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European Radiology

, Volume 27, Issue 6, pp 2400–2410 | Cite as

Contribution of mono-exponential, bi-exponential and stretched exponential model-based diffusion-weighted MR imaging in the diagnosis and differentiation of uterine cervical carcinoma

  • Meng Lin
  • Xiaoduo Yu
  • Yan Chen
  • Han Ouyang
  • Bing Wu
  • Dandan Zheng
  • Chunwu Zhou
Magnetic Resonance

Abstract

Objectives

To investigate the potential of various metrics derived from mono-exponential model (MEM), bi-exponential model (BEM) and stretched exponential model (SEM)-based diffusion-weighted imaging (DWI) in diagnosing and differentiating the pathological subtypes and grades of uterine cervical carcinoma.

Methods

71 newly diagnosed patients with cervical carcinoma (50 cases of squamous cell carcinoma [SCC] and 21 cases of adenocarcinoma [AC]) and 32 healthy volunteers received DWI with multiple b values. The apparent diffusion coefficient (ADC), pure molecular diffusion (D), pseudo-diffusion coefficient (D*), perfusion fraction (f), water molecular diffusion heterogeneity index (alpha), and distributed diffusion coefficient (DDC) were calculated and compared between tumour and normal cervix, among different pathological subtypes and grades.

Results

All of the parameters were significantly lower in cervical carcinoma than normal cervical stroma except alpha. SCC showed lower ADC, D, f and DDC values and higher D* value than AC; D and DDC values of SCC and ADC and D values of AC were lower in the poorly differentiated group than those in the well–moderately differentiated group.

Conclusion

Compared with MEM, diffusion parameters from BEM and SEM may offer additional information in cervical carcinoma diagnosis, predicting pathological tumour subtypes and grades, while f and D showed promising significance.

Key Points

DWI-derived parameters by different models are related but provide diversified information.

Commonly used ADC by MEM of DWI overestimates the tissue water diffusivity.

DWI processed by BEM could separate blood perfusion from true diffusion effects.

The derived diffusion-related and perfusion-related parameters by BEM are superior to ADC.

Keywords

Diffusion-weighted imaging Mono-exponential model Bi-exponential model Stretched exponential model Uterine cervical neoplasm 

Abbreviations

AC

Adenocarcinoma

ADC

Apparent diffusion coefficient

Alpha

Water molecular diffusion heterogeneity index

BEM

Bi-exponential model

D

Pure molecular diffusion

D*

Pseudo-diffusion coefficient

DCE-MRI

Dynamic contrast-enhanced MRI

DDC

Distributed diffusion coefficient

DWI

Diffusion-weighted imaging

f

Perfusion fraction

MEM

Mono-exponential model

MRI

Magnetic resonance imaging

SCC

Squamous cell carcinoma

SEM

Stretched exponential model

Notes

Acknowledgments

We gratefully acknowledge Yingkui Zhang and Shikuo Fu (GE healthcare) for his excellent MR technique support, and associate professor Yan Song (Department of Pathology, Cancer Institute & Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences) for helping with the pathological images. The scientific guarantor of this publication is Prof. Chunwu Zhou. All authors of this manuscript state that this work has not received any funding. Author Meng Lin, Xiaoduo Yu, Yan Chen, Han Ouyang and Chunwu Zhou declare no relationships with any companies, while authors Bing Wu and Dandan Zheng were the scientists at GE MR Research China who mainly contributed to the manuscript editing and did not contribute to the design, data collection or analysis/interpretation of this study. No complex statistical methods were necessary for this paper. Institutional review board approval was obtained. Written informed consent was obtained from all subjects (patients) in this study. Study subjects or cohorts have not been previously reported. Methodology: prospective, diagnostic or prognostic study, performed at one institution.

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Copyright information

© European Society of Radiology 2016

Authors and Affiliations

  • Meng Lin
    • 1
  • Xiaoduo Yu
    • 1
  • Yan Chen
    • 1
  • Han Ouyang
    • 1
  • Bing Wu
    • 2
  • Dandan Zheng
    • 2
  • Chunwu Zhou
    • 1
  1. 1.Department of Diagnostic Radiology, Cancer Institute & Hospital, Peking Union Medical CollegeChinese Academy of Medical SciencesBeijingPeople’s Republic of China
  2. 2.GE MR Research ChinaBeijingPeople’s Republic of China

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