Prostate cancer: Identification with combined diffusion-weighted MR imaging and 3D 1H MR spectroscopic imaging - Correlation with pathologic findings Journal Article

Authors: Mazaheri, Y.; Shukla-Dave, A.; Hricak, H.; Fine, S. W.; Zhang, J.; Inurrigarro, G.; Moskowitz, C. S.; Ishill, N. M.; Reuter, V. E.; Touijer, K.; Zakian, K. L.; Koutcher, J. A.
Article Title: Prostate cancer: Identification with combined diffusion-weighted MR imaging and 3D 1H MR spectroscopic imaging - Correlation with pathologic findings
Abstract: Purpose: To retrospectively measure the mean apparent diffusion coefficient (ADC) with diffusion-weighted magnetic resonance (MR) imaging and the mean metabolic ratio (MET) with three-dimensional (3D) hydrogen 1 (1H) MR spectroscopic imaging in regions of interest (ROIs) drawn over benign and malignant peripheral zone (PZ) prostatic tissue and to assess ADC, MET, and combined ADC and MET for identifying malignant ROIs, with whole-mount histopathologic examination as the reference standard. Materials and Methods: The institutional review board approved this HIPAA-compliant retrospective study and issued a waiver of informed consent. From among 61 consecutive patients with prostate cancer, 38 men (median age, 61 years; range, 42-72 years) who underwent 1.5-T endorectal MR imaging before radical prostatectomy and who fulfilled all inclusion criteria of no prior hormonal or radiation treatment and at least one PZ lesion (volume, >0.1 cm3) at whole-mount pathologic examination were included. ADC maps were generated from diffusion-weighted MR imaging data, and MET maps of (choline plus polyamine plus creatine)/citrate were calculated from 3D 1H MR spectroscopic imaging data. ROIs in the PZ identified by matching pathologic slides with T2-weighted images were overlaid on MET and ADC maps. Areas under the receiver operating characteristic curves (AUCs) were used to evaluate accuracy. Results: The mean ADC ± standard deviation, (1.39 ± 0.23) × 10 -3 mm2/sec, and mean MET (0.92 ± 0.32) for malignant ROIs differed significantly from the mean ADC, (1.69 ± 0.24) × 10-3 mm2/sec, and mean MET (0.73 ± 0.18) for benign ROIs (P < .001 for both). In distinguishing malignant ROIs, combined ADC and MET (AUC = 0.85) performed significantly better than MET alone (AUC = 0.74; P = .005) and was also better than ADC alone (AUC = 0.81), although the difference was not statistically significant (P = .09). Conclusion: The combination of ADC and MET performs significantly better than MET for differentiating between benign and malignant ROIs in the PZ. © RSNA, 2008.
Keywords: adult; human tissue; aged; middle aged; major clinical study; histopathology; cancer radiotherapy; comparative study; nuclear magnetic resonance imaging; methodology; magnetic resonance imaging; cancer diagnosis; diagnostic accuracy; sensitivity and specificity; reproducibility; reproducibility of results; metabolism; statistics; image analysis; clinical assessment; tumor markers, biological; creatinine; retrospective study; tumor marker; cancer hormone therapy; prostate cancer; prostatic neoplasms; diagnostic agent; computer assisted diagnosis; evaluation; prostatectomy; three dimensional imaging; prostate tumor; statistics as topic; magnetic resonance spectroscopy; choline; citric acid; nuclear magnetic resonance spectroscopy; protons; diffusion weighted imaging; diffusion magnetic resonance imaging; diffusion coefficient; proton nuclear magnetic resonance; receiver operating characteristic; proton; diagnosis, computer-assisted; calculation; polyamine
Journal Title: Radiology
Volume: 246
Issue: 2
ISSN: 0033-8419
Publisher: Radiological Society of North America, Inc.  
Date Published: 2008-02-01
Start Page: 480
End Page: 488
Language: English
DOI: 10.1148/radiol.2462070368
PUBMED: 18227542
PROVIDER: scopus
Notes: --- - "Cited By (since 1996): 79" - "Export Date: 17 November 2011" - "CODEN: RADLA" - "Source: Scopus"
Citation Impact
MSK Authors
  1. Chaya S. Moskowitz
    235 Moskowitz
  2. Karim Abdelkrim Touijer
    237 Touijer
  3. Jingbo Zhang
    37 Zhang
  4. Hedvig Hricak
    401 Hricak
  5. Amita Dave
    117 Dave
  6. Kristen L Zakian
    82 Zakian
  7. Jason A Koutcher
    268 Koutcher
  8. Samson W Fine
    420 Fine
  9. Victor Reuter
    1181 Reuter
  10. Nicole Marie Leoce
    86 Leoce