Skip to main content

Preoperative radiomic signature based on multiparametric magnetic resonance imaging for noninvasive evaluation of biological characteristics in rectal cancer



To develop and validate radiomic models in evaluating biological characteristics of rectal cancer based on multiparametric magnetic resonance imaging (MP-MRI).


This study consisted of 345 patients with rectal cancer who underwent MP-MRI. We focused on evaluating five postoperative confirmed characteristics: lymph node (LN) metastasis, tumor differentiation, fraction of Ki-67-positive tumor cells, human epidermal growth factor receptor 2 (HER-2), and KRAS-2 gene mutation status. Data from 197 patients were used to develop the biological characteristics evaluation models. Radiomic features were extracted from MP-MRI and then refined for reproducibility and redundancy. The refined features were investigated for usefulness in building radiomic signatures by using two feature-ranking methods (MRMR and WLCX) and three classifiers (RF, SVM, and LASSO). Multivariable logistic regression was used to build an integrated evaluation model combining radiomic signatures and clinical characteristics. The performance was evaluated using an independent validation dataset comprising 148 patients.


The MRMR and LASSO regression produced the best-performing radiomic signatures for evaluating HER-2, LN metastasis, tumor differentiation, and KRAS-2 gene status, with AUC values of 0.696 (95% CI, 0.610–0.782), 0.677 (95% CI, 0.591–0.763), 0.720 (95% CI, 0.621–0.819), and 0.651 (95% CI, 0.539–0.763), respectively. The best-performing signatures for evaluating Ki-67 produced an AUC value of 0.699 (95% CI, 0.611–0.786), and it was developed by WLCX and RF algorithm. The integrated evaluation model incorporating radiomic signature and MRI-reported LN status had improved AUC of 0.697 (95% CI, 0.612–0.781).


Radiomic signatures based on MP-MRI have potential to noninvasively evaluate the biological characteristics of rectal cancer.

Key Points

• Radiomic features were extracted from MP-MRI images of the rectal tumor.

The proposed radiomic signatures demonstrated discrimination ability in identifying the histopathological, immunohistochemical, and genetic characteristics of rectal cancer.

• All MRI sequences were important and could provide complementary information in radiomic analysis.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2



Area under the receiver operating characteristic curve


Colorectal cancer


Dynamic contrast-enhanced


Diffusion-weighted imaging


Human epidermal growth factor receptor 2


Least absolute shrinkage and selection operator


Multiparametric magnetic resonance imaging


Minimum redundancy maximum relevance


Random forest


Wilcoxon rank-sum test


  1. Siegel RL, Miller KD, Jemal A (2017) Cancer statistics, 2017. CA Cancer J Clin 67:7–30

    Article  PubMed  Google Scholar 

  2. Siegel RL, Miller KD, Fedewa SA et al (2017) Colorectal cancer statistics, 2017. CA Cancer J Clin 67:177–193

    Article  PubMed  Google Scholar 

  3. Van Cutsem E, Kohne CH, Hitre E et al (2009) Cetuximab and chemotherapy as initial treatment for metastatic colorectal cancer. N Engl J Med 360:1408–1417

    Article  PubMed  Google Scholar 

  4. Heinemann V, von Weikersthal LF, Decker T et al (2014) FOLFIRI plus cetuximab versus FOLFIRI plus bevacizumab as first-line treatment for patients with metastatic colorectal cancer (FIRE-3): a randomised, open-label, phase 3 trial. Lancet Oncol 15:1065–1075

    Article  CAS  PubMed  Google Scholar 

  5. Van Cutsem E, Kohne CH, Lang I et al (2011) Cetuximab plus irinotecan, fluorouracil, and Leucovorin as first-line treatment for metastatic colorectal cancer: updated analysis of overall survival according to tumor KRAS and BRAF mutation status. J Clin Oncol 29:2011–2019

    Article  CAS  PubMed  Google Scholar 

  6. Martin V, Landi L, Molinari F et al (2013) HER2 gene copy number status may influence clinical efficacy to anti-EGFR monoclonal antibodies in metastatic colorectal cancer patients. Br J Cancer 108:668–675

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Brown DC, Gatter KC (2002) Ki67 protein: the immaculate deception? Histopathology 40:2–11

    Article  CAS  PubMed  Google Scholar 

  8. Shah MA, Renfro LA, Allegra CJ et al (2016) Impact of patient factors on recurrence risk and time dependency of oxaliplatin benefit in patients with colon cancer: analysis from modern-era adjuvant studies in the Adjuvant Colon Cancer End Points (ACCENT) Database. J Clin Oncol 34:843-+

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Xiao H, Yoon YS, Hong SM et al (2013) Poorly differentiated colorectal cancers correlation of microsatellite instability with clinicopathologic features and survival. Am J Clin Pathol 140:341–347

    Article  PubMed  Google Scholar 

  10. Burrell RA, McGranahan N, Bartek J, Swanton C (2013) The causes and consequences of genetic heterogeneity in cancer evolution. Nature 501:338–345

    Article  CAS  PubMed  Google Scholar 

  11. Robertson EG, Baxter G (2011) Tumour seeding following percutaneous needle biopsy: the real story! Clin Radiol 66:1007–1014

    Article  CAS  PubMed  Google Scholar 

  12. Lambin P, Rios-Velazquez E, Leijenaar R et al (2012) Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 48:441–446

    Article  PubMed  PubMed Central  Google Scholar 

  13. Jhaveri KS, Hosseini-Nik H (2015) MRI of rectal cancer: an overview and update on recent advances. AJR Am J Roentgenol 205:W42–W55

    Article  PubMed  Google Scholar 

  14. Dinapoli N, Casa C, Barbaro B et al (2016) Radiomics for rectal cancer. Translational Cancer Research 5:424–431

    Article  Google Scholar 

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

    Article  PubMed  Google Scholar 

  16. 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−+

    Article  PubMed  Google Scholar 

  17. Nie K, Shi LM, 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 

  18. Liu ZY, 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 23:7253–7262

    Article  CAS  PubMed  Google Scholar 

  19. Yankai M, Yuchen Z, Di D et al (2018) Novel radiomic signature as a prognostic biomarker for locally advanced rectal cancer. J Magn Reson Imaging.

