title: Logistic regression model for diagnosis of transition zone prostate cancer on multi-parametric MRI.
abstract: OBJECTIVE
  We aimed to develop logistic regression (LR) models for classifying prostate cancer within the transition zone on multi-parametric magnetic resonance imaging (mp-MRI).
  
  
  METHODS
  One hundred and fifty-five patients (training cohort, 70 patients; temporal validation cohort, 85 patients) underwent mp-MRI and transperineal-template-prostate-mapping (TPM) biopsy. Positive cores were classified by cancer definitions: (1) any-cancer; (2) definition-1 [≥Gleason 4 + 3 or ≥ 6 mm cancer core length (CCL)] [high risk significant]; and (3) definition-2 (≥Gleason 3 + 4 or ≥ 4 mm CCL) cancer [intermediate-high risk significant]. For each, logistic-regression mp-MRI models were derived from the training cohort and validated internally and with the temporal cohort. Sensitivity/specificity and the area under the receiver operating characteristic (ROC-AUC) curve were calculated. LR model performance was compared to radiologists' performance.
  
  
  RESULTS
  Twenty-eight of 70 patients from the training cohort, and 25/85 patients from the temporal validation cohort had significant cancer on TPM. The ROC-AUC of the LR model for classification of cancer was 0.73/0.67 at internal/temporal validation. The radiologist A/B ROC-AUC was 0.65/0.74 (temporal cohort). For patients scored by radiologists as Prostate Imaging Reporting and Data System (Pi-RADS) score 3, sensitivity/specificity of radiologist A 'best guess' and LR model was 0.14/0.54 and 0.71/0.61, respectively; and radiologist B 'best guess' and LR model was 0.40/0.34 and 0.50/0.76, respectively.
  
  
  CONCLUSIONS
  LR models can improve classification of Pi-RADS score 3 lesions similar to experienced radiologists.
  
  
  CONCLUSIONS
  • MRI helps find prostate cancer in the anterior of the gland • Logistic regression models based on mp-MRI can classify prostate cancer • Computers can help confirm cancer in areas doctors are uncertain about.
authors.affiliation: Centre for Medical Imaging, University College London, 3rd Floor East Wing, 250 Euston Road, London, UK, NW1 2PG.
authors.forename: Nikolaos
authors.initials: N
authors.lastname: Dikaios
authors.forename: Jokha
authors.initials: J
authors.lastname: Alkalbani
authors.forename: Harbir Singh
authors.initials: HS
authors.lastname: Sidhu
authors.forename: Taiki
authors.initials: T
authors.lastname: Fujiwara
authors.forename: Mohamed
authors.initials: M
authors.lastname: Abd-Alazeez
authors.forename: Alex
authors.initials: A
authors.lastname: Kirkham
authors.forename: Clare
authors.initials: C
authors.lastname: Allen
authors.forename: Hashim
authors.initials: H
authors.lastname: Ahmed
authors.forename: Mark
authors.initials: M
authors.lastname: Emberton
authors.forename: Alex
authors.initials: A
authors.lastname: Freeman
authors.forename: Steve
authors.initials: S
authors.lastname: Halligan
authors.forename: Stuart
authors.initials: S
authors.lastname: Taylor
authors.forename: David
authors.initials: D
authors.lastname: Atkinson
authors.forename: Shonit
authors.initials: S
authors.lastname: Punwani
mesh_terms: D000328:Adult; D000368:Aged; D001706:Biopsy / Q000379:methods; D003937:Diagnosis, Differential; D006801:Humans; D016015:Logistic Models; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D011467:Prostate / Q000473:pathology*; D011471:Prostatic Neoplasms / Q000175:diagnosis*; D012372:ROC Curve; D015203:Reproducibility of Results