The Role of Magnetic Resonance Pulse Sequences in the Diagnosis of Acute Appendicitis in Pregnant Women.

Abstract

Acute appendicitis (AA) is one of the most common surgical emergencies, with a lifetime risk estimated at 7-8%. Pregnant women with appendicitis can have a difficult diagnosis becausemany signs and symptoms could overlap with other causes of acute abdominal pain. Although magnetic resonance imaging (MRI) is not contraindicated at all gestational ages for units with a field strength of three Tesla or less, there is still much discussion regarding the best protocol to follow in order to minimize survey time and maximize diagnostic efficiency. The purpose of this study was to assess how well different MR pulse sequences can diagnose AA. This retrospective study involved 179 pregnant females. All patients treated and admitted to the University Medical Center, Ho Chi Minh City, Vietnam, between January 2016 and October 2023 had their MR scans and medical data examined. MRI results were assessed and compared with surgical and histopathological findings. The mean age of the population was 29.7 ± 4.8 years (range, 18-46 years). On T1-weighted (T1W) and T2-weighted (T2W) sequences, the appendix was clearly visualized at rates of 81.8% and 89.9%, respectively. The sensitivity and specificity of the T2W in diagnosing AA were 93.5% and 92.3%, and when combined with T1W and diffusion-weighted (DW) images, the sensitivity and specificity further increased, being 96.8% and 94.9%, respectively. The predictive value of non-AA of the T1 bright appendix sign was 95.6%. Our study supports the use of MRI as an imaging test to identify appendicitis during pregnancy, as it has been shown to be a useful method for diagnosing the condition in pregnant women. The T2W pulse sequence is a useful tool for diagnosing appendicitis because of its high sensitivity and specificity. When identifying appendicitis from T2W alone proves challenging, T1W with the T1 bright sign and DW to take advantage of the appendix lumen and/or wall's diffusion features can yield additional information and boost diagnostic confidence.