title: Non-Gaussian diffusion imaging for malignant and benign pulmonary nodule differentiation: a preliminary study.
abstract: BACKGROUND
  Diffusion-weighted imaging (DWI) derived apparent diffusion coefficient (ADC) has demonstrated inconsistent results in pulmonary nodule differentiation. Diffusion kurtosis imaging (DKI), which quantifies non-Gaussian diffusion, is believed to better characterize tissue micro-structure than conventional DWI.
  
  
  OBJECTIVE
  To assess the feasibility of DKI in human lungs and to compare its diagnostic value with standard DWI in differentiating malignancies from benign pulmonary nodules.
  
  
  METHODS
  Thirty-five pulmonary nodules in 32 consecutive patients were evaluated by DKI by using 3b-values of 0, 500, and 1000 s/mm2and conventional DWI with b values of 0 and 800 s/mm2. Two observers independently evaluated and compared diagnostic accuracy of mean kurtosis (MK) and ADC values in differentiating malignancies from benign pulmonary nodules. The intra- and inter-observer repeatability (intra-class correlation coefficient [ICC]) were also assessed for each derived measures.
  
  
  RESULTS
  The diagnostic accuracy, and the area under curve (AUC) in differentiating malignancies from benign pulmonary nodule, were not significantly higher for MK (Obs. 1a: 85.70%, 0.87; Obs. 1b: 80.00%, 0.80; and Obs. 2: 82.80%, 0.91) as compared to ADC (Obs. 1a: 77.14%, 0.81; Obs. 1b: 80.00%, 0.85; and Obs. 2: 77.14%, 0.85 respectively). The intra- and inter-observer agreement (ICC) for malignant and benign lesions was substantial for each reading.
  
  
  CONCLUSIONS
  The initial results of this study indicate the feasibility of DKI in human lungs. However, there was no significant benefit of DKI derived MK values over ADC for malignant and benign pulmonary nodule differentiation.
authors.affiliation: Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Sichuan, PR China.
authors.forename: Sushant Kumar
authors.initials: SK
authors.lastname: Das
authors.affiliation: Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Sichuan, PR China.
authors.forename: Dong Jun
authors.initials: DJ
authors.lastname: Yang
authors.affiliation: Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Sichuan, PR China.
authors.forename: Jin Liang
authors.initials: JL
authors.lastname: Wang
authors.affiliation: Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Sichuan, PR China.
authors.forename: Chuan
authors.initials: C
authors.lastname: Zhang
authors.affiliation: Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Sichuan, PR China 782616733@qq.com.
authors.forename: Han Feng
authors.initials: HF
authors.lastname: Yang
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D005240:Feasibility Studies; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D010865:Pilot Projects; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D003074:Solitary Pulmonary Nodule / Q000000981:diagnostic imaging*; D016008:Statistical Distributions
keywords: Diffusion kurtosis imaging; lung cancer; non-Gaussian imaging; pulmonary nodule