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. Author manuscript; available in PMC: 2019 Jun 1.
Published in final edited form as: J Vasc Interv Radiol. 2018 Mar 14;29(6):850–857.e1. doi: 10.1016/j.jvir.2018.01.769

Table 2.

Feature Selection Criteria.

Binary feature Class variance > 20% Univariate p-value
Clinical / Laboratory
 Albumin > 3.5 g/dL Yes 0.68
 Alcoholic liver disease Yes 0.96
 Ascites present 0.31
 Bilirubin > 1.5 mg/dL 0.83
 Encephalopathy present 0.47
 Hepatitis B 0.59
 Hepatitis C Yes 0.58
 Transplant recipient 0.97
 Distant (extra-hepatic) metastases present 0.30
 Lymph node metastases present 0.59
Cirrhosis present Yes 0.34
Treatment
Treated with Lipiodol Yes 0.51
Sorafenib treatment Yes 0.51
Demographic
 Male 0.28
 White ethnicity Yes 0.74
Imaging
 Portal vein invasion 0.47
 Pre-TACE enhancing tumor volume > 598 cm3 0.38
 Pre-TACE liver volume > 1,990 cm3 Yes 0.57
 Pre-TACE mean liver signal intensity > 15.4 0.97
Pre-TACE tumor signal intensity > 27.0 Yes 0.19
Pre-TACE number of tumors > 2 Yes 0.51
 Pre-TACE standard deviation of liver signal intensity > 9.4 Yes 0.83
 Pre-TACE standard deviation of tumor signal intensity > 10.6 0.90
 Pre-TACE tumor volume > 1,070 cm^3 0.38
 Tumor diameter > 3 cm Yes 0.70

Total features considered and final features selected to train machine learning model after applying selection criteria: a) > 20% variance and b) Univariate p-value < 0.55 when associated with treatment response. Five features (italicized) satisfied both criteria.