Multiparametric PET/MR (PET and MR-IVIM) for the evaluation of early treatment response and prediction of tumor recurrence in patients with locally advanced cervical cancer

  • Si Gao
  • Siyao Du
  • Zaiming Lu
  • Jun Xin
  • Song Gao
  • Hongzan SunEmail author
Molecular Imaging



To assess the value of 18F-FDG PET and MR-IVIM parameters before and during concurrent chemoradiotherapy (CCRT) for evaluating early treatment response and predicting tumor recurrence in patients with locally advanced cervical cancer (LACC) using a hybrid PET/MR scanner.


Fifty-one patients with LACC underwent pelvic PET/MR scans with an IVIM sequence at two time-points (pretreatment [pre] and midtreatment [mid]). Pre- and mid-PET parameters (SUVmax, MTV, TLG) and IVIM parameters (D, F, D*) and their percentage changes (Δ%SUVmax, Δ%MTV, Δ%TLG, Δ%D, Δ%F, Δ%D*) were calculated. We selected independent imaging parameters and built a combined prediction model incorporating imaging parameters and clinicopathological risk factors. The performance of the combinative evaluation for tumor early shrinkage rates (TESR) and the prediction model for tumor recurrence was assessed.


Thirty-two patients were classified into the good response (GR) group with TESR ≥ 50%, and 19 patients were categorized into the poor response (PR) group with TESR < 50%. Δ%D (p = 0.013) and Δ%F (p = 0.006) are independently related to TESR with superior combined diagnostic ability (AUC = 0.901). Pre-TLG, Δ%D, and suspicious lymph node metastasis (SLNM) were selected for the construction of the combined prediction model. The model for identifying the patients with high risk of tumor recurrence reached a moderate predictive ability and good stability with c-index of 0.764 (95% CI, 0.672–0.855).


The combined prediction model based on pretreatment PET metabolic parameter (pre-TLG), IVIM-D percentage changes, and LNs status provides great potential to identify the LACC patients with high risk of recurrence at early stage of CCRT.

Key Points

PET/MR plus IVIM offers various complementary information for LACC.

IVIM-D and IVIM-F percentage changes are independently related to tumor early shrinkage rates.

The combined prediction model can help identify the LACC patients with high risk of tumor recurrence.


Concurrent chemoradiotherapy Positron emission tomography Diffusion magnetic resonance imaging Cervical cancer 





Apparent diffusion coefficient


Concurrent chemoradiotherapy


Slow diffusion coefficient


Fast diffusion coefficient


Diffusion-weighted imaging


Perfusion-related diffusion fraction


Federation International of Gynecology and Obstetrics


Good response


Intravoxel incoherent motion


Locally advanced cervical cancer


Lymph nodes


Maximum diameter


Magnetic resonance imaging


Metabolic tumor volume


Positron emission tomography


Poor response


Recurrence-free survival


Receiver operator characteristic


Region of interest


Suspicious lymph nodes metastasis


Maximum standardized uptake value


Tumor early shrinkage rates


Total lesion glycolysis


Volume of interest

Week 4

The end of the fourth week during CCRT



All authors sincerely thank Dr. Shengtao Lin, Dr. Zhongwei Chen, and SAGE Language Service Team for providing language help on the writing of the paper. The authors also thank Dr. Qijun Wu for his constructive advice on the statistical analysis.

Funding information

This study has received funding by the National Natural Science Foundation of China (No.81401438), LIAONING Science & Technology Project (No.2017225012).

Compliance with ethical standards


The scientific guarantor of this publication is Dr. Hongzan Sun.

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

An expert in statistics Dr. Qijun Wu kindly provided statistical advice for this manuscript.

Informed consent

Written informed consent was obtained from all patients in this study.

Ethical approval

Institutional Review Board approval from Ethics Committee of Shengjing Hospital affiliated to China Medical University (Shenyang, China) was obtained.


• prospective

• prognostic study

• performed at one institution

Supplementary material

330_2019_6428_MOESM1_ESM.doc (112 kb)
ESM 1 (DOC 111 kb)


