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Evaluation of amide proton transfer-weighted imaging for endometrial carcinoma histological features: a comparative study with diffusion kurtosis imaging

  • Magnetic Resonance
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

To investigate whether amide proton transfer-weighted imaging (APTWI) and diffusion kurtosis imaging (DKI) can be used to evaluate endometrial carcinoma (EC) in terms of clinical type, histological grade, subtype, and Ki-67 index.

Methods

Eighty-eight patients with EC underwent pelvic DKI and APTWI. The non-Gaussian diffusion coefficient (Dapp), apparent kurtosis coefficient (Kapp), and magnetization transfer ratio asymmetry (MTRasym (3.5 ppm)) were calculated and compared based on the clinical type (type I, II), histological grade (high- and low-grade), and subtype (endometrioid adenocarcinoma (EA) and non-EA). Correlation coefficients were calculated for each parameter with histological grades and the Ki-67 index.

Results

The MTRasym (3.5 ppm) and Kapp values were higher in the type II group and high-grade group than in the type I and low-grade groups, respectively, while the Dapp values were lower in the type I and low-grade groups, respectively (all p < 0.05). The Kapp value was higher in the EA group than in the non-EA group (p = 0.022). The Kapp value was the only independent predictor for the histological grade of EA and the clinical type of EC. The AUC (DKI) was higher than the AUC (APTWI) in the identification of type I and II EC and high- and low-grade EA (Z = 2.042, 2.013, p = 0.041, 0.044), while in the identification of EA and non-EA, only the difference in Kapp was statistically significant. Moreover, the Kapp and MTRasym (3.5 ppm) values and Dapp values correlated positively and negatively, respectively, with histological grade (r = 0.759, 0.555, 0.624, and 0.462, all p < 0.05) and Ki-67 index (r = −0.704, −0.507, all p < 0.05).

Conclusion

Both DKI- and APTWI-related parameters have potential as imaging markers in estimating the histological features of EC, while DKI shows better performance than APTWI in this study.

Key Points

• DKI and APTWI can be used to preliminarily evaluate the histological characteristics of endometrial carcinoma (EC).

• The Kapp was the only independent predictor for the histological grade of EA and the clinical type of EC.

• The Kapp, MTRasym (3.5 ppm), and Dapp correlated positively and negatively, respectively, with histological grade and Ki-67 index.

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Abbreviations

APTWI:

Amide proton transfer-weighted imaging

D app :

Non-Gaussian diffusion coefficient

DKI:

Diffusion-kurtosis imaging

EA:

Endometrioid adenocarcinoma

EC:

Endometrial carcinoma

FIGO:

International Federation of Gynecology and Obstetrics

Kapp :

Apparent kurtosis coefficient

MTRasym (3.5 ppm):

Magnetization transfer ratio asymmetry at 3.5 ppm

SI:

Signal intensity

References

  1. Chen W, Zheng R, Baade PD et al (2016) Cancer statistics in China, 2015. CA Cancer J Clin 66:115–132

    Article  Google Scholar 

  2. Fontham E, Wolf A, Church TR et al (2020) Cervical cancer screening for individuals at average risk: 2020 guideline update from the American Cancer Society. CA Cancer J Clin 70:321–346. 

  3. Bokhman JV (1983) Two pathogenetic types of endometrial carcinoma. Gynecol Oncol 15:10–17

    Article  CAS  Google Scholar 

  4. Scholten AN, Creutzberg CL, Noordijk EM, Smit VT (2002) Long-term outcome in endometrial carcinoma favors a two- instead of a three-tiered grading system. Int J Radiat Oncol Biol Phys 52:1067–1074

    Article  Google Scholar 

  5. Kitson S, Sivalingam VN, Bolton J et al (2017) Ki-67 in endometrial cancer: scoring optimization and prognostic relevance for window studies. Mod Pathol 30:459–468

    Article  CAS  Google Scholar 

  6. Werner HM, Trovik J, Marcickiewicz J et al (2013) A discordant histological risk classification in preoperative and operative biopsy in endometrial cancer is reflected in metastatic risk and prognosis. Eur J Cancer 49:625–632

    Article  CAS  Google Scholar 

  7. Garcia TS, Appel M, Rivero R et al (2017) Agreement between preoperative endometrial sampling and surgical specimen findings in endometrial carcinoma. Int J Gynecol Cancer 27:473–478

    Article  Google Scholar 

  8. Nougaret S, Horta M, Sala E et al (2019) Endometrial cancer MRI staging: Updated Guidelines of the European Society of Urogenital Radiology. Eur Radiol 29:792–805

    Article  Google Scholar 

  9. Ahmed M, Al-Khafaji JF, Class CA et al (2018) Can MRI help assess aggressiveness of endometrial cancer. Clin Radiol 73:833.e11–833.e18

    Article  CAS  Google Scholar 

  10. Bhosale P, Ma J, Iyer R et al (2016) Feasibility of a reduced field-of-view diffusion-weighted (rFOV) sequence in assessment of myometrial invasion in patients with clinical FIGO stage I endometrial cancer. J Magn Reson Imaging 43:316–324

