title: Quantitative analysis of multiparametric prostate MR images: differentiation between prostate cancer and normal tissue and correlation with Gleason score--a computer-aided diagnosis development study.
abstract: OBJECTIVE
  To evaluate the potential utility of a number of parameters obtained at T2-weighted, diffusion-weighted, and dynamic contrast material-enhanced multiparametric magnetic resonance (MR) imaging for computer-aided diagnosis (CAD) of prostate cancer and assessment of cancer aggressiveness.
  
  
  METHODS
  In this institutional review board-approved HIPAA-compliant study, multiparametric MR images were acquired with an endorectal coil in 48 patients with prostate cancer (median age, 62.5 years; age range, 44-73 years) who subsequently underwent prostatectomy. A radiologist and a pathologist identified 104 regions of interest (ROIs) (61 cancer ROIs, 43 normal ROIs) based on correlation of histologic and MR findings. The 10th percentile and average apparent diffusion coefficient (ADC) values, T2-weighted signal intensity histogram skewness, and Tofts K(trans) were analyzed, both individually and combined, via linear discriminant analysis, with receiver operating characteristic curve analysis with area under the curve (AUC) as figure of merit, to distinguish cancer foci from normal foci. Spearman rank-order correlation (ρ) was calculated between cancer foci Gleason score (GS) and image features.
  
  
  RESULTS
  AUC (maximum likelihood estimate ± standard error) values in the differentiation of prostate cancer from normal foci of 10th percentile ADC, average ADC, T2-weighted skewness, and K(trans) were 0.92 ± 0.03, 0.89 ± 0.03, 0.86 ± 0.04, and 0.69 ± 0.04, respectively. The combination of 10th percentile ADC, average ADC, and T2-weighted skewness yielded an AUC value for the same task of 0.95 ± 0.02. GS correlated moderately with 10th percentile ADC (ρ = -0.34, P = .008), average ADC (ρ = -0.30, P = .02), and K(trans) (ρ = 0.38, P = .004).
  
  
  CONCLUSIONS
  The combination of 10th percentile ADC, average ADC, and T2-weighted skewness with CAD is promising in the differentiation of prostate cancer from normal tissue. ADC image features and K(trans) moderately correlate with GS.
authors: Departments of Radiology, Pathology, and Surgery, Section of Urology, University of Chicago, 5841 S Maryland Ave, MC2026, Chicago, IL 60637, USA. yahuip@uchicago.edu
authors: Yahui
authors: Y
authors: Peng
authors: Yulei
authors: Y
authors: Jiang
authors: Cheng
authors: C
authors: Yang
authors: Jeremy Bancroft
authors: JB
authors: Brown
authors: Tatjana
authors: T
authors: Antic
authors: Ila
authors: I
authors: Sethi
authors: Christine
authors: C
authors: Schmid-Tannwald
authors: Maryellen L
authors: ML
authors: Giger
authors: Scott E
authors: SE
authors: Eggener
authors: Aytekin
authors: A
authors: Oto
mesh_terms: D000328:Adult; D000368:Aged; D019540:Area Under Curve; D001706:Biopsy; D003287:Contrast Media; D003936:Diagnosis, Computer-Assisted; D003937:Diagnosis, Differential; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D016002:Discriminant Analysis; D019786:Gadolinium DTPA; D006801:Humans; D008297:Male; D008875:Middle Aged; D011468:Prostatectomy; D011471:Prostatic Neoplasms / Q000473:pathology* / Q000601:surgery; D012372:ROC Curve; D012189:Retrospective Studies
chemical_list: D003287:Contrast Media; C064925:gadodiamide; D019786:Gadolinium DTPA
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