title: Accuracy of diffusion-weighted (DW) MRI with background signal suppression (MR-DWIBS) in diagnosis of mediastinal lymph node metastasis of nonsmall-cell lung cancer (NSCLC).
abstract: OBJECTIVE
  To prospectively evaluate the accuracy of diffusion-weighted (DW) magnetic resonance (MR) imaging with background signal suppression (MR-DWIBS) for detecting mediastinal lymph node metastasis of nonsmall-cell lung cancer (NSCLC).
  
  
  METHODS
  MR-DWIBS was performed in 42 consecutive patients (27 men, 15 women; age range, 42-78 years; median age, 55 years) with histologically proven NSCLC. The visualization rate of metastatic lymph node (MLN) and benign lymph node (BLN) of enlarged lymph nodes (ELN) and normal-sized lymph nodes (NLN) was compared by using a chi-square test or Fisher's exact test on a per-nodal basis. Apparent diffusion coefficient (ADC) of MLN and BLN was measured and compared by using two-tailed unpaired Student's t-test. Receiver operating characteristic (ROC) analysis was used to assess the overall diagnostic accuracy of ADC for ELN and NLN. The optimal cutoff value was determined and the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy was calculated.
  
  
  RESULTS
  Thirty-five out of 119 lymph resected nodes were confirmed to be metastatic by histologic examination. The visualization rate of MLN was significantly higher than that of BLN for ELN (P < 0.001) and for NLN (χ(2) = 7.506, P = 0.006). For both ELN and NLN, ADC of MLN was significantly lower than that of BLN (t = -5.380, P < 0.001 and t = -6.435, P < 0.001). ADC was significant for detection of MLN for both ELN (Az = 0.975, P < 0.001) and NLN (Az = 0.919, P < 0.001). For NLN, the optimal cutoff value of ADC was 2.04 mm(2)/s, where the sensitivity, specificity, PPV, NPV, and accuracy were 75.0%, 90.9%, 66.7%, 93.8%, and 87.8%, respectively.
  
  
  CONCLUSIONS
  MR-DWIBS may be clinically useful to visually detect mediastinal lymph nodes and ADC measurement can aid in malignant node discrimination.
authors.affiliation: Department of Radiology, Shandong Cancer Hospital, Shandong Academy of Medical Sciences, Ji'nan, P.R. China.
authors.forename: Liang
authors.initials: L
authors.lastname: Xu
authors.forename: Jiakai
authors.initials: J
authors.lastname: Tian
authors.forename: Yuhui
authors.initials: Y
authors.lastname: Liu
authors.forename: Chuanfu
authors.initials: C
authors.lastname: Li
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D016477:Artifacts; D002289:Carcinoma, Non-Small-Cell Lung / Q000473:pathology* / Q000556:secondary*; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D008175:Lung Neoplasms / Q000473:pathology*; D008198:Lymph Nodes / Q000473:pathology*; D008207:Lymphatic Metastasis; D008297:Male; D008875:Middle Aged; D015588:Observer Variation; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D059629:Signal-To-Noise Ratio; D013382:Subtraction Technique
keywords: diffusion magnetic resonance imaging; lung neoplasms; lymphatic metastasis; magnetic resonance imaging; neoplasm metastasis