  1. 1.
    Al-Mansour Z, Verschraegen C (2010) Locally advanced cervical cancer: what is the standard of care? Curr Opin Oncol 22:503–512CrossRefGoogle Scholar
  2. 2.
    Kirwan JM, Symonds P, Green JA, Tierney J, Collingwood M, Williams CJ (2003) A systematic review of acute and late toxicity of concomitant chemoradiation for cervical cancer. Radiother Oncol 68:217–226CrossRefGoogle Scholar
  3. 3.
    Barwick TD, Taylor A, Rockall A (2013) Functional imaging to predict tumor response in locally advanced cervical cancer. Curr Oncol Rep 15:549–558CrossRefGoogle Scholar
  4. 4.
    Herrera FG, Breuneval T, Prior JO, Bourhis J, Ozsahin M (2016) [18F]FDG-PET/CT metabolic parameters as useful prognostic factors in cervical cancer patients treated with chemo-radiotherapy. Radiat Oncol 11:43CrossRefGoogle Scholar
  5. 5.
    Yoo J, Choi JY, Moon SH et al (2012) Prognostic significance of volume-based metabolic parameters in uterine cervical cancer determined using 18F-fluorodeoxyglucose positron emission tomography. Int J Gynecol Cancer 22:1226–1233CrossRefGoogle Scholar
  6. 6.
    Kidd EA, Thomas M, Siegel BA, Dehdashti F, Grigsby PW (2013) Changes in cervical cancer FDG uptake during chemoradiation and association with response. Int J Radiat Oncol Biol Phys 85:116–122CrossRefGoogle Scholar
  7. 7.
    Liu Y, Ye Z, Sun H, Bai R (2015) Clinical application of diffusion-weighted magnetic resonance imaging in uterine cervical cancer. Int J Gynecol Cancer 25:1073–1078CrossRefGoogle Scholar
  8. 8.
    Le Bihan D, Breton E, Lallemand D, Aubin ML, Vignaud J, Laval-Jeantet M (1988) Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging. Radiology 168:497–505CrossRefGoogle Scholar
  9. 9.
    Zhu L, Zhu L, Shi H et al (2016) Evaluating early response of cervical cancer under concurrent chemo-radiotherapy by intravoxel incoherent motion MR imaging. BMC Cancer 16:79CrossRefGoogle Scholar
  10. 10.
    Zhu L, Zhu L, Wang H et al (2017) Predicting and early monitoring treatment efficiency of cervical cancer under concurrent chemoradiotherapy by intravoxel incoherent motion magnetic resonance imaging. J Comput Assist Tomogr 41:422–429CrossRefGoogle Scholar
  11. 11.
    Zhu L, Wang H, Zhu L et al (2017) Predictive and prognostic value of intravoxel incoherent motion (IVIM) MR imaging in patients with advanced cervical cancers undergoing concurrent chemoradiotherapy. Sci Rep 7:11635CrossRefGoogle Scholar
  12. 12.
    Grueneisen J, Schaarschmidt BM, Heubner M et al (2015) Integrated PET/MRI for whole-body staging of patients with primary cervical cancer: preliminary results. Eur J Nucl Med Mol Imaging 42:1814–1824CrossRefGoogle Scholar
  13. 13.
    Park JJ, Kim CK, Park BK (2016) Prognostic value of diffusion-weighted magnetic resonance imaging and 18F-fluorodeoxyglucose-positron emission tomography/computed tomography after concurrent chemoradiotherapy in uterine cervical cancer. Radiother Oncol 120:507–511CrossRefGoogle Scholar
  14. 14.
    Akkas BE, Demirel BB, Dizman A, Vural GU (2013) Do clinical characteristics and metabolic markers detected on positron emission tomography/computerized tomography associate with persistent disease in patients with in-operable cervical cancer? Ann Nucl Med 27:756–763CrossRefGoogle Scholar
  15. 15.
    Makino H, Kato H, Furui T, Morishige K, Kanematsu M (2014) Predictive value of diffusion-weighted magnetic resonance imaging during chemoradiotherapy for uterine cervical cancer. J Obstet Gynaecol Res 40:1098–1104CrossRefGoogle Scholar
  16. 16.
    Onal C, Erbay G, Guler OC (2016) Treatment response evaluation using the mean apparent diffusion coefficient in cervical cancer patients treated with definitive chemoradiotherapy. J Magn Reson Imaging 44:1010–1019CrossRefGoogle Scholar
  17. 17.
    Miccò M, Vargas HA, Burger IA et al (2014) Combined pre-treatment MRI and 18F-FDG PET/CT parameters as prognostic biomarkers in patients with cervical cancer. Eur J Radiol 83:1169–1176CrossRefGoogle Scholar
  18. 18.
    Bae JM, Kim CK, Park JJ, Park BK (2016) Can diffusion-weighted magnetic resonance imaging predict tumor recurrence of uterine cervical cancer after concurrent chemoradiotherapy? Abdom Radiol (NY) 41:1604–1610CrossRefGoogle Scholar
  19. 19.
    Liu FY, Su TP, Wang CC et al (2018) Roles of posttherapy 18F-FDG PET/CT in patients with advanced squamous cell carcinoma of the uterine cervix receiving concurrent chemoradiotherapy. Eur J Nucl Med Mol Imaging 45:1197–1204CrossRefGoogle Scholar
  20. 20.
    Kuang F, Yan Z, Wang J, Rao Z (2014) The value of diffusion weighted MRI to evaluate the response to radiochemotherapy for cervical cancer. Magn Reson Imaging 32:342–349CrossRefGoogle Scholar
  21. 21.
    Das S, Chandramohan A, Reddy JK et al (2015) Role of conventional and diffusion weighted MRI in predicting treatment response after low dose radiation and chemotherapy in locally advanced carcinoma cervix. Radiother Oncol 117:288–293CrossRefGoogle Scholar
  22. 22.
    Liu Y, Sun H, Bai R, Ye Z (2015) Time-window of early detection of response to concurrent chemoradiation in cervical cancer by using diffusion-weighted MR imaging: a pilot study. Radiat Oncol 10:185CrossRefGoogle Scholar
  23. 23.
    Liu FY, Lai CH, Yang LY et al (2016) Utility of 18F-FDG PET/CT in patients with advanced squamous cell carcinoma of the uterine cervix receiving concurrent chemoradiotherapy: a parallel study of a prospective randomized trial. Eur J Nucl Med Mol Imaging 43:1812–1823CrossRefGoogle Scholar

Copyright information

© European Society of Radiology 2019

Authors and Affiliations

  1. 1.Department of RadiologyShengjing Hospital of China Medical UniversityShenyangPeople’s Republic of China
  2. 2.Liaoning Provincial Key Laboratory of Medical ImagingShenyangPeople’s Republic of China
  3. 3.Division of Gynecologic Oncology, Department of Obstetrics and GynecologyShengjing Hospital of China Medical UniversityShenyangPeople’s Republic of China

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