Skip to main content

Advertisement

SpringerLink
Log in

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

  • Gastrointestinal
  • Published:
European Radiology Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Abbreviations

AFP:

Alpha fetoprotein

AUC:

Area under curve

CA199:

Carbohydrate antigen 199

CA242:

Carbohydrate antigen 242

CA724:

Carbohydrate antigen 724

CEA:

Carcinoembryonic antigen

CI:

Confidence interval

CRC:

Colorectal cancer

CT:

Computed tomography

EGFR:

Epidermal growth factor receptor

18F-FDG PET/CT:

Positron emession tomography/computerd tomography with 18F-fluorodexyglucose

FFPE:

Formalin-fixed paraffin- embedded

GLCM:

Gray-level co-occurrence matrix

GLRLM:

Gray-level run-length matrix

ICCs:

Intra-/inter-class correlation coefficients

NCCN:

Nationgal comprehensive cancer network

NGS:

Next-generation sequencing

OR:

Odds ratio

PACS:

Picture archiving and communication system

ROC:

Receiver operating characteristic

SUV:

Standardized uptake value

TPS:

Tissue polypeptide specific antigen

3D:

Three-dimensional

References

  1. van de Velde CJ, Boelens PG, Borras JM et al (2014) EURECCA colorectal: multidisciplinary management: European consensus conference colon & rectum. Eur J Cancer 50:1.e1–1.e34

    Article  Google Scholar 

  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 16:1306–1315

    Article  CAS  PubMed  Google Scholar 

  3. Strickler JH, Wu C, Bekaii-Saab T (2017) Targeting BRAF in metastatic colorectal cancer: maximizing molecular approaches. Cancer Treat Rev 60:109–119

    Article  CAS  PubMed  Google Scholar 

  4. Sundar R, Hong DS, Kopetz S, Yap TA (2017) Targeting BRAF-mutant colorectal cancer: progress in combination strategies. Cancer Discov 7:558–560

    Article  PubMed  Google Scholar 

  5. De Roock W, Claes B, Bernasconi D et al (2010) Effects of KRAS, BRAF, NRAS, and PIK3CA mutations on the efficacy of cetuximab plus chemotherapy in chemotherapy-refractory metastatic colorectal cancer: a retrospective consortium analysis. Lancet Oncol 11:753–762

    Article  PubMed  Google Scholar 

  6. Van Cutsem E, Lenz HJ, Köhne CH et al (2015) Fluorouracil, leucovorin, and irinotecan plus cetuximab treatment and RAS mutations in colorectal cancer. J Clin Oncol 33:692–700

    Article  PubMed  Google Scholar 

  7. Peeters M, Oliner KS, Price TJ et al (2015) Analysis of KRAS/NRAS mutations in a phase III study of panitumumab with FOLFIRI compared with FOLFIRI alone as second-line treatment for metastatic colorectal cancer. Clin Cancer Res 21:5469–5479

    Article  CAS  PubMed  Google Scholar 

  8. Barras D, Missiaglia E, Wirapati P et al (2017) BRAF V600E mutant colorectal cancer subtypes based on gene expression. Clin Cancer Res 23:104–115

    Article  CAS  PubMed  Google Scholar 

  9. Douillard JY, Oliner KS, Siena S et al (2013) Panitumumab-FOLFOX4 treatment and RAS mutations in colorectal cancer. N Engl J Med 369:1023–1034

    Article  CAS  PubMed  Google 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.

  11. Lubner MG, Stabo N, Lubner SJ et al (2015) CT textural analysis of hepatic metastatic colorectal cancer: pre-treatment tumour heterogeneity correlates with pathology and clinical outcomes. Abdom Imaging 40:2331–2337

    Article  PubMed  Google Scholar 

  12. Kawada K, Nakamoto Y, Kawada M et al (2012) Relationship between 18F-fluorodeoxyglucose accumulation and KRAS/BRAF mutations in colorectal cancer. Clin Cancer Res 18:1696–1703

    Article  CAS  PubMed  Google Scholar 

  13. Miles KA, Ganeshan B, Rodriguez-Justo M et al (2014) Multifunctional imaging signature for V-KI-RAS2 Kirsten rat sarcoma viral oncogene homolog (KRAS) mutations in colorectal cancer. J Nucl Med 55:386–391

    Article  CAS  PubMed  Google Scholar 

  14. Kawada K, Toda K, Nakamoto Y et al (2015) Relationship between 18F-FDG PET/CT scans and KRAS mutations in metastatic colorectal cancer. J Nucl Med 56:1322–1327

    Article  CAS  PubMed  Google Scholar 

  15. Chen SW, Chiang HC, Chen WT et al (2014) Correlation between PET/CT parameters and KRAS expression in colorectal cancer. Clin Nucl Med 39:685–689

    Article  PubMed  Google Scholar 

  16. Lovinfosse P, Koopmansch B, Lambert F et al (2016) 18 F-FDG PET/CT imaging in rectal cancer: relationship with the RAS mutational status. Br J Radiol 89:20160212

