Abstract

To evaluate short inversion time inversion-recovery (STIR) turbo spin-echo (TSE) magnetic resonance (MR) imaging for detection of metastases in lymph nodes by using quantitative and qualitative analyses. One hundred ten patients (68 men and 42 women) with non-small cell lung cancer who ranged in age from 36 to 82 years (mean age, 64 years) were examined with respiratory-triggered STIR TSE MR imaging. Ratios of signal intensity in a lymph node to that in a 0.9% saline phantom (lymph node-saline ratios [LSRs]) for all lymph nodes were classified into three groups according to nodal short-axis diameter. LSRs of each group were compared by using pathologic diagnosis as the standard of reference. For quantitative analysis, the LSR threshold value for a positive test was determined on a per-node basis and tested for ability to enable a correct diagnosis on a per-patient basis. For qualitative analysis, signal intensities of lymph nodes were assessed by using a five-point visual scoring system. Results of quantitative and qualitative analyses were compared on a per-patient basis with McNemar testing. In 110 patients, 92 of 802 lymph nodes were pathologically diagnosed as containing metastases, while 710 lymph nodes did not contain metastases. Mean LSR in the lymph node group with metastasis was higher than that in the group without metastasis (P <.05). When an LSR of 0.6 was used as the positive-test threshold at quantitative analysis, sensitivity was 93% (37 of 40 patients) and specificity was 87% (61 of 70 patients) on a per-patient basis. With a score of 4 as the positive-test threshold at qualitative analysis, sensitivity was 88% (35 of 40 patients) and specificity was 86% (60 of 70 patients) on a per-patient basis. There was no significant difference (P >.05) between results of quantitative and those of qualitative analysis. Quantitative and qualitative analyses of STIR TSE MR images enable differentiation of lymph nodes with metastasis from those without. Qualitative analysis can substitute for quantitative analysis of STIR TSE MR imaging data.

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