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

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World Journal of Radiology

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1949-8470

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

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World J Radiol. Nov 28, 2023; 15(11): 304-314
Published online Nov 28, 2023. doi: 10.4329/wjr.v15.i11.304

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.

Citation: Saleh M, Virarkar M, Mahmoud HS, Wong VK, Gonzalez Baerga CI, Parikh M, Elsherif SB, Bhosale PR. Radiomics analysis with three-dimensional and two-dimensional segmentation to predict survival outcomes in pancreatic cancer. World J Radiol 2023; 15(11): 304-314
URL: https://www.wjgnet.com/1949-8470/full/v15/i11/304.htm
DOI: https://dx.doi.org/10.4329/wjr.v15.i11.304

Saleh M, Virarkar M, Mahmoud HS, Wong VK, Gonzalez Baerga CI, Parikh M, Elsherif SB, Bhosale PR. Radiomics analysis with three-dimensional and two-dimensional segmentation to predict survival outcomes in pancreatic cancer. World J Radiol 2023; 15(11): 304-314 [PMID: 38058604 DOI: 10.4329/wjr.v15.i11.304]

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