Computed Tomography Image Texture: A Noninvasive Prognostic Marker of Hepatic Recurrence After Hepatectomy for Metastatic Colorectal Cancer.

Journal Article (Journal Article)

BACKGROUND: Recurrence after resection of colorectal liver metastases (CRLMs) occurs in up to 75% of patients. Preoperative prediction of hepatic recurrence may inform therapeutic strategies at the time of initial resection. Texture analysis (TA) is an established technique that quantifies pixel intensity variations (heterogeneity) on cross-sectional imaging. We hypothesized that tumoral and parenchymal changes that are predictive of overall survival (OS) and recurrence in the future liver remnant (FLR) can be detected using TA on preoperative computed tomography (CT) images. METHODS: Patients who underwent resection for CRLM between 2003 and 2007 with appropriate preoperative CT scans were included (n = 198) in this retrospective study. Texture features extracted from the tumor and FLR, and clinicopathologic variables, were incorporated into a multivariable survival model. RESULTS: Quantitative imaging features of the FLR were an independent predictor of both OS and hepatic disease-free survival (HDFS). Tumor texture showed significant association with OS. TA of the FLR allowed patient stratification into two groups, with significantly different risks of hepatic recurrence (hazard ratio 2.09, 95% confidence interval 1.33-3.28; p = 0.001). Patients with homogeneous parenchyma had approximately twice the risk of hepatic recurrence (41 vs. 20%). CONCLUSION: TA of the tumor and FLR are independently associated with OS, and TA of the FLR is independently associated with HDFS. Patients with homogeneous parenchyma had a significantly higher risk of hepatic recurrence. Preoperative TA of the liver represents a potential biomarker to identify patients at risk of liver recurrence after resection for CRLM.

Full Text

Duke Authors

Cited Authors

  • Simpson, AL; Doussot, A; Creasy, JM; Adams, LB; Allen, PJ; DeMatteo, RP; Gönen, M; Kemeny, NE; Kingham, TP; Shia, J; Jarnagin, WR; Do, RKG; D'Angelica, MI

Published Date

  • September 2017

Published In

Volume / Issue

  • 24 / 9

Start / End Page

  • 2482 - 2490

PubMed ID

  • 28560599

Pubmed Central ID

  • PMC5553273

Electronic International Standard Serial Number (EISSN)

  • 1534-4681

Digital Object Identifier (DOI)

  • 10.1245/s10434-017-5896-1


  • eng

Conference Location

  • United States