Diagnostic performance of nanoparticle-enhanced magnetic resonance Imaging in the diagnosis of lymph node metastases in patients with endometrial and cervical cancer

Purpose Lymph node metastases affect management and prognosis of patients with gynecologic malignancies. Preoperative nodal assessment with computed tomography or magnetic resonance imaging (MRI) is inaccurate. A new lymph node-specific contrast agent, ferumoxtran-10, composed of ultrasmall particles of iron oxide (USPIO), may enhance the detection of lymph node metastases independent of node size. Our aim was to compare the diagnostic performance of MRI with USPIO against standard size criteria. Methods Forty-four patients with endometrial (n = 15) or cervical (n = 29) cancer were included. MRI was performed before and after administration of USPIO. Two independent observers viewed the MR images before lymph node sampling. Lymph node metastases were predicted using size criteria and USPIO criteria Lymph node sampling was performed in all patients. Results Lymph node sampling provided 768 pelvic or para-aortic nodes for pathology, of which 335 were correlated on MRI; 17 malignant nodes were found in 11 of 44 patients (25 %). On a node-by-node basis, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) by size criteria were 29 %*, 99 %, 56 %, and 96 %, and by USPIO criteria (reader 1/reader 2) were 93 %/82 %* (*P = 008/ 004) 97 %/97 % 61 %/59 % and 100 %/99 %, respectively (where [*] indicates the statistical difference of P = x/x between the two results marked by the asterisk). On a patient-by-patient basis, sensitivity, specificity, PPV, and NPV by size criteria were 27 %*, 94 %, 60 %, and 79 %, and by USPIO criteria (reader 1/reader 2) were 100 %/91 %* (*P = .031/.06), 94 %/87 %, 82 %/71 %, and 100 %/ 96 %, respectively. The kappa statistic was 0.93. Conclusion Lymph node characterization with USPIO increases the sensitivity of MRI in the prediction of lymph node metastases, with no loss of specificity. This may greatly improve preoperative treatment planning.