Can CT-based radiomics signature predict KRAS/NRAS/BRAF mutations in colorectal cancer?
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To investigate whether CT-based radiomics signature can predict KRAS/NRAS/BRAF mutations in colorectal cancer (CRC).
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.
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).
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 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.
KeywordsColorectal neoplasms Adenocarcinoma Mutation Diagnostic imaging ROC curve
Abbreviations and Acronyms
Area under curve
Carbohydrate antigen 199
Carbohydrate antigen 242
Carbohydrate antigen 724
Epidermal growth factor receptor
- 18F-FDG PET/CT
Positron emession tomography/computerd tomography with 18F-fluorodexyglucose
Formalin-fixed paraffin- embedded
Gray-level co-occurrence matrix
Gray-level run-length matrix
Intra-/inter-class correlation coefficients
Nationgal comprehensive cancer network
Picture archiving and communication system
Receiver operating characteristic
Standardized uptake value
Tissue polypeptide specific antigen
This study has received funding by the National Natural Science Foundation of China (grant numbers 81227901, 81771924, 61231004, 81501616, 81671851, 81527805, 81501549, 81671829 and 81671757), the National Key R&D Program of China (grant numbers 2017YFA0205200, 2017YFC1308700, 2017YFC1309100, and 2017YFC1308701), the Science and Technology Service Network Initiative of the Chinese Academy of Sciences (grant number KFJ-SW-STS-160), the Instrument Developing Project (grant number YZ201502), the Beijing Municipal Science and Technology Commission (grant number Z161100002616022), and the Youth Innovation Promotion Association CAS.
Compliance with ethical standards
The scientific guarantor of this publication is Jie Tian.
Conflict of interest
The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.
Statistics and biometry
No complex statistical methods were necessary for this paper.
Written informed consent was waived in this study.
Institutional Review Board approval was obtained.
• diagnostic experimental
• performed at one institution
- 2.Cremolini C, Loupakis F, Antoniotti C et al (2015) FOLFOXIRI plus bevacizumab versus FOLFIRI plus bevacizumab as first-line treatment of patients with metastatic colorectal cancer: updated overall survival and molecular subgroup analyses of the open-label, phase 3 TRIBE study. Lancet Oncol 16:1306–1315CrossRefPubMedGoogle Scholar
- 10.Tabernero J, Lenz HJ, Siena S et al (2015) Analysis of circulating DNA and protein biomarkers to predict the clinical activity of regorafenib and assess prognosis in patients with metastatic colorectal cancer: a retrospective, exploratory analysis of the CORRECT trial. Lancet Oncol 16: 937-948.Google Scholar
- 24.Liu Z, Zhang XY, Shi YJ et al (2017) Radiomics analysis for evaluation of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Clin Cancer Res. https://doi.org/10.1158/1078-0432.CCR-17-1038
- 29.Roffo G, Melzi S, Cristani M (2015) Infinite feature selection. Proceedings of the IEEE International Conference on Computer Vision 4202–4210Google Scholar
- 30.Liu S, Liu S, Ji C et al (2017) Application of CT texture analysis in predicting histopathological characteristics of gastric cancers. Eur Radiol. https://doi.org/10.1007/s00330-017-4881-1
- 31.Shiri I, Rahmim A, Ghaffarian P, Geramifar P, Abdollahi H, Bitarafan-Rajabi A (2017) The impact of image reconstruction settings on 18F-FDG PET radiomic features: multi-scanner phantom and patient studies. Eur Radiol. https://doi.org/10.1007/s00330-017-4859-z