The following list has been reviewed and approved by the European Society of Urogenital Radiology’s (ESUR)’s Women’s Pelvic Imaging Group.

CERVIX

Validated
biomarkers

Level of
evidence1!

Single centre/
Multicentre/
metaanalysis

Indication

Patient prep

Data acquisition
requirements

Image processing
algorithm

Recommended biomarkers

Morphology

12,3!
34,5!

Multicentre, Single centre

Staging and treatment response assessment

IM antiperstaltic agent, or 6h fast before MRI or vaginal/rectal gel

T2W in three planes, optional dynamic DCE and DWI

Manual documentation by trained observer

Morphology: tumour volume

16!
37!

Single centre

Detection, staging, prognosis

IM antiperistaltic agent

ZOOM T2 and DWI in three planes with endovaginal coil, large FOV pelvis

Manual documentation by trained observer

DWI (visual) +/- T2

18-10!
311!

Single centre

Diagnosis & staging, Treatment response assessment

IM antiperistaltic agent

At least 2 b-values (b=0/50 and b=800-1000 s/mm2)

Manual documentation by trained observer
               



Not recommended
for clinical trials

ADC

112-15!
316!

Single centre

Differentation benign from malingnant, treatment response assessment

IM antiperistaltic agent

At least 2 b-values (b=0/50 and b=800-1000 mm/s)

Monoexponential fit of data

ADC histogram analysis

117, 18!

Single centre

Histopathological tumour features, Outcome prediction

IM antiperistaltic agent

At least 2 b-values (b=0/50 and b=800-1000 mm/s)

Monoexponential fit of data, derivation of first order parameters

ADC IVIM

119, 20!

Single centre

Histopathological tumour features, Treatment response assessment

IM antiperistaltic agent

9 b-values to 14 b-values

Non-monoexponential fit

Ktrans, Ve

113, 21!

Single centre

Treatment response assessment

No

Temporal resolution: 3 s, 80-100 repetitions

Tofts and Kety two-compartment models

Radiomics

122!
223!

