BACKGROUND: Texture analysis is a promising method of analyzing imaging data to potentially enhance diagnostic capability. This approach involves automated measurement of pixel intensity variation that may offer further insight into disease progression than do standard imaging techniques alone. We postulated that postoperative @entity7752 , a major source of morbidity and mortality, correlates with preoperative heterogeneous parenchymal enhancement that can be quantified with texture analysis of cross-sectional imaging. STUDY DESIGN: A retrospective case-matched study (waiver of informed consent and HIPAA authorization, approved by the Institutional Review Board) was performed comparing @entity1 who underwent major hepatic resection and developed @entity7752 (n  = 12) with a matched group of @entity1 with no postoperative @entity7752 (n  = 24) by procedure, remnant volume, and year of procedure. Texture analysis (with gray-level co-occurrence matrices) was used to quantify the heterogeneity of @entity409 on preoperative CT scans. Statistical significance was evaluated using Wilcoxon's signed rank and Pearson's chi-square tests. RESULTS: No statistically significant differences were found between study groups for preoperative @entity1 demographics and clinical characteristics, with the exception of sex (p < 0.05). Two texture features differed significantly between the groups: correlation (linear dependency of gray levels on neighboring pixels) and entropy ( @entity35628 of brightness variation) (p < 0.05). CONCLUSIONS: In this preliminary study, the texture of @entity409 on preoperative CT was significantly more varied, less symmetric, and less homogeneous in @entity1 with postoperative @entity7752 . Therefore, texture analysis has the potential to provide an additional means of preoperative risk stratification.

Texture Analysis of Preoperative CT Images for Prediction of Postoperative XXXX : A Preliminary Study.

@entity1 :: ('9606', 'Species') :: ['patients', 'patient'] @entity35628 :: ('MESH:C562757', 'Disease') :: ['randomness'] @entity409 :: ('MESH:D008107', 'Disease') :: ['liver parenchyma'] @entity7752 :: ('MESH:D048550', 'Disease') :: ['liver insufficiency']
@entity7752 :: (MESH:D048550,Disease) :: ['Hepatic Insufficiency']
