title: Haralick texture analysis of prostate MRI: utility for differentiating non-cancerous prostate from prostate cancer and differentiating prostate cancers with different Gleason scores.
abstract: OBJECTIVE
  To investigate Haralick texture analysis of prostate MRI for cancer detection and differentiating Gleason scores (GS).
  
  
  METHODS
  One hundred and forty-seven patients underwent T2- weighted (T2WI) and diffusion-weighted prostate MRI. Cancers ≥0.5 ml and non-cancerous peripheral (PZ) and transition (TZ) zone tissue were identified on T2WI and apparent diffusion coefficient (ADC) maps, using whole-mount pathology as reference. Texture features (Energy, Entropy, Correlation, Homogeneity, Inertia) were extracted and analysed using generalized estimating equations.
  
  
  RESULTS
  PZ cancers (n = 143) showed higher Entropy and Inertia and lower Energy, Correlation and Homogeneity compared to non-cancerous tissue on T2WI and ADC maps (p-values: <.0001-0.008). In TZ cancers (n = 43) we observed significant differences for all five texture features on the ADC map (all p-values: <.0001) and for Correlation (p = 0.041) and Inertia (p = 0.001) on T2WI. On ADC maps, GS was associated with higher Entropy (GS 6 vs. 7: p = 0.0225; 6 vs. >7: p = 0.0069) and lower Energy (GS 6 vs. 7: p = 0.0116, 6 vs. >7: p = 0.0039). ADC map Energy (p = 0.0102) and Entropy (p = 0.0019) were significantly different in GS ≤3 + 4 versus ≥4 + 3 cancers; ADC map Entropy remained significant after controlling for the median ADC (p = 0.0291).
  
  
  CONCLUSIONS
  Several Haralick-based texture features appear useful for prostate cancer detection and GS assessment.
  
  
  CONCLUSIONS
  • Several Haralick texture features may differentiate non-cancerous and cancerous prostate tissue. • Tumour Energy and Entropy on ADC maps correlate with Gleason score. • T2w-image-derived texture features are not associated with the Gleason score.
authors.affiliation: Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, 10065, New York City, NY, USA, a.wibmer@gmx.net.
authors.forename: Andreas
authors.initials: A
authors.lastname: Wibmer
authors.forename: Hedvig
authors.initials: H
authors.lastname: Hricak
authors.forename: Tatsuo
authors.initials: T
authors.lastname: Gondo
authors.forename: Kazuhiro
authors.initials: K
authors.lastname: Matsumoto
authors.forename: Harini
authors.initials: H
authors.lastname: Veeraraghavan
authors.forename: Duc
authors.initials: D
authors.lastname: Fehr
authors.forename: Junting
authors.initials: J
authors.lastname: Zheng
authors.forename: Debra
authors.initials: D
authors.lastname: Goldman
authors.forename: Chaya
authors.initials: C
authors.lastname: Moskowitz
authors.forename: Samson W
authors.initials: SW
authors.lastname: Fine
authors.forename: Victor E
authors.initials: VE
authors.lastname: Reuter
authors.forename: James
authors.initials: J
authors.lastname: Eastham
authors.forename: Evis
authors.initials: E
authors.lastname: Sala
authors.forename: Hebert Alberto
authors.initials: HA
authors.lastname: Vargas
mesh_terms: D000328:Adult; D000368:Aged; D003937:Diagnosis, Differential; D038524:Diffusion Magnetic Resonance Imaging; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D008297:Male; D008875:Middle Aged; D060787:Neoplasm Grading; D011468:Prostatectomy; D011471:Prostatic Neoplasms / Q000473:pathology* / Q000601:surgery; D012189:Retrospective Studies; D047368:Tumor Burden