The FIGO classification of uterine leiomyomas - a pictorial review
Number:28ESURABS0071
Type:Educational Electronic Presentation
Authors: Matilde Gonçalves, Pedro Alves, Mafalda Machado, Rute Martins, Ana Sofia Moreira
Keywords: FIGO classification, uterine leiomyomas, magnetic resonance
SECTIONS
Educational objective

Pictorial review the International Federation of Gynecology and Obstetrics (FIGO) categorization of leiomyomas on magnetic resonance imaging (MRI) to provide a standardized guide for radiology reporting.

Topic Review

Uterine leiomyomas are the most common pelvic tumors in females of reproductive age and a major source of health costs and morbidity.

While the majority of uterine leiomyomas are asymptomatic, being diagnosed incidentally on ultrasound, they can have multiple presentations depending on the number of lesions, their size and location. These presentations usually are gynecological (especially abnormal uterine bleeding), urinary and gastrointestinal symptoms. 

Uterine leiomyomas are also associated with infertility, and sometimes they can cause complications during pregnancy, such as abortion and preterm labor. 

Based on location, submucosal leiomyomas are the most commonly associated with irregular bleeding and large pedunculated fibroids can cause acute pelvic pain due torsion and infarction and some may be mistaken for adnexal...

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Figure 1. The FIGO leiomyoma subclassification system (A - Table, B - Schematic representation). Red - endometrium, pink - inner myometrium, brown - outer myometrium.
Figure 2. Axial oblique(A,C) and sagittal (B,D) T2-weighted images demonstrate one submucosal leiomyoma FIGO type 1 (yellow arrow), one submucosal FIGO type 3 (blue arrow) and three intramural leiomyoma FIGO type 4 (red arrows). E - representation of FIGO classification with colour scheme
Conclusions

Understanding The FIGO classification for uterine leiomyomas is important to standardize the radiologist’s report in order to improve clinical communication, to facilitate management of the patient, choosing the treatment that fits better with the overall clinical picture and also to enable scientific research.