Diffusion weighted MR accurately identifies gynecologic malignancies

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Diffusion weighted MR can accurately identify benign from malignant pelvic lymph nodes in patients with gynecologic malignancy, according to a study performed at Massachusetts General Hospital in Boston, MA.

Diffusion weighted imaging is a noninvasive test that uses an MRI sequence sensitized to the motion of water molecules within tissue.

"Conventional imaging with CT or MRI uses the size of a lymph node to determine whether it is malignant. Unfortunately, lymph node assessment by size criteria and morphology is not very accurate in detecting metastasis," said Michael Chew, MD, lead author of the study. This study included pelvic MRI and DWI results of 40 patients with gynecological malignancy. "Our study suggests that DWI can predict lymph node involvement by a tumor with a sensitivity of 92%, specificity of 85% and accuracy of 87%. It has a negative predictive value of 97%, so when DWI determines that a node is not involved by a tumor, it is almost always correct," he said.

"The choice of correct treatment for patients with gynecological malignancy is often determined by whether the cancer has spread to the nodes. DWI can help to depict gynecologic malignancy more clearly and provide useful information regarding lymph node metastasis so that patients are directed to the appropriate treatment," said Dr. Chew.

"It is noninvasive, safe and does not involve radiation exposure or the injection of contrast agents. It is a simple method for defining both primary neoplasm and lymph node spread, helping the oncologist decide between surgery and chemoradiation, or to define the extent of surgery that needs to be performed. It also aids in assessing prognosis," he said.

"Our results are based on a small series of patients at a single institution. The next step is to carry out larger studies that include more patients and institutions. The long-term goal is to improve the care of cancer patients by developing more accurate imaging," said Dr. Chew.

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