title: Zone-specific logistic regression models improve classification of prostate cancer on multi-parametric MRI.
abstract: OBJECTIVE
  To assess the interchangeability of zone-specific (peripheral-zone (PZ) and transition-zone (TZ)) multiparametric-MRI (mp-MRI) logistic-regression (LR) models for classification of prostate cancer.
  
  
  METHODS
  Two hundred and thirty-one patients (70 TZ training-cohort; 76 PZ training-cohort; 85 TZ temporal validation-cohort) underwent mp-MRI and transperineal-template-prostate-mapping biopsy. PZ and TZ uni/multi-variate mp-MRI LR-models for classification of significant cancer (any cancer-core-length (CCL) with Gleason > 3 + 3 or any grade with CCL ≥ 4 mm) were derived from the respective cohorts and validated within the same zone by leave-one-out analysis. Inter-zonal performance was tested by applying TZ models to the PZ training-cohort and vice-versa. Classification performance of TZ models for TZ cancer was further assessed in the TZ validation-cohort. ROC area-under-curve (ROC-AUC) analysis was used to compare models.
  
  
  RESULTS
  The univariate parameters with the best classification performance were the normalised T2 signal (T2nSI) within the TZ (ROC-AUC = 0.77) and normalized early contrast-enhanced T1 signal (DCE-nSI) within the PZ (ROC-AUC = 0.79). Performance was not significantly improved by bi-variate/tri-variate modelling. PZ models that contained DCE-nSI performed poorly in classification of TZ cancer. The TZ model based solely on maximum-enhancement poorly classified PZ cancer.
  
  
  CONCLUSIONS
  LR-models dependent on DCE-MRI parameters alone are not interchangable between prostatic zones; however, models based exclusively on T2 and/or ADC are more robust for inter-zonal application.
  
  
  CONCLUSIONS
  • The ADC and T2-nSI of benign/cancer PZ are higher than benign/cancer TZ. • DCE parameters are significantly different between benign PZ and TZ, but not between cancerous PZ and TZ. • Diagnostic models containing contrast enhancement parameters have reduced performance when applied across zones.
authors: Centre for Medical Imaging, University College London, Level 3 East, 250 Euston Road, London, NW1 2PG, UK.
authors: Nikolaos
authors: N
authors: Dikaios
authors: Jokha
authors: J
authors: Alkalbani
authors: Mohamed
authors: M
authors: Abd-Alazeez
authors: Harbir Singh
authors: HS
authors: Sidhu
authors: Alex
authors: A
authors: Kirkham
authors: Hashim U
authors: HU
authors: Ahmed
authors: Mark
authors: M
authors: Emberton
authors: Alex
authors: A
authors: Freeman
authors: Steve
authors: S
authors: Halligan
authors: Stuart
authors: S
authors: Taylor
authors: David
authors: D
authors: Atkinson
authors: Shonit
authors: S
authors: Punwani
mesh_terms: D000328:Adult; D000368:Aged; D019540:Area Under Curve; D001706:Biopsy; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016015:Logistic Models; D008279:Magnetic Resonance Imaging / Q000706:statistics & numerical data*; D008297:Male; D008875:Middle Aged; D011467:Prostate / Q000473:pathology; D011471:Prostatic Neoplasms / Q000175:diagnosis* / Q000473:pathology; D012372:ROC Curve; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012680:Sensitivity and Specificity