A clinical-radiomic model for improved prognostication of surgical candidates with colorectal liver metastases

Joshua Shur, Matthew Orton, Ashton Connor, Sandra Fischer, Carol Anne Moulton, Steven Gallinger, Dow Mu Koh, Kartik S. Jhaveri

Research output: Contribution to journalArticlepeer-review

33 Scopus citations


Background and Objectives: Colorectal cancer with liver metastases is potentially curable with surgical resection however clinical prognostic factors can insufficiently stratify patients. This study aims to assess whether radiomic features are prognostic and can inform clinical decision making. Methods: This single-site retrospective study included 102 patients who underwent colorectal liver metastases resection with preoperative computed tomography (CT), magnetic resonance imaging (MRI) with gadoxetic acid (EOB) and clinical covariates. A lasso-regularized multivariate Cox proportional hazards model was applied to 114 features (10 clinical, 104 radiomic) to determine association with disease-free survival (DFS). A prognostic index was derived using the significant Cox regression coefficients and their corresponding input features and a threshold was determined to classify patients into high- and low-risk groups, and DFS compared using log-rank tests. Results: Four covariates were significantly associated with DFS; bilobar disease (hazard ratio [HR]= 1.56; P =.0043), complete pathological response (HR= 0.67; P =.025), minimum pixel value (HR= 1.66; P =.00016), and small area emphasis (HR= 0.62; P =.0013) from the EOB-MRI data. Radiomic CT features were not prognostic. The prognostic index strongly stratified high- and low-risk prognostic groups (HR = 0.31; P =.00068). Conclusion: Radiomic MRI features provided meaningful prognostic information above clinical covariates alone. This merits further validation for potential clinical implementation to inform management.

Original languageEnglish (US)
Pages (from-to)357-364
Number of pages8
JournalJournal of Surgical Oncology
Issue number2
StatePublished - Feb 1 2020


  • colorectal cancer
  • computed tomography
  • magnetic resonance imaging
  • radiomics

ASJC Scopus subject areas

  • Surgery
  • Oncology


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