Single centre

Histopathological tumour features, Treatment response assessment

No

T2 and DWI
DCE temporal resolution <10 s

No consensus
  1. Schweitzer ME. Evidence level. J Magn Reson Imaging. 2016;43(3):543-543. doi:10.1002/jmri.25187
  2. Epstein E, Testa A, Gaurilcikas A, et al. Early-stage cervical cancer: Tumor delineation by magnetic resonance imaging and ultrasound — A European multicenter trial. Gynecol Oncol. 2013;128(3):449-453. doi:10.1016/j.ygyno.2012.09.025
  3. Balleyguier C, Sala E, Da Cunha T, et al. Staging of uterine cervical cancer with MRI: guidelines of the European Society of Urogenital Radiology. Eur Radiol. 2011;21(5):1102-1110. doi:10.1007/s00330-010-1998-x
  4. Hricak H, Swift PS, Campos Z, Quivey JM, Gildengorin V, Göranson H. Irradiation of the cervix uteri: value of unenhanced and contrast-enhanced MR imaging. Radiology. 1993;189(2):381-388. doi:10.1148/radiology.189.2.8210364
  5. Mongula J, Slangen B, Lambregts D, et al. Predictive criteria for MRI-based evaluation of response both during and after radiotherapy for cervical cancer. J Contemp Brachytherapy. 2016;8(3):181-188. doi:10.5114/jcb.2016.61065
  6. Soutter WP, Hanoch J, D’Arcy T, Dina R, McIndoe GA, deSouza NM. Pretreatment tumour volume measurement on high-resolution magnetic resonance imaging as a predictor of survival in cervical cancer. BJOG An Int J Obstet Gynaecol. 2004;111(7):741-747. doi:10.1111/j.1471-0528.2004.00172.x
  7. deSouza NM, Dina R, McIndoe GA, Soutter WP. Cervical cancer: Value of an endovaginal coil magnetic resonance imaging technique in detecting small volume disease and assessing parametrial extension. Gynecol Oncol. 2006;102(1):80-85. doi:10.1016/j.ygyno.2005.11.038
  8. Exner M, Kühn A, Stumpp P, et al. Value of diffusion-weighted MRI in diagnosis of uterine cervical cancer: a prospective study evaluating the benefits of DWI compared to conventional MR sequences in a 3T environment. Acta radiol. 2016;57(7):869-877. doi:10.1177/0284185115602146
  9. Downey K, Attygalle AD, Morgan VA, et al. Comparison of optimised endovaginal vs external array coil T2-weighted and diffusion-weighted imaging techniques for detecting suspected early stage (IA/IB1) uterine cervical cancer. Eur Radiol. 2016;26(4):941-950. doi:10.1007/s00330-015-3899-5
  10. Park JJ, Kim CK, Park SY, Park BK. Parametrial Invasion in Cervical Cancer: Fused T2-weighted Imaging and High- b -Value Diffusion-weighted Imaging with Background Body Signal Suppression at 3 T. Radiology. 2015;274(3):734-741. doi:10.1148/radiol.14140920
  11. Park JJ, Kim CK, Park BK. Prediction of disease progression following concurrent chemoradiotherapy for uterine cervical cancer: value of post-treatment diffusion-weighted imaging. Eur Radiol. 2016;26(9):3272-3279. doi:10.1007/s00330-015-4156-7
  12. Charles-Edwards EM, Messiou C, Morgan VA, et al. Diffusion-weighted Imaging in Cervical Cancer with an Endovaginal Technique: Potential Value for Improving Tumor Detection in Stage Ia and Ib1 Disease. Radiology. 2008;249(2):541-550. doi:10.1148/radiol.2491072165
  13. Park JJ, Kim CK, Park SY, et al. Assessment of early response to concurrent chemoradiotherapy in cervical cancer: value of diffusion-weighted and dynamic contrast-enhanced MR imaging. Magn Reson Imaging. 2014;32(8):993-1000. doi:10.1016/j.mri.2014.05.009
  14. Kuang F, Yan Z, Wang J, Rao Z. The value of diffusion-weighted MRI to evaluate the response to radiochemotherapy for cervical cancer. Magn Reson Imaging. 2014;32(4):342-349. doi:10.1016/j.mri.2013.12.007
  15. Fu Z-Z, Peng Y, Cao L-Y, Chen Y-S, Li K, Fu B-H. Value of apparent diffusion coefficient (ADC) in assessing radiotherapy and chemotherapy success in cervical cancer. Magn Reson Imaging. 2015;33(5):516-524. doi:10.1016/j.mri.2015.02.002
  16. Gladwish A, Milosevic M, Fyles A, et al. Association of Apparent Diffusion Coefficient with Disease Recurrence in Patients with Locally Advanced Cervical Cancer Treated with Radical Chemotherapy and Radiation Therapy. Radiology. 2016;279(1):158-166. doi:10.1148/radiol.2015150400
  17. Downey K, Riches SF, Morgan VA, et al. Relationship between imaging biomarkers of stage I cervical cancer and poor-prognosis histologic features: quantitative histogram analysis of diffusion-weighted MR images. AJR Am J Roentgenol. 2013;200(2):314-320. doi:10.2214/AJR.12.9545
  18. Meng J, Zhu L, Zhu L, et al. Whole-lesion ADC histogram and texture analysis in predicting recurrence of cervical cancer treated with CCRT. Oncotarget. 2017;8(54):92442-92453. doi:10.18632/oncotarget.21374
  19. Zhu L, Zhu L, Shi H, et al. Evaluating early response of cervical cancer under concurrent chemo-radiotherapy by intravoxel incoherent motion MR imaging. BMC Cancer. 2016;16(1):79. doi:10.1186/s12885-016-2116-5
  20. Winfield JM, Orton MR, Collins DJ, et al. Separation of type and grade in cervical tumours using non-mono-exponential models of diffusion-weighted MRI. Eur Radiol. 2017;27(2):627-636. doi:10.1007/s00330-016-4417-0
  21. Kim J-H, Kim CK, Park BK, Park SY, Huh SJ, Kim B. Dynamic contrast-enhanced 3-T MR imaging in cervical cancer before and after concurrent chemoradiotherapy. Eur Radiol. 2012;22(11):2533-2539. doi:10.1007/s00330-012-2504-4
  22. Liu Y, Zhang Y, Cheng R, et al. Radiomics analysis of apparent diffusion coefficient in cervical cancer: A preliminary study on histological grade evaluation. J Magn Reson Imaging. May 2018. doi:10.1002/jmri.26192
  23. Bowen SR, Yuh WTC, Hippe DS, et al. Tumor radiomic heterogeneity: Multiparametric functional imaging to characterize variability and predict response following cervical cancer radiation therapy. J Magn Reson Imaging. 2018;47(5):1388-1396. doi:10.1002/jmri.25874