title: Pilot study of a novel tool for input-free automated identification of transition zone prostate tumors using T2- and diffusion-weighted signal and textural features.
abstract: OBJECTIVE
  To present results of a pilot study to develop software that identifies regions suspicious for prostate transition zone (TZ) tumor, free of user input.
  
  
  METHODS
  Eight patients with TZ tumors were used to develop the model by training a Naïve Bayes classifier to detect tumors based on selection of most accurate predictors among various signal and textural features on T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) maps. Features tested as inputs were: average signal, signal standard deviation, energy, contrast, correlation, homogeneity and entropy (all defined on T2WI); and average ADC. A forward selection scheme was used on the remaining 20% of training set supervoxels to identify important inputs. The trained model was tested on a different set of ten patients, half with TZ tumors.
  
  
  RESULTS
  In training cases, the software tiled the TZ with 4 × 4-voxel "supervoxels," 80% of which were used to train the classifier. Each of 100 iterations selected T2WI energy and average ADC, which therefore were deemed the optimal model input. The two-feature model was applied blindly to the separate set of test patients, again without operator input of suspicious foci. The software correctly predicted presence or absence of TZ tumor in all test patients. Furthermore, locations of predicted tumors corresponded spatially with locations of biopsies that had confirmed their presence.
  
  
  CONCLUSIONS
  Preliminary findings suggest that this tool has potential to accurately predict TZ tumor presence and location, without operator input.
authors.affiliation: Department of Radiology, NYU Langone Medical Center, New York, New York, USA.
authors.forename: Joseph N
authors.initials: JN
authors.lastname: Stember
authors.forename: Fang-Ming
authors.initials: FM
authors.lastname: Deng
authors.forename: Samir S
authors.initials: SS
authors.lastname: Taneja
authors.forename: Andrew B
authors.initials: AB
authors.lastname: Rosenkrantz
mesh_terms: D000368:Aged; D000465:Algorithms; D001185:Artificial Intelligence; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D010865:Pilot Projects; D011471:Prostatic Neoplasms / Q000473:pathology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012984:Software; D012986:Software Validation
keywords: MRI; T2-weighted imaging; computer-aided detection; prostate cancer; transition zone