Diffusion-weighted imaging of peritoneal disease for noninvasive staging of advanced ovarian cancer

• Published in: Radiographics Vol. 30; no. 5; p. 1269
• Main Authors: Kyriazi, Stavroula, Collins, David J, Morgan, Veronica A, Giles, Sharon L, deSouza, Nandita M
• Format: Journal Article
• Language: English
• Published: United States 01.09.2010
• Subjects: Diagnosis, Differential; Diffusion Magnetic Resonance Imaging - methods; Female; Humans; Image Enhancement - methods; Neoplasm Staging - methods; Ovarian Neoplasms - diagnosis; Peritoneal Diseases - diagnosis
• ISSN: 1527-1323
• DOI: 10.1148/rg.305105073

Cross-sectional imaging of peritoneal carcinomatosis in patients with advanced ovarian cancer is important for appropriate management but can be compromised by the small size of cancer implants and the complexity of anatomic relationships. Diffusion-weighted imaging is a functional magnetic resonance (MR) imaging technique that exploits the restricted water mobility within hypercellular tumors to increase the contrast between these lesions and surrounding tissue. Its use improves the detection and delineation of peritoneal implants at both initial staging and follow-up. Moreover, diffusion-weighted imaging provides quantitative information about tissue cellularity that may be used to distinguish viable tumors from treatment-related changes. These data allow calculation of apparent diffusion coefficient (ADC) values, which, when considered in conjunction with biochemical and morphologic parameters, are helpful for assessing the effectiveness of treatment. The value of diffusion-weighted images is maximized when they are interpreted in comparison with anatomic MR images to avoid diagnostic pitfalls arising from normal hypercellular structures and neoplasms with low cellularity. When incorporating diffusion-weighted imaging into abdominal and pelvic MR studies, it is important to be aware of the strengths and limitations of the technique. Competence in data display methods and ADC calculations also helps improve the accuracy of image interpretation and may aid in the management of patients with advanced ovarian cancer.