Skip to main content
SpringerLink
Log in

Diffusion kurtosis imaging in the characterisation of rectal cancer: utilizing the most repeatable region-of-interest strategy for diffusion parameters on a 3T scanner

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

Abstract

Objectives

Our goal was to investigate the correlation between histopathology and diffusion parameters by utilising the most repeatable region-of-interest (ROI) strategy for diffusion parameters in rectal cancer on a 3T scanner.

Methods

113 patients underwent DKI-MR and 66 of these patients received surgery without neoadjuvant chemoradiotherapy. Two readers independently measured the parameters using three slice protocols including single slice, three slices and whole-tumour slice (WTS), combined with one of two ROIs, including outline and round ROI. ANOVA, Kruskal-Wallis, a paired sample t-test, interclass correlation coefficient (ICC), Bland-Altman, Student’s t-tests, receiver operating characteristic curves and z statistic were used for statistical analysis.

Results

There were no significant differences among the three slice protocols in ADC values (p = 0.822, 0.987), K values (p = 0.842, 0.859) and D values (p = 0.917, 0.988) using round and outline ROI, respectively. The ADC and D values derived from outline ROIs were higher than those from round ROIs (all p < 0.001 for ADC, all p < 0.001 for D), while K values derived from outline ROIs were lower than those from round ROIs (p < 0.001, p = 0.001, p < 0.001) using three slice protocols, respectively. The WTS-outline ROI resulted in the best intra- and inter-observer ICC. Utilising the WTS-outline ROI method, the AUC for assessment of well-differentiated tumours was 0.871 by K and 0.809 by ADC; and the AUC for T2 was 0.768 by K.

Conclusions

The most repeatable strategy was the WTS-outline ROI method. In addition to DWI, DKI also have diagnostic value for rectal cancer histopathological characteristics utilising the WTS-outline ROI on a 3T scanner.

Key Points

• DKI using a 3T scanner is feasible for assessing rectal cancer.

• ROI and slice protocol show considerable influence on DKI parameters.

• DKI parameters exhibit excellent repeatability using whole-tumour slice-outline ROI on 3T scanner.

• DKI has considerable diagnostic value for the estimation of rectal cancer characteristics.

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
Fig. 4
Fig. 5

Similar content being viewed by others

Abbreviations

ADC:

Apparent diffusion coefficient

AJCC:

American Joint Committee on Cancer

AUC:

Area under curve

BA-LA:

Bland-Altman limits of agreements

CRM:

Circumferential resection margin

D:

Corrected diffusion coefficient

DKI:

Diffusion kurtosis imaging

DWI:

Diffusion weighted imaging

EPI:

Single-shot echo-planar imaging

ICC:

Interclass correlation coefficient

K:

Diffusion kurtosis coefficient

LVI:

Lymphovascular invasion

MRI:

Magnetic resonance imaging

NCRT:

Neoadjuvant chemoradiotherapy

ROC:

Receiver operating characteristic

ROI:

Region of interest

SD:

Standard deviation

SNR:

Signal-to-noise ratio

SS:

Single slice

T2WI:

T2-weighted images

TS:

Three slices

TSE:

Turbo spin echo

WHO:

World Health Organization

WTS:

Whole tumour slice

References

  1. Van Cauter S, Veraart J, Sijbers J et al (2012) Gliomas: diffusion kurtosis MR imaging in grading. Radiology 263:492–501

    Article  Google Scholar 

  2. Rosenkrantz AB, Sigmund EE, Johnson G et al (2012) Prostate cancer: feasibility and preliminary experience of a diffusional kurtosis model for detection and assessment of aggressiveness of peripheral zone cancer. Radiology 264:126–135

    Article  Google Scholar 

  3. Rosenkrantz AB, Sigmund EE, Winnick A et al (2012) Assessment of hepatocellular carcinoma using apparent diffusion coefficient and diffusion kurtosis indices: preliminary experience in fresh liver explants. Magn Reson Imaging 30:1534–1540

    Article  Google Scholar 

  4. Yu J, Xu Q, Song JC et al (2017) The value of diffusion kurtosis magnetic resonance imaging for assessing treatment response of neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Eur Radiol 27:1848–1857

    Article  Google Scholar 

  5. Hu F, Tang W, Sun Y et al (2017) The value of diffusion kurtosis imaging in assessing pathological complete response to neoadjuvant chemoradiation therapy in rectal cancer: a comparison with conventional diffusion-weighted imaging. Oncotarget. https://doi.org/10.18632/oncotarget.17491

  6. Zhu L, Pan Z, Ma Q et al (2017) Diffusion Kurtosis Imaging Study of Rectal Adenocarcinoma Associated with Histopathologic Prognostic Factors: Preliminary Findings. Radiology 284:66–76

    Article  Google Scholar 

  7. Lambregts DM, Beets GL, Maas M et al (2011) Tumour ADC measurements in rectal cancer: effect of ROI methods on ADC values and interobserver variability. Eur Radiol 21:2567–2574

    Article  Google Scholar 

  8. Chao M, Guo X, Li L et al (2017) Effect of region of interest size on ADC measurements in pancreatic adenocarcinoma. Cancer Imaging 17:13

