Radiomics on gadoxetic acid-enhanced magnetic resonance imaging for prediction of postoperative early and late recurrence of single hepatocellular carcinoma

Sungwon Kim, Jaeseung Shin, Do Young Kim, Gi Hong Choi, Myeong Jin Kim, Jin Young Choi

Research output: Contribution to journalArticle

17 Citations (Scopus)

Abstract

Purpose: To evaluate the usefulness of the radiomic model in predicting early (≤2 years) and late (>2 years) recurrence after curative resection in cases involving a single hepatocellular carcinoma (HCC) 2-5 cm in diameter using preoperative gadoxetic acid-enhanced magnetic resonance imaging (MRI), in comparison with the clinicopathologic model. Experimental Design: This retrospective study included 167 patients with surgically resected and pathologically confirmed single HCC 2-5 cm in diameter (n = 167, training set:validation set = 128:39) who underwent preoperative gadoxetic acid-enhanced MRI between January 2010 and December 2015. A radiomic model, a clinicopathologic model, and a combined clinicopathologic-radiomic (CCR) model were built using a random survival forest to predict disease-free survival (DFS) in the following conditions: early DFS versus late DFS, dynamic phases, and the peritumoral area included in the segmentation. Results: The radiomic model showed a prognostic performance comparable with the clinicopathologic model only with 3-mm peritumoral border extension [c-index difference (radiomic-clinicopathologic),-0.021, P = 0.758]. The CCR model with the 3-mm border extension showed the highest c-index value but no statistically significant improvement over the clinicopathologic model [CCR, 0.716 (0.627-0.799); clinicopathologic model, 0.696 (0.557-0.799)]. Conclusions: The prognostic value of the preoperative radiomic model with 3-mm border extension showed comparable performance with that of the postoperative clinicopathologic model for predicting DFS of early recurrence of HCC using gadoxetic acid-enhanced MRI. This suggests the importance of including peritumoral changes in the radiomic analysis of HCC.

Original languageEnglish
Pages (from-to)3847-3855
Number of pages9
JournalClinical Cancer Research
Volume25
Issue number13
DOIs
Publication statusPublished - 2019 Jan 1

All Science Journal Classification (ASJC) codes

  • Oncology
  • Cancer Research

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