    Article  Google Scholar 

  11. Andreano A, Rechichi G, Rebora P, Sironi S, Valsecchi MG, Galimberti S (2014) MR diffusion imaging for preoperative staging of myometrial invasion in patients with endometrial cancer: a systematic review and meta-analysis. Eur Radiol 24:1327–1338

    Article  Google Scholar 

  12. Beddy P, Moyle P, Kataoka M et al (2012) Evaluation of depth of myometrial invasion and overall staging in endometrial cancer: comparison of diffusion-weighted and dynamic contrast-enhanced MR imaging. Radiology 262:530–537

    Article  Google Scholar 

  13. Inoue C, Fujii S, Kaneda S et al (2015) Correlation of apparent diffusion coefficient value with prognostic parameters of endometrioid carcinoma. J Magn Reson Imaging 41:213–219

    Article  Google Scholar 

  14. 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 

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

    Article  Google Scholar 

  16. Qi XX, Shi DF, Ren SX et al (2018) Histogram analysis of diffusion kurtosis imaging derived maps may distinguish between low and high grade gliomas before surgery. Eur Radiol 28:1748–1755

    Article  Google Scholar 

  17. Yin J, Sun H, Wang Z, Ni H, Shen W, Sun PZ (2018) Diffusion kurtosis imaging of acute infarction: comparison with routine diffusion and follow-up MR imaging. Radiology 287:651–657

    Article  Google Scholar 

  18. 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 

  19. Meng N, Wang X, Sun J et al (2020) Application of the amide proton transfer-weighted imaging and diffusion kurtosis imaging in the study of cervical cancer. Eur Radiol 30:5758–5767

    Article  CAS  Google Scholar 

  20. Zhou J, Payen JF, Wilson DA, Traystman RJ, van Zijl PC (2003) Using the amide proton signals of intracellular proteins and peptides to detect pH effects in MRI. Nat Med 9:1085–1090

    Article  CAS  Google Scholar 

  21. Ohno Y, Yui M, Koyama H et al (2016) Chemical exchange saturation transfer MR imaging: preliminary results for differentiation of malignant and benign thoracic lesions. Radiology 279:578–589

    Article  Google Scholar 

  22. Krikken E, Khlebnikov V, Zaiss M et al (2018) Amide chemical exchange saturation transfer at 7 T: a possible biomarker for detecting early response to neoadjuvant chemotherapy in breast cancer patients. Breast Cancer Res 20:51

    Article  Google Scholar 

  23. Takayama Y, Nishie A, Togao O et al (2018) Amide proton transfer MR imaging of endometrioid endometrial adenocarcinoma: association with histologic grade. Radiology 286:909–917

    Article  Google Scholar 

  24. Yamada I, Sakamoto J, Kobayashi D et al (2019) Diffusion kurtosis imaging of endometrial carcinoma: correlation with histopathological findings. Magn Reson Imaging 57:337–346

    Article  Google Scholar 

  25. Jia G, Abaza R, Williams JD et al (2011) Amide proton transfer MR imaging of prostate cancer: a preliminary study. J Magn Reson Imaging 33:647–654

    Article  Google Scholar 

  26. Mitra AP, Birkhahn M, Cote RJ (2009) Re: Joseph Chin. In Search of the Perfect Crystal Ball for Ta Urothelial Cancer. Eur Urol. 10.1016/j.eururo.2009.09.014. Eur Urol 57:23–24

    Article  Google Scholar 

  27. Voss MA, Ganesan R, Ludeman L et al (2012) Should grade 3 endometrioid endometrial carcinoma be considered a type 2 cancer-a clinical and pathological evaluation. Gynecol Oncol 124:15–20

    Article  Google Scholar 

  28. Togao O, Yoshiura T, Keupp J et al (2014) Amide proton transfer imaging of adult diffuse gliomas: correlation with histopathological grades. Neuro Oncol 16:441–448

    Article  CAS  Google Scholar 

  29. Zheng S, van der Bom IM, Zu Z, Lin G, Zhao Y, Gounis MJ (2014) Chemical exchange saturation transfer effect in blood. Magn Reson Med 71:1082–1092

    Article  CAS  Google Scholar 

  30. Zaino RJ, Kurman RJ, Diana KL, Morrow CP (1995) The utility of the revised International Federation of Gynecology and Obstetrics histologic grading of endometrial adenocarcinoma using a defined nuclear grading system. A Gynecologic Oncology Group study. Cancer 75:81–86

    Article  CAS  Google Scholar 

  31. Dobrzycka B, Mackowiak-Matejczyk B, Kinalski M, Terlikowski SJ (2013) Pretreatment serum levels of bFGF and VEGF and its clinical significance in endometrial carcinoma. Gynecol Oncol 128:454–460

    Article  CAS  Google Scholar 

  32. Fukunaga T, Fujii S, Inoue C et al (2015) Accuracy of semiquantitative dynamic contrast-enhanced MRI for differentiating type II from type I endometrial carcinoma. J Magn Reson Imaging 41:1662–1668