    Article  Google Scholar 

  9. Colagrande S, Pasquinelli F, Mazzoni LN, Belli G, Virgili G (2010) MR-diffusion weighted imaging of healthy liver parenchyma: repeatability and reproducibility of apparent diffusion coefficient measurement. J Magn Reson Imaging 31:912–920

    Article  Google Scholar 

  10. Bickel H, Pinker K, Polanec S et al (2017) Diffusion-weighted imaging of breast lesions: Region-of-interest placement and different ADC parameters influence apparent diffusion coefficient values. Eur Radiol 27:1883–1892

    Article  Google Scholar 

  11. Sun K, Chen X, Chai W et al (2015) Breast cancer: diffusion kurtosis mr imaging—diagnostic accuracy and correlation with clinical-pathologic factors. Radiology 277:46–55

    Article  Google Scholar 

  12. Jensen JH, Helpern JA (2010) MRI quantification of non-Gaussian water diffusion by kurtosis analysis. NMR Biomed 23:698–710

    Article  Google Scholar 

  13. Jensen JH, Helpern JA, Ramani A, Lu H, Kaczynski K (2005) Diffusional kurtosis imaging: The quantification of non-gaussian water diffusion by means of magnetic resonance imaging. Magn Reson Med 53:1432–1440

    Article  Google Scholar 

  14. Edge SB, Compton CC (2010) The American Joint Committee on Cancer: the 7th edition of the AJCC cancer staging manual and the future of TNM. Ann Surg Oncol 17:1471–1474

    Article  Google Scholar 

  15. Inoue C, Fujii S, Kaneda S et al (2014) Apparent diffusion coefficient (ADC) measurement in endometrial carcinoma: effect of region of interest methods on ADC values. J Magn Reson Imaging JMRI 40:157–161

    Article  Google Scholar 

  16. Akashi M, Nakahusa Y, Yakabe T et al (2014) Assessment of aggressiveness of rectal cancer using 3-T MRI: correlation between the apparent diffusion coefficient as a potential imaging biomarker and histologic prognostic factors. Acta Radiol 55:524–531

    Article  Google Scholar 

  17. Curvo-Semedo L, Lambregts DM, Maas M et al (2012) Diffusion-weighted MRI in rectal cancer: Apparent diffusion coefficient as a potential noninvasive marker of tumor aggressiveness. J Magn Reson Imaging 35:1365–1371

    Article  Google Scholar 

  18. Sun Y, Tong T, Cai S et al (2014) Apparent Diffusion Coefficient (ADC) Value: A Potential Imaging Biomarker That Reflects the Biological Features of Rectal Cancer. PloS One 9:e109371

    Article  Google Scholar 

  19. Wu EX, Cheung MM (2010) MR diffusion kurtosis imaging for neural tissue characterisation. NMR Biomed 23:836–848

    Article  Google Scholar 

Download references

Funding

This study has received funding by the National Natural Science Foundation of China (Grant No. 81501437).

Author information

Author notes

  1. Yiqun Sun and Qin Xiao contributed equally to this work and are co-first authors.

Authors and Affiliations

  1. Department of Radiology, Fudan University Shanghai Cancer Center, 270 Dong An Road, Shanghai, 200032, China

    Yiqun Sun, Qin Xiao, Feixiang Hu, Chao Xin, Weijun Peng, Tong Tong & Yajia Gu

  2. Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China

    Yiqun Sun, Qin Xiao, Feixiang Hu, Huixun Jia, Chao Xin, Sanjun Cai, Weijun Peng, Tong Tong & Yajia Gu

  3. Shanghai Institute of Medical Imaging, Fudan University, Shanghai, China

    Yiqun Sun & Xiaolin Wang

  4. Siemens Shenzhen Magnetic Resonance Ltd., Shenzhen, China

    Caixia Fu

  5. Department of Clinical Statistics Center, Fudan University Shanghai Cancer Center, Shanghai, China

    Huixun Jia

  6. MR Collaboration NE Asia, Siemens Healthineer, Shanghai, China

    Xu Yan

  7. Department of Colorectal Surgery, Fudan University Shanghai Cancer Center, Shanghai, China

    Sanjun Cai

Authors
  1. Yiqun Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qin Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Feixiang Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Caixia Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Huixun Jia
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xu Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Chao Xin
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Sanjun Cai
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Weijun Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Xiaolin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Tong Tong
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Yajia Gu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Tong Tong or Yajia Gu.

Ethics declarations

Guarantor

The scientific guarantor of this publication is Yajia Gu.

Conflict of interest

The authors of this manuscript declare relationships with the following companies: Siemens Shenzhen Magnetic Resonance Ltd., Shenzhen, China and MR Collaboration NE Asia, Siemens Healthineers, Shanghai, China

Statistics and biometry

Huixun Jia, MD, Department of Clinical Statistics Center, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China kindly provided statistical advice for this manuscript.

Informed consent

This was a retrospective study and did not require informed consent.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• retrospective

• diagnostic or prognostic study

• performed at one institution

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sun, Y., Xiao, Q., Hu, F. et al. Diffusion kurtosis imaging in the characterisation of rectal cancer: utilizing the most repeatable region-of-interest strategy for diffusion parameters on a 3T scanner. Eur Radiol 28, 5211–5220 (2018). https://doi.org/10.1007/s00330-018-5495-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00330-018-5495-y

Keywords

Search

Navigation