ABSTRACT
PURPOSE: To characterize benign and malignant prostate peripheral zone (PZ) tissue retrospectively by using a commercial magnetic resonance (MR) spectroscopic imaging package and incorporating the choline plus creatine-to-citrate ratio ([Cho + Cr]/Cit) and polyamine (PA) information into a statistically based voxel classification procedure. MATERIALS AND METHODS: The institutional review board approved this HIPAA-compliant study and waived the requirement for informed consent. Fifty men (median age, 60 years; range, 44-69 years) with untreated biopsy-proved prostate cancer underwent combined endorectal MR imaging and MR spectroscopic imaging. Commercial software was used to acquire and process MR spectroscopic imaging data. The (Cho + Cr)/Cit and the PA level were tabulated for each voxel. The PA level was scored on a scale of 0 (PA undetectable) to 2 (PA peak as high as or higher than Cho peak). Whole-mount step-section histopathologic analysis constituted the reference standard. Classification and regression tree analysis in a training set generated a decision-making tree (rule) for classifying voxels as malignant or benign, which was validated in a test set. Receiver operating characteristic and generalized estimating equation regression analyses were used to assess accuracy and sensitivity, respectively. RESULTS: The median (Cho + Cr)/Cit was 0.55 (mean +/- standard deviation, 0.59 +/- 0.03) in benign and 0.77 (mean, 1.08 +/- 0.20) in malignant PZ voxels (P = .027). A significantly higher percentage of benign (compared with malignant) voxels had higher PA than choline peaks (P < .001). In the 24-patient training set (584 voxels), the rule yielded 54% sensitivity and 91% specificity for cancer detection; in the 26-patient test set (667 voxels), it yielded 42% sensitivity and 85% specificity. The percentage of cancer in the voxel at histopathologic analysis correlated positively (P < .001) with the sensitivity of the classification and regression tree rule, which was 75% in voxels with more than 90% malignancy. CONCLUSION: The statistically based classification rule developed indicated that PAs have an important role in the detection of PZ prostate cancer. With commercial software, this method can be applied in clinical settings.
Subject(s)
Biomarkers, Tumor/analysis , Diagnosis, Computer-Assisted/methods , Magnetic Resonance Imaging/methods , Magnetic Resonance Spectroscopy/methods , Polyamines/analysis , Prostatic Neoplasms/diagnosis , Prostatic Neoplasms/metabolism , Adult , Aged , Algorithms , Humans , Male , Middle Aged , Reproducibility of Results , Sensitivity and SpecificityABSTRACT
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. PATIENTS AND 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