    Article  Google Scholar 

  33. Li B, Sun H, Zhang S, Wang X, Guo Q (2019) The utility of APT and IVIM in the diagnosis and differentiation of squamous cell carcinoma of the cervix: a pilot study. Magn Reson Imaging 63:105–113

    Article  CAS  Google Scholar 

  34. Zhao X, Wen Z, Zhang G et al (2013) Three-dimensional turbo-spin-echo amide proton transfer MR imaging at 3-Tesla and its application to high-grade human brain tumors. Mol Imaging Biol 15:114–122

    Article  Google Scholar 

  35. Heo HY, Zhang Y, Lee DH et al (2017) Accelerating chemical exchange saturation transfer (CEST) MRI by combining compressed sensing and sensitivity encoding techniques. Magn Reson Med 77:779–786

    Article  CAS  Google Scholar 

  36. Rutgers JK (2015) Update on pathology, staging and molecular pathology of endometrial (uterine corpus) adenocarcinoma. Future Oncol 11:3207–3218

    Article  CAS  Google Scholar 

  37. Xiao Z, Zhong Y, Tang Z et al (2018) Standard diffusion-weighted, diffusion kurtosis and intravoxel incoherent motion MR imaging of sinonasal malignancies: correlations with Ki-67 proliferation status. Eur Radiol 28:2923–2933

    Article  Google Scholar 

  38. Khlebnikov V, Polders D, Hendrikse J et al (2017) Amide proton transfer (APT) imaging of brain tumors at 7 T: The role of tissue water T1 -Relaxation properties. Magn Reson Med 77:1525–1532

    Article  CAS  Google Scholar 

  39. Zhang S, Keupp J, Wang X et al (2018) Z-spectrum appearance and interpretation in the presence of fat: Influence of acquisition parameters. Magn Reson Med 79:2731–2737

    Article  Google Scholar 

  40. Liu R, Zhang H, Niu W et al (2020) Erratum to: improved chemical exchange saturation transfer imaging with real-time frequency drift correction (Magn Reson Med. 2019; 81: 2915-2923). Magn Reson Med 83:1884

    Article  Google Scholar 

Download references

Acknowledgements

We acknowledge the support received from the National Natural Science Foundation of China and Henan Medical Science and Technology Research Program. In addition, Nan Meng wants to say to Jing Sun: It is graceful grief and sweet sadness to think of you, but in my heart, there is a kind of soft warmth that can’t be expressed with any choice of words.

Funding

This study has received funding by the National Key R&D Program of China (2017YFE0103600), the Henan Medical Science and Technology Research Program (2018020357 and 2018020367), the National Natural Science Foundation of China (81720108021 and 31470047), the Zhongyuan Thousand Talents Plan Project-Basic Research Leader Talent (ZYQR201810117), and the Zhengzhou Collaborative Innovation Major Project (20XTZX05015).

Author information

Author notes

  1. Nan Meng and Xuejia Wang contributed equally to this work and should be considered co-first authors.

Authors and Affiliations

  1. Department of Medical Imaging, Zhengzhou University People’s Hospital & Henan Provincial People’s Hospital, Zhengzhou, Henan, China

    Nan Meng, Yan Bai, Wei Wei & Meiyun Wang

  2. Academy of Medical Sciences, Zhengzhou University, Zhengzhou, Henan, China

    Nan Meng, Yan Bai, Wei Wei & Meiyun Wang

  3. Department of MR, The First Affiliated Hospital, Xinxiang Medical University, Weihui, China

    Xuejia Wang & Dongming Han

  4. Department of Pediatrics, Zhengzhou Central Hospital, Zhengzhou University, Zhengzhou, China

    Jing Sun & Zhen Yang

  5. Department of Medical Imaging, Henan University People’s Hospital & Henan Provincial People’s Hospital, Zhengzhou, Henan, China

    Zhun Huang

  6. Department of Pathology, The First Affiliated Hospital, Xinxiang Medical University, Weihui, China

    Jie Shang

  7. Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA

    Hui Han

  8. MR Research China, GE Healthcare, Beijing, China

    Kaiyu Wang

  9. Department of Nephrology, Zhengzhou University People’s Hospital & Henan Provincial People’s Hospital, Zhengzhou, Henan, China

    Fengmin Shao

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  1. Nan Meng
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Corresponding authors

Correspondence to Fengmin Shao or Meiyun Wang.

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The scientific guarantor of this publication is Meiyun Wang.

Conflict of interest

One of the authors of this manuscript (Kaiyu Wang) is an employee of GE Healthcare. The remaining authors declare no relationships with any companies whose products or services may be related to the subject matter of the article.

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• performed at one institution

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Meng, N., Wang, X., Sun, J. et al. Evaluation of amide proton transfer-weighted imaging for endometrial carcinoma histological features: a comparative study with diffusion kurtosis imaging. Eur Radiol 31, 8388–8398 (2021). https://doi.org/10.1007/s00330-021-07966-y

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  • DOI: https://doi.org/10.1007/s00330-021-07966-y

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