Texture Analysis Based on Preoperative Magnetic Resonance Imaging (MRI) and Conventional MRI Features for Predicting the Early Recurrence of Single Hepatocellular Carcinoma after Hepatectomy.

Abstract:

RATIONALE AND OBJECTIVES:To investigate the value of texture analysis and conventional magnetic resonance imaging (MRI) features for predicting the early recurrence (ER) of single hepatocellular carcinoma (HCC) after hepatectomy. MATERIALS AND METHODS:A total of 100 HCC patients were first divided into group A (tumor diameter ≤3 cm) and group B (tumor diameter >3 cm) and then classified into two subgroups with ER or nonearly recurrence. Textural parameters (skewness, kurtosis, uniformity, energy, entropy, and correlation) based on MR images and conventional MRI features were compared between the ER and nonearly recurrence subgroups. Predictive factors for ER were further assessed with multivariate logistic regression analysis. Receiver operating characteristic curve was performed to assess the predictive power. RESULTS:There were 53 patients in group A and 47 patients in group B. On arterial phase analysis, tumors with ER displayed significantly lower uniformity and higher entropy in group A, and higher skewness and entropy in group B. On portal venous phase analysis, tumors with ER had significantly lower kurtosis and energy in group A, and higher entropy in group B. Irregular margin in groups A and B, and arterial peritumoral enhancement and capsule presence in group B were associated with ER. In multivariate logistic regression analysis, uniformity and entropy based on arterial phase images and irregular margin in group A, and skewness and entropy based on arterial phase images and arterial peritumoral enhancement in group B were independent predictors for ER. Entropy displayed higher predictive power for ER. CONCLUSION:Texture analysis based on preoperative MRI are potential quantitative predictors of ER in HCC patients after hepatectomy, and may provide more information for preoperative treatment decision-making and follow up.

journal_name

Acad Radiol

journal_title

Academic radiology

authors

Zhang J,Liu X,Zhang H,He X,Liu Y,Zhou J,Guo D

doi

10.1016/j.acra.2018.10.011

subject

Has Abstract

pub_date

2019-09-01 00:00:00

pages

1164-1173

issue

9

eissn

1076-6332

issn

1878-4046

pii

S1076-6332(18)30475-6

journal_volume

26

pub_type

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