Abstract
Purpose: To prospectively evaluate the diagnostic accuracy of 3.0-T magnetic resonance (MR) imaging in the detection of non-small cell lung cancer nodal metastasis, with histopathologic analysis as the reference standard. Materials and Methods: Institutional review board approval and informed consent were obtained. From July 2005 to May 2006, 113 patients (91 men, 22 women; age range, 34-82 years; mean age, 61 years) with non-small cell lung cancer underwent thoracic 3.0-T MR imaging followed by surgery or mediastinoscopy. The lymph node-to-tumor ratios (LTRs) of signal intensity and nodal morphologic characteristics (such as eccentric cortical thickening or obliteration of the fatty hilum) were assessed on T2-weighted triple-inversion black-blood fast spin-echo images. Nodal short-axis diameter was assessed on T1-weighted three-dimensional fast field-echo images. Receiver operating characteristic and multivariate logistic regression analyses were used for statistical evaluation. Results: The cutoff value (LTR > 0.84) proved to be most appropriate (area under the receiver operating characteristic curve = 0.735, P < .001) in the detection of a nodal metastasis. Of the various parameters examined, morphologic characteristics appeared to be the most significant (P < .001) parameters for depicting a malignant node (multivariate logistic regression analyses; odds ratio, 7.5). Nodal morphology was analyzed, and diagnostic sensitivity, specificity, and accuracy were 53% (39 of 74 nodal stations), 91% (453 of 496 nodal stations), and 86% (492 of 570 nodal stations), respectively. Conclusion: Morphologic details of lymph nodes on T2-weighted triple-inversion black-blood fast spin-echo MR images are significant for detection of mediastinal or hilar nodal metastasis at 3.0-T MR imaging.
| Original language | English |
|---|---|
| Pages (from-to) | 596-604 |
| Number of pages | 9 |
| Journal | Radiology |
| Volume | 246 |
| Issue number | 2 |
| DOIs | |
| State | Published - Feb 2008 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
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