title: Automated prostate cancer detection using T2-weighted and high-b-value diffusion-weighted magnetic resonance imaging.
abstract: OBJECTIVE
  The authors propose a computer-aided diagnosis (CAD) system for prostate cancer to aid in improving the accuracy, reproducibility, and standardization of multiparametric magnetic resonance imaging (MRI).
  
  
  METHODS
  The proposed system utilizes two MRI sequences [T2-weighted MRI and high-b-value (b = 2000 s/mm(2)) diffusion-weighted imaging (DWI)] and texture features based on local binary patterns. A three-stage feature selection method is employed to provide the most discriminative features. The authors included a total of 244 patients. Training the CAD system on 108 patients (78 MR-positive prostate cancers and 105 benign MR-positive lesions), two validation studies were retrospectively performed on 136 patients (68 MR-positive prostate cancers, 111 benign MR-positive lesions, and 117 MR-negative benign lesions).
  
  
  RESULTS
  In distinguishing cancer from MR-positive benign lesions, an area under receiver operating characteristic curve (AUC) of 0.83 [95% confidence interval (CI): 0.76-0.89] was achieved. For cancer vs MR-positive or MR-negative benign lesions, the authors obtained an AUC of 0.89 AUC (95% CI: 0.84-0.93). The performance of the CAD system was not dependent on the specific regions of the prostate, e.g., a peripheral zone or transition zone. Moreover, the CAD system outperformed other combinations of MRI sequences: T2W MRI, high-b-value DWI, and the standard apparent diffusion coefficient (ADC) map of DWI.
  
  
  CONCLUSIONS
  The novel CAD system is able to detect the discriminative texture features for cancer detection and localization and is a promising tool for improving the quality and efficiency of prostate cancer diagnosis.
authors.affiliation: Center for Interventional Oncology, Clinical Center, National Institutes of Health, Bethesda, Maryland 20892.
authors.forename: Jin Tae
authors.initials: JT
authors.lastname: Kwak
authors.affiliation: Center for Interventional Oncology, Clinical Center, National Institutes of Health, Bethesda, Maryland 20892.
authors.forename: Sheng
authors.initials: S
authors.lastname: Xu
authors.affiliation: Center for Interventional Oncology, Clinical Center, National Institutes of Health, Bethesda, Maryland 20892.
authors.forename: Bradford J
authors.initials: BJ
authors.lastname: Wood
authors.affiliation: Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892.
authors.forename: Baris
authors.initials: B
authors.lastname: Turkbey
authors.affiliation: Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892.
authors.forename: Peter L
authors.initials: PL
authors.lastname: Choyke
authors.affiliation: Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892.
authors.forename: Peter A
authors.initials: PA
authors.lastname: Pinto
authors.affiliation: Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Clinical Center, National Institutes of Health, Bethesda, Maryland 20892.
authors.forename: Shijun
authors.initials: S
authors.lastname: Wang
authors.affiliation: Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Clinical Center, National Institutes of Health, Bethesda, Maryland 20892.
authors.forename: Ronald M
authors.initials: RM
authors.lastname: Summers
mesh_terms: D019540:Area Under Curve; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D011467:Prostate / Q000473:pathology*; D011471:Prostatic Neoplasms / Q000175:diagnosis* / Q000473:pathology*; D012372:ROC Curve; D012189:Retrospective Studies