title: Development and validation of a logistic regression model to distinguish transition zone cancers from benign prostatic hyperplasia on multi-parametric prostate MRI.
abstract: OBJECTIVE
  To develop a prediction model to distinguish between transition zone (TZ) cancers and benign prostatic hyperplasia (BPH) on multi-parametric prostate magnetic resonance imaging (mp-MRI).
  
  
  METHODS
  This retrospective study enrolled 60 patients with either BPH or TZ cancer, who had undergone 3 T-MRI. We generated ten parameters for T2-weighted images (T2WI), diffusion-weighted images (DWI) and dynamic MRI. Using a t-test and multivariate logistic regression (LR) analysis to evaluate the parameters' accuracy, we developed LR models. We calculated the area under the receiver operating characteristic curve (ROC) of LR models by a leave-one-out cross-validation procedure, and the LR model's performance was compared with radiologists' performance with their opinion and with the Prostate Imaging Reporting and Data System (Pi-RADS v2) score.
  
  
  RESULTS
  Multivariate LR analysis showed that only standardized T2WI signal and mean apparent diffusion coefficient (ADC) maintained their independent values (P < 0.001). The validation analysis showed that the AUC of the final LR model was comparable to that of board-certified radiologists, and superior to that of Pi-RADS scores.
  
  
  CONCLUSIONS
  A standardized T2WI and mean ADC were independent factors for distinguishing between BPH and TZ cancer. The performance of the LR model was comparable to that of experienced radiologists.
  
  
  CONCLUSIONS
  • It is difficult to diagnose transition zone (TZ) cancer. • We performed quantitative image analysis in multi-parametric MRI. • Standardized-T2WI and mean-ADC were independent factors for diagnosing TZ cancer. • We developed logistic-regression analysis to diagnose TZ cancer accurately. • The performance of the logistic-regression analysis was higher than PIRADSv2.
authors.affiliation: Department of Diagnostic Radiology, Kumamoto Chuo Hospital, Tainoshima 1-5-1, Kumamoto, Kumamoto, 862-0965, Japan. iyamayuuji28@gmail.com.|Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Honjo 1-1-1, Kumamoto, Kumamoto, 860-8556, Japan. iyamayuuji28@gmail.com.
authors.forename: Yuji
authors.initials: Y
authors.lastname: Iyama
authors.affiliation: Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Honjo 1-1-1, Kumamoto, Kumamoto, 860-8556, Japan.
authors.forename: Takeshi
authors.initials: T
authors.lastname: Nakaura
authors.affiliation: Department of Diagnostic Radiology, Kumamoto Chuo Hospital, Tainoshima 1-5-1, Kumamoto, Kumamoto, 862-0965, Japan.
authors.forename: Kazuhiro
authors.initials: K
authors.lastname: Katahira
authors.affiliation: Department of Diagnostic Radiology, National Hospital Organization Kumamoto Medical Center, Ninomaru 1-5, Kumamoto, Kumamoto, 860-0008, Japan.
authors.forename: Ayumi
authors.initials: A
authors.lastname: Iyama
authors.affiliation: Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Honjo 1-1-1, Kumamoto, Kumamoto, 860-8556, Japan.
authors.forename: Yasunori
authors.initials: Y
authors.lastname: Nagayama
authors.affiliation: Department of Diagnostic Radiology, Kumamoto Chuo Hospital, Tainoshima 1-5-1, Kumamoto, Kumamoto, 862-0965, Japan.
authors.forename: Seitaro
authors.initials: S
authors.lastname: Oda
authors.affiliation: Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Honjo 1-1-1, Kumamoto, Kumamoto, 860-8556, Japan.
authors.forename: Daisuke
authors.initials: D
authors.lastname: Utsunomiya
authors.affiliation: Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Honjo 1-1-1, Kumamoto, Kumamoto, 860-8556, Japan.
authors.forename: Yasuyuki
authors.initials: Y
authors.lastname: Yamashita
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D003937:Diagnosis, Differential; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D016015:Logistic Models; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging / Q000379:methods; D008297:Male; D008875:Middle Aged; D011468:Prostatectomy; D011470:Prostatic Hyperplasia / Q000000981:diagnostic imaging* / Q000473:pathology / Q000601:surgery; D011471:Prostatic Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology / Q000601:surgery; D012372:ROC Curve; D012189:Retrospective Studies; D012680:Sensitivity and Specificity
keywords: Logistic models; Machine learning; Magnetic resonance imaging; Prostate; Prostatic neoplasms