0.993	0.014
0	MPP	0.962	0.933-0.992	0.014
0	Skewness	3.067	1.345-6.993	0.008
2	Entropy	0.042	0.003-0.636	0.022
2	Mean HU	0.975	0.961-0.989	0.001
3	Mean HU	0.980	0.968-0.991	< 0.001
4	Kurtosis	1.071	1.000-1.146	0.048
4	Mean HU	0.985	0.974-0.995	0.003
4	MPP	1.012	1.000-1.023	0.046
4	SD	1.006	1.001-1.011	0.016
5	MPP	1.009	1.002-1.017	0.012
5	SD	1.006	1.001-1.011	0.010
6	MPP	1.007	1.002-1.013	0.008
6	SD	1.006	1.001-1.011	0.011

Without a spatial scale filter (SSF), patients with high kurtosis and skewness had worse overall survival. Patients with elevated mean Hounsfield units and mean positive pixels had better overall survival. Several other significant findings were found when an SSF was applied (2 through 6). HU: Hounsfield units; SSF: Spatial scale filter; MPP: Mean positive pixels; 95%CI: 95% confidence interval.