    Article  PubMed  PubMed Central  Google Scholar 

  17. Krikelis D, Skoura E, Kotoula V et al (2014) Lack of association between KRAS mutations and 18F-FDG PET/CT in Caucasian metastatic colorectal cancer patients. Anticancer Res 34:2571–2579

    PubMed  Google Scholar 

  18. Huang YQ, Liang CH, He L et al (2016) Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer. J Clin Oncol 34:2157–2164

    Article  PubMed  Google Scholar 

  19. Nie K, Shi L, Chen Q et al (2016) Rectal cancer: assessment of neoadjuvant chemoradiation outcome based on radiomics of multiparametric MRI. Clin Cancer Res 22:5256–5264

    Article  PubMed  Google Scholar 

  20. Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577

    Article  PubMed  Google Scholar 

  21. Zhang B, Tian J, Dong D et al (2017) Radiomics features of multiparametric MRI as novel prognostic factors in advanced nasopharyngeal carcinoma. Clin Cancer Res 23:4259–4269

    Article  PubMed  Google Scholar 

  22. Coroller TP, Grossmann P, Hou Y et al (2015) CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma. Radiother Oncol 114:345–350

    Article  PubMed  PubMed Central  Google Scholar 

  23. Vallières M, Kay-Rivest E, Perrin LJ (2017) Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer. Sci Rep 7:10117

    Article  PubMed  PubMed Central  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

  25. Aerts HJ, Velazquez ER, Leijenaar RT et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Boonstra K, Weersma RK, van Erpecum KJ, Rauws EA, Spanier BW, Poen AC (2013) Population-based epidemiology, malignancy risk, and outcomeof primary sclerosing cholangitis. Hepatology 58:2045–2055

    Article  CAS  PubMed  Google Scholar 

  27. Reyes O, Morell C, Ventura S (2015) Scalable extensions of the ReliefF algorithm for weighting and selecting features on the multi-label learning context. Neurocomputing 161:168–182

    Article  Google Scholar 

  28. Vandenberghe R, Nelissen N, Salmon E et al (2013) Binary classification of 18 F-flutemetamol PET using machine learning: comparison with visual reads and structural MRI. Neuroimage 64:517–525

    Article  PubMed  Google Scholar 

  29. Roffo G, Melzi S, Cristani M (2015) Infinite feature selection. Proceedings of the IEEE International Conference on Computer Vision 4202–4210

  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

  32. Baldus SE, Schaefer KL, Engers R, Hartleb D, Stoecklein NH, Gabbert HE (2010) Prevalence and heterogeneity of KRAS, BRAF, and PIK3CA mutations in primary colorectal adenocarcinomas and their corresponding metastases. Clin Cancer Res 16:790–799

    Article  CAS  PubMed  Google Scholar 

  33. Esteva A, Kuprel B, Novoa RA et al (2017) Dermatologist-level classification of skin cancer with deepneural networks. Nature 542:115–118

    Article  CAS  PubMed  Google Scholar 

  34. Anthimopoulos M, Christodoulidis S, Ebner L, Christe A, Mougiakakou S (2016) Lung pattern classification for interstitial lung diseases using a deep convolutional neural network. IEEE Trans Med Imaging 35:1207–1216

    Article  PubMed  Google Scholar 

  35. Emblem KE, Pinho MC, Zöllner FG et al (2015) A generic support vector machine model for preoperative glioma survival associations. Radiology 275:228–234

    Article  PubMed  Google Scholar 

Download references

Funding

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.

Author information

Author notes

  1. Lei Yang and Mengjie Fang contributed equally to this work.

Authors and Affiliations

  1. Department of Diagnostic Radiology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17, Pan Jia Yuan Nan-li, PO Box 2258, Beijing, 100021, China

    Lei Yang, Hongmei Zhang & Xinming Zhao

  2. CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Lei Yang, Di Dong, Mengjie Fang, Yongbei Zhu, Yali Zang, Zhenyu Liu & Jie Tian

  3. University of Chinese Academy of Sciences, Beijing, 100190, China

    Di Dong, Mengjie Fang & Jie Tian

  4. Beijing Key Lab of Molecular Imaging, Beijing, 100190, China

    Di Dong

  5. Department of Pathology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China

    Jianming Ying

  6. The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Jie Tian

Authors
  1. Lei Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Di Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mengjie Fang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yongbei Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yali Zang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Zhenyu Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Hongmei Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Jianming Ying
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Xinming Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Jie Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Di Dong, Xinming Zhao or Jie Tian.

Ethics declarations

Guarantor

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.

Informed consent

Written informed consent was waived in this study.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• retrospective

• diagnostic experimental

• performed at one institution

Electronic supplementary material

ESM 1

(DOCX 555 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, L., Dong, D., Fang, M. et al. Can CT-based radiomics signature predict KRAS/NRAS/BRAF mutations in colorectal cancer?. Eur Radiol 28, 2058–2067 (2018). https://doi.org/10.1007/s00330-017-5146-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00330-017-5146-8

Keywords

Search

Navigation