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Clinical Trial Details — Status: Not yet recruiting

Administrative data

NCT number NCT05695690
Other study ID # MRI and US in uterine fibroids
Secondary ID
Status Not yet recruiting
Phase
First received
Last updated
Start date May 2023
Est. completion date May 2024

Study information

Verified date April 2023
Source Assiut University
Contact Ashraqat Sherif
Phone 01062389453
Email ashrakat.gehad@gmail.com
Is FDA regulated No
Health authority
Study type Observational

Clinical Trial Summary

Evaluation of diagnostic accuracy of ultrasound in uterine fibroid characterization compared with MRI using FIGO classification


Description:

Uterine fibroids, also known as uterine leiomyomata or myomas, are the most common gynecologic tumors occurring in 20-30% of women of reproductive age and in up to 80% of all women. Leiomyomas have a wide spectrum of clinical presentations, but are mostly asymptomatic. Submucosal leiomyomas are more commonly associated with symptoms such as AUB, dysmenorrhea, infertility, and recurrent pregnancy loss. Subserosal leiomyomas can cause gastrointestinal or genitourinary symptoms related to size such as constipation, urinary retention, urinary frequency and urgency, or bladder and bowel incontinence. Fibroids are monoclonal smooth muscle tumors arising from the myometrium. While benign, their growth is dependent on estrogen and progesterone levels, and thus fibroids may enlarge with pregnancy and use of oral contraceptives and regress during menopause. Ultrasonography (USG) is the first-line imaging modality for any suspected structural anomaly of the female pelvis as well as the most appropriate imaging modality for abnormal uterine bleeding. USG has high sensitivity and specificity in diagnosing leiomyomas, but can vary with the experience of the operator performing the USG, ranging from 65 to 99%. Pelvic USG usually consists of a combined approach to include both transabdominal (TAS) and transvaginal (TVUS) imaging. TVUS is generally considered to be more sensitive and specific with greater contrast and spatial resolution when compared to TAS. TVUS is more useful in obese patients, patients with a large amount of bowel gas, and in patients with incomplete bladder filling, all of which lower the sensitivity of TAS. TAS, on the other hand, is helpful in females with a larger uterus that extends superiorly from the pelvis and cannot be completely visualized in the field of TVUS. TAS provides an anatomic overview to better estimate overall uterine size and visualize the fundus in such cases. For incomplete visualization or indeterminate US findings, MRI is recommended. MR imaging is considered to be superior to USG offering anatomic detail with increased sensitivity and specificity in mapping the location and size of leiomyomas prior to treatment. Previous studies have shown that MRI has the greatest advantage in diagnosing leiomyomas especially in cases of multiple leiomyomas (> 4) or a large volume uterus (> 375 mL). Through the use of diffusion weighted imaging MR may also help identify lesions with higher cellularity such as leiomyosarcomas. Despite these limitations, TVUS remains as efficient as MR in detecting the presence of leiomyomas and due to the lower cost, greater availability, and fewer contraindications, USG remains the first-line imaging modality for diagnosing the presence of leiomyomas. Furthermore, Baird et al. found that the presence of leiomyomas on a baseline ultrasound is strongly predictive of future uterine procedures approaching nearly 50% for those patients with a leiomyoma 4 cm or greater at baseline. Leiomyomas have traditionally been classified as submucosal, intramural, and subserosal. With the advancements in the treatment modalities, there was a need for a more detailed and universally accepted classification system to direct optimal treatment options. For this reason, the FIGO classification system was developed in 2011 for causes of AUB. The International Federation of Gynecology and Obstetrics (FIGO) classification system for abnormal uterine bleeding is intended to help both clinicians and researchers better categorize the causes of bleeding and plan treatments for the patient, be it hysteroscopy, laparoscopy/laparotomy, or UAE. Precise classification is also necessary in the post-treatment setting in order to assess treatment response, change in overall tumor burden and presence of recurrent lesions. The FIGO classification system subdivides fibroids into submucosal, other (intramural and subserosal), and hybrid types.


Recruitment information / eligibility

Status Not yet recruiting
Enrollment 43
Est. completion date May 2024
Est. primary completion date March 2024
Accepts healthy volunteers
Gender Female
Age group N/A and older
Eligibility Inclusion Criteria: - All females known or clinically suspected to have uterine fibroids & fit for the MRI examination. Exclusion Criteria: - Patients with metallic prosthesis which are not MRI compatible. - Patients refused the examination. - Patients who suffer from claustrophobia. - Pregnant women especially in the first trim

Study Design


Related Conditions & MeSH terms


Intervention

Device:
Ultrasonography
Comparing between value of ultrasound vs MRI in uterine fibroids

Locations

Country Name City State
n/a

Sponsors (1)

Lead Sponsor Collaborator
Assiut University

References & Publications (2)

Bajaj S, Gopal N, Clingan MJ, Bhatt S. A pictorial review of ultrasonography of the FIGO classification for uterine leiomyomas. Abdom Radiol (NY). 2022 Jan;47(1):341-351. doi: 10.1007/s00261-021-03283-6. Epub 2021 Sep 28. — View Citation

Gomez E, Nguyen MT, Fursevich D, Macura K, Gupta A. MRI-based pictorial review of the FIGO classification system for uterine fibroids. Abdom Radiol (NY). 2021 May;46(5):2146-2155. doi: 10.1007/s00261-020-02882-z. Epub 2021 Jan 1. — View Citation

Outcome

Type Measure Description Time frame Safety issue
Primary Comparison of MRI with Ultrasonography in characterization of uterine fibroids using FIGO classification system Evaluation of diagnostic accuracy of ultrasound in uterine fibroid characterization compared with MRI using FIGO classification Baseline
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