title: The utility of magnetic resonance imaging and spectroscopy for predicting insignificant prostate cancer: an initial analysis.
abstract: OBJECTIVE
  To design new models that combine clinical variables and biopsy data with magnetic resonance imaging (MRI) and MR spectroscopic imaging (MRSI) data, and assess their value in predicting the probability of insignificant prostate cancer.
  
  
  METHODS
  In all, 220 patients (cT stage T1c or T2a, prostate-specific antigen level <20 ng/mL, biopsy Gleason score 6) had MRI/MRSI before surgery and met the inclusion criteria for the study. The probability of insignificant cancer was recorded retrospectively and separately for MRI and combined MRI/MRSI on a 0-3 scale (0, definitely insignificant; - 3, definitely significant). Insignificant cancer was defined from surgical pathology as organ-confined cancer of </= 0.5 cm(3) with no poorly differentiated elements. The accuracy of predicting insignificant prostate cancer was assessed using areas under receiver operating characteristic curves (AUCs), for previously reported clinical models and for newly generated MR models combining clinical variables, and biopsy data with MRI data (MRI model) and MRI/MRSI data (MRI/MRSI model).
  
  
  RESULTS
  At pathology, 41% of patients had insignificant cancer; both MRI (AUC 0.803) and MRI/MRSI (AUC 0.854) models incorporating clinical, biopsy and MR data performed significantly better than the basic (AUC 0.574) and more comprehensive medium (AUC 0.726) clinical models. The P values for the differences between the models were: base vs medium model, <0.001; base vs MRI model, <0.001; base vs MRI/MRSI model, <0.001; medium vs MRI model, <0.018; medium vs MRI/MRSI model, <0.001.
  
  
  CONCLUSIONS
  The new MRI and MRI/MRSI models performed better than the clinical models for predicting the probability of insignificant prostate cancer. After appropriate validation, the new MRI and MRI/MRSI models might help in counselling patients who are considering choosing deferred therapy.
authors.affiliation: Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, NY, USA.
authors.forename: Amita
authors.initials: A
authors.lastname: Shukla-Dave
authors.forename: Hedvig
authors.initials: H
authors.lastname: Hricak
authors.forename: Michael W
authors.initials: MW
authors.lastname: Kattan
authors.forename: Darko
authors.initials: D
authors.lastname: Pucar
authors.forename: Kentaro
authors.initials: K
authors.lastname: Kuroiwa
authors.forename: Hui-Ni
authors.initials: HN
authors.lastname: Chen
authors.forename: Jessica
authors.initials: J
authors.lastname: Spector
authors.forename: Jason A
authors.initials: JA
authors.lastname: Koutcher
authors.forename: Kristen L
authors.initials: KL
authors.lastname: Zakian
authors.forename: Peter T
authors.initials: PT
authors.lastname: Scardino
mesh_terms: D000368:Aged; D001707:Biopsy, Needle; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000592:standards*; D009682:Magnetic Resonance Spectroscopy / Q000592:standards*; D008297:Male; D008875:Middle Aged; D009367:Neoplasm Staging / Q000379:methods / Q000592:standards; D011237:Predictive Value of Tests; D017430:Prostate-Specific Antigen / Q000097:blood; D011471:Prostatic Neoplasms / Q000473:pathology*; D012372:ROC Curve; D012189:Retrospective Studies
chemical_list: D017430:Prostate-Specific Antigen