Can CT-based radiomics signature predict KRAS/NRAS/BRAF mutations in colorectal cancer?

  • Lei Yang
  • , Di Dong
  • , Mengjie Fang
  • , Yongbei Zhu
  • , Yali Zang
  • , Zhenyu Liu
  • , Hongmei Zhang
  • , Jianming Ying
  • , Xinming Zhao*
  • , Jie Tian
  • *Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Objectives: To investigate whether CT-based radiomics signature can predict KRAS/NRAS/BRAF mutations in colorectal cancer (CRC). Methods: This retrospective study consisted of a primary cohort (n = 61) and a validation cohort (n = 56) with pathologically confirmed CRC. Patients underwent KRAS/NRAS/BRAF mutation tests and contrast-enhanced CT before treatment. A total of 346 radiomics features were extracted from portal venous-phase CT images of the entire primary tumour. Associations between the genetic mutations and clinical background, tumour staging, and histological differentiation were assessed using univariate analysis. RELIEFF and support vector machine methods were performed to select key features and build a radiomics signature. Results: The radiomics signature was significantly associated with KRAS/NRAS/BRAF mutations (P < 0.001). The area under the curve, sensitivity, and specificity for predicting KRAS/NRAS/BRAF mutations were 0.869, 0.757, and 0.833 in the primary cohort, respectively, while they were 0.829, 0.686, and 0.857 in the validation cohort, respectively. Clinical background, tumour staging, and histological differentiation were not associated with KRAS/NRAS/BRAF mutations in both cohorts (P>0.05). Conclusions: The proposed CT-based radiomics signature is associated with KRAS/NRAS/BRAF mutations. CT may be useful for analysis of tumour genotype in CRC and thus helpful to determine therapeutic strategies. Key Points: • Key features were extracted from CT images of the primary colorectal tumour. • The proposed radiomics signature was significantly associated with KRAS/NRAS/BRAF mutations. • In the primary cohort, the proposed radiomics signature predicted mutations. • Clinical background, tumour staging, and histological differentiation were unable to predict mutations.

Original languageEnglish
Pages (from-to)2058-2067
Number of pages10
JournalEuropean Radiology
Volume28
Issue number5
DOIs
StatePublished - 1 May 2018
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • Adenocarcinoma
  • Colorectal neoplasms
  • Diagnostic imaging
  • Mutation
  • ROC curve

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