Search for Optimal MRI Protocol for the Diagnosis of Vaginal Tumor

Background. A wide variety of pulse sequences, the possibility of multiplanar imaging significantly increase the capabilities of magnetic resonance imaging (MRI) in diagnosing diseases of the female reproductive system. At the same time, the lack of a regulated scanning protocol, especially in patients who have undergone anticancer treatment, requires standardization of the technique and the search for an optimal set of pulse sequences that allows to visualize the vagina throughout its entire length and to perform differential diagnosis between continued tumor growth and post-radiation changes while maintaining an adequate examination time.
Objective: to determine the optimal set of MRI pulse sequences for pelvic organs examination in patients with vaginal tumors and to form an original research protocol based on diagnostic information content.
Material and methods. The study included 141 patients with vaginal tumors. A comparative analysis of four MRI protocols was carried out, built according to the principle “from simple to complex” (from native to multiparametric MRI).
Results. A significant difference was obtained between all information content indicators of Protocol 4 compared to Protocol 1 (sensitivity p = 0.00006, specificity p = 0.00443, AUC p = 0.00000). Data analysis also showed a significant difference between sensitivity and AUC for Protocols 2 and 4 (p = 0.00150 and p = 0.00087, respectively), and Protocols 3 and 4 (p = 0.01333 and p = 0.01333, respectively).
Conclusion. Significant increase in the information content of Protocol 4 compared to other protocols (sensitivity up to 93%, specificity up to 94%, accuracy up to 93%) indicates the expediency of the priority use of multiparametric MRI in the primary diagnosis of vaginal tumor lesions.

1. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2019. CA Cancer J Clin. 2019; 69(1): 7–34. https://doi.org/10.3322/caac.21551.

2. López C, Balogun M, Ganesan R, Olliff JF. MRI of vaginal conditions. Clin Radiol. 2005; 60(6): 648–62. https://doi.org/10.1016/j.crad.2005.02.010.

3. Filatova EI. Primary cancer of the vagina. Diagnosis and treatment tactics. Practical Oncology. 2006; 7(4): 228–35 (in Russ).

4. Nudnov NV, Aksenova SP, Kreynina YuM, Kotlyarov PM. Magnetic resonance imaging in the diagnosis of secondary vaginal tumor involvement. Journal of Radiology and Nuclear Medicine. 2015; 3: 37–46 (in Russ).

5. Chow L, Tsui BQ, Bahrami S, et al. Gynecologic tumor board: a radiologist’s guide to vulvar and vaginal malignancies. Abdom Radiol (NY). 2021; 46(12): 5669–86. https://doi.org/10.1007/s00261-021-03209-2.

6. Current FIGO staging for cancer of the vagina, fallopian tube, ovary, and gestational trophoblastic neoplasia. Int J Gynaecol Obstet. 2009; 105(1): 3–4. https://doi.org/10.1016/j.ijgo.2008.12.015.

7. Trufanov VG, Panov VO. A guide to radiation diagnostics in gynecology. St. Petersburg: ELBI-SPb; 2008: 590–2, 616 (in Russ).

8. Aksenova SP, Nudnov NV, Kreynina YuM. Complex magnetic resonance imaging in differentiation of vaginal neoplastic and non-neoplastk lesions. Medical Visualization. 2015; 4: 131–41 (in Russ).

9. Elsayes KM, Narra VR, Dillman JR, et al. Vaginal masses: magnetic resonance imaging features with pathologic correlation. Acta Radiol 2007; 48(8): 921–33. https://doi.org/10.1080/02841850701552926.

10. Gardner CS, Sunil J, Klopp AH, et al. Primary vaginal cancer:role of MRI in diagnosis, staging and treatment. Br J Radiol. 2015; 88(1052): 20150033. https://doi.org/10.1259/bjr.20150033.

11. Taylor MB, Dugar N, Davidson SE, et al. Magnetic resonance imaging of primary vaginal carcinoma. Clin Radiol. 2007; 62(6): 549–55. https://doi.org/10.1016/j.crad.2007.01.008.

12. Chang YC, Hricak H, Thurnher S, Lacey CG. Vagina: evaluation with MR imaging. Part II. Neoplasms. Radiology. 1988; 169(1): 175–9. https://doi.org/10.1148/radiology.169.1.3420257.

13. Kreynina YuM, Nudnov NV, Aksenova SP. Multiparametric magnetic resonance imaging of the small pelvis organs in specifying diagnosis and monitoring of vaginal tumor brachytherapy. Difficult Patient. 2016; 14(2-3): 27–34 (in Russ).

14. Thoeny HC, Ross BD. Predicting and monitoring cancer treatment response with diffusion-weighted MRI. J Magn Reson Imaging. 2010; 32(1): 2–16. https://doi.org/10.1002/jmri.22167.

15. Aksenova SP, Solodkiy VA, Nudnov NV, et al. Magnetic resonance imaging in the diagnosis of tumor lesions of the vagina (based on guidelines). Bulletin of the Russian Scientific Center for Roentgen Radiology. 2022; 2: 26.

16. Nishie A, Stolpen AH, Obuchi M, et al. Evaluation of locally recurrent pelvic malignancy: performance of T2- and diffusionweighted MRI with image fusion. J Magn Reson Imaging. 2008; 28(3): 705–13. https://doi.org/10.1002/jmri.21486.

17. Kinkel K, Ariche M, Tardivon AA, et al. Differentiation between recurrent tumor and benign conditions after treatment of gynecologic pelvic carcinoma: value of dynamic contrast-enhanced subtraction MR imaging. Radiology. 1997; 204(1): 55–63. https://doi.org/10.1148/radiology.204.1.9205223.

18. Parikh JH, Barton DP, Ind TE, Sohaib SA. MR imaging features of vaginal malignancies. Radiographics. 2008; 28(1): 49–63. https://doi.org/10.1148/rg.281075065.

19. Siegelman ES, Outwater EK, Banner MP, et al High resolution MR imaging of the vagina. Radiographics. 1997; 17(5): 1183–203. https://doi.org/10.1148/radiographics.17.5.9308110.

20. Alt CD, Bharwani N, Brunesh L, et al. ESUR Quick Guide to Female Pelvis Imaging. ESUR; 2019.

21. Boss EA, Barentsz JO, Massuger LF, Boonstra H. The role of MR imaging in invasive cervical carcinoma. Eur Radiol. 2000; 10(2): 256–70. https://doi.org/10.1007/s003300050042.

22. Brown MA, Mattrey RF, Stamato S, Sirlin CB. MRI of the female pelvis using vaginal gel. AJR Am J Roentgenol. 2005; 185(5): 1221–7. https://doi.org/10.2214/AJR.04.1660.

23. Young P, Daniel B, Sommer G, et al. Intravaginal gel for staging of female pelvic cancers – preliminary report of safety, distention, and gel-mucosal contrast during magnetic resonance examination. J Comput Assist Tomogr. 2012; 36(2): 253–6. https://doi.org/10.1097/RCT.0b013e3182483c05.