  20. Liang CS, Huang YQ, He L et al (2016) The development and validation of a CT-based radiomics signature for the preoperative discrimination of stage I-II and stage III-IV colorectal cancer. Oncotarget 7:31401–31412

    PubMed  PubMed Central  Google Scholar 

  21. Fluge O, Gravdal K, Carlsen E et al (2009) Expression of EZH2 and Ki-67 in colorectal cancer and associations with treatment response and prognosis. Br J Cancer 101:1282–1289

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Xia W, Gao X (2014) A fast deformable registration method for 4D lung CT in hybrid framework. Int J Comput Assist Radiol Surg 9:523–533

    Article  PubMed  Google Scholar 

  23. Vallieres M, Freeman CR, Skamene SR, El Naqa I (2015) A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities. Phys Med Biol 60:5471–5496

    Article  CAS  PubMed  Google Scholar 

  24. Xia W, Chen Y, Zhang R et al (2018) Radiogenomics of hepatocellular carcinoma: multiregion analysis-based identification of prognostic imaging biomarkers by integrating gene data-a preliminary study. Phys Med Biol.

  25. Aerts HJWL, Velazquez ER, Leijenaar RTH et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun.

  26. Parmar C, Grossmann P, Bussink J, Lambin P, Aerts HJWL (2015) Machine learning methods for quantitative radiomic biomarkers. Sci Rep.

  27. Peng HC, Long FH, Ding C (2005) Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27:1226–1238

    Article  PubMed  Google Scholar 

  28. Parmar C, Grossmann P, Rietveld D, Rietbergen MM, Lambin P, Aerts HJWL (2015) Radiomic machine-learning classifiers for prognostic biomarkers of head and neck cancer. Front Oncol.

  29. Fernandez-Delgado M, Cernadas E, Barro S, Amorim D (2014) Do we need hundreds of classifiers to solve real world classification problems? J Mach Learn Res 15:3133–3181

    Google Scholar 

  30. Hanley JA, Mcneil BJ (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143:29–36

    Article  CAS  PubMed  Google Scholar 

  31. Delong ER, Delong DM, Clarkepearson DI (1988) Comparing the areas under 2 or more correlated receiver operating characteristic curves - a nonparametric approach. Biometrics 44:837–845

    Article  CAS  PubMed  Google Scholar 

  32. Wu J, Sun XL, Wang J et al (2017) Identifying relations between imaging phenotypes and molecular subtypes of breast cancer: model discovery and external validation. J Magn Reson Imaging 46:1017–1027

    Article  PubMed  PubMed Central  Google Scholar 

  33. Zweig MH, Campbell G (1993) Receiver-operating characteristic (ROC) plots - a fundamental evaluation tool in clinical medicine. Clin Chem 39:561–577

    CAS  PubMed  Google Scholar 

  34. Saeys Y, Inza I, Larranaga P (2007) A review of feature selection techniques in bioinformatics. Bioinformatics 23:2507–2517

    Article  CAS  PubMed  Google Scholar 

  35. 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 

  36. Wu W, Parmar C, Grossmann P et al (2016) Exploratory study to identify radiomics classifiers for lung cancer histology. Front Oncol.

  37. Huang YQ, Liu ZY, He L et al (2016) Radiomics signature: a potential biomarker for the prediction of disease-free survival in early-stage (I or II) non-small cell lung cancer. Radiology 281:947–957

    Article  PubMed  Google Scholar 

  38. Collewet G, Strzelecki M, Mariette F (2004) Influence of MRI acquisition protocols and image intensity normalization methods on texture classification. Magn Reson Imaging 22:81–91

    Article  CAS  PubMed  Google Scholar 

Download references


Medical editor Katharine O’Moore-Klopf, ELS (East Setauket, NY, USA) provided professional English-language editing of this article.


This study has received funding by the National Natural Science Foundation of China [grant number 81571772, 81430041]; Science, Technology Plan Projects of Jiangsu—Society Development Project [grant number BE2017671]; and Foundation for Pearl River Science & Technology Young Scholars of Guangzhou [grant 201610010059].

Author information

Authors and Affiliations


Corresponding authors

Correspondence to Lei Wang or Xin Gao.

Ethics declarations


The scientific guarantor of this publication is Xin Gao.

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 by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.


• retrospective

• diagnostic experimental

• performed at one institution

Electronic supplementary material


(DOC 2182 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Meng, X., Xia, W., Xie, P. et al. Preoperative radiomic signature based on multiparametric magnetic resonance imaging for noninvasive evaluation of biological characteristics in rectal cancer. Eur Radiol 29, 3200–3209 (2019).

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:


  • Rectal neoplasms
  • Magnetic resonance imaging
  • Algorithms