Cross-Sectional Imaging of Pelvic Inflammatory Disease: Diagnostic Pearls and Pitfalls on CT and MR

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Abstract

Pelvic inflammatory disease (PID) encompasses a broad range of infection-induced inflammatory disorders of the female upper genital tract, commonly caused by ascending sexually transmitted infections. Diagnosis is often challenging because of nonspecific or absent symptoms and the overlap with other pelvic pathologies. While clinical and laboratory assessments are essential, cross-sectional imaging plays a pivotal role, especially in complicated, atypical, or equivocal cases. This review focuses on the typical and atypical imaging features of PID and highlights the crucial roles of computed tomography (CT) and magnetic resonance imaging (MRI) in its diagnostic evaluation. CT is frequently employed in emergency settings because of its widespread availability and ability to detect acute complications such as tubo-ovarian abscesses (TOA), peritonitis, or Fitz-Hugh–Curtis syndrome. However, it is limited by ionizing radiation and suboptimal soft-tissue contrast. MRI provides superior tissue characterization and multiplanar imaging without radiation exposure. When combined with diffusion-weighted imaging (DWI), MRI achieves high diagnostic accuracy, particularly in differentiating PID from other entities such as endometriosis, adnexal tumors, and gastrointestinal or urinary tract diseases. This review also addresses PID in specific clinical contexts, including post-partum infection, post-assisted reproductive technologies (ART), intrauterine device (IUD) use, and chronic or recurrent forms. A comprehensive, multimodal imaging approach integrated with clinical findings is essential for timely diagnosis, effective treatment, and prevention of severe reproductive sequelae.

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Gigli, S., Gennarini, M., Ninkova, R. V., Miceli, V., Curti, F., Riccardi, S., … Manganaro, L. (2025, August 1). Cross-Sectional Imaging of Pelvic Inflammatory Disease: Diagnostic Pearls and Pitfalls on CT and MR. Diagnostics. Multidisciplinary Digital Publishing Institute (MDPI). https://doi.org/10.3390/diagnostics15162001

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