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

Table 1 Radiomic features extracted from segmentation programs

MIM Software Inc (3D segmentation)	Cambridge Computed Imaging LTD (2D segmentation)
Integral total value	Entropy
Kurtosis	Kurtosis
Maximum HU	Mean HU
Mean HU	Mean positive pixels
Maximum mean HU ratio	Skewness
Median HU	Standard deviation
Median minimum HU ratio
Minimum HU
Minimum mean HU ratio
Skewness
Sphere value
Standard deviation
Standard deviation mean HU ratio
Total HU
Volume
Voxel count
Entropy

HU: Hounsfield units; 2D: Two-dimensional; 3D: Three-dimensional.

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.