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. Author manuscript; available in PMC: 2023 Sep 1.
Published in final edited form as: Acad Radiol. 2021 Dec 18;29(9):1378–1386. doi: 10.1016/j.acra.2021.11.020

Table 3.

The list of features used to generate regression models for characterizing the behavior of histological tumor markers as reflecting biological changes in tumor tissue.

T1w T2w T1w 1 + T2w 2
H&E SZ LGL Emphasis (GLSZM) LGL ZE (GLSZM) SZ LGL Emphasis (GLSZM1)
SZ HGL Emphasis (GLSZM) Kurtosis (FOS) SZ emphasis (GLSZM1)
Kurtosis (FOS) SR LGL emphasis (GLRLM) SZ HGL Emphasis (GLSZM1)
CD31 HGL ZE (GLSZM) LGL ZE (GLSZM) SZ LGL emphasis (GLSZM1)
SZ HGL Emphasis (GLSZM) Kurtosis (FOS) HGL ZE (GLSZM1)
Autocorrelation (GLCM) SR LGL emphasis (GLRLM) Autocorrelation (GLCM1)
Correlation (GLCM) ZS nonuniformity (GLSZM) Correlation (GLCM1)
TUNEL HGL ZE (GLSZM) Busyness (NGTDM) Autocorrelation (GLCM1)
SZ LGL Emphasis (GLSZM) ZS variance (GLSZM) Kurtosis (FOS2)
Autocorrelation (GLCM) SR LGL emphasis (GLRLM) SZ LGL emphasis (GLSZM1)

Features: Gray-level (GL), high gray-level (HGL), low gray-level (LGL), Long run (LR), run emphasis (RE), run percentage (RP), Short-run (SR), small zone (SZ), standard deviation (SD), zone emphasis (ZE), zone percentage (ZP).

Classes: First order statistics (FOS), gray-level co-occurrence matrix (GLCM), gray-level run-length matrix (GLRLM), gray-level size-zone matrix (GLSZM), neighborhood grey tone difference matrix (NGTDM).