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Clinical utility of the normalized apparent diffusion coefficient for preoperative evaluation of the aggressiveness of prostate cancer.

AbstractPURPOSE:
Normalization of the apparent diffusion coefficient (ADC) may overcome ADC variability attributable to different patient and/or technical factors. The purpose of this study was to compare the efficacy of ADC and the normalized ADC (nADC) for differentiating between prostate cancer with a Gleason score (GS) = 6 and GS > 6 and to identify an optimum reference for nADC calculations.
MATERIALS AND METHODS:
Our study population comprised 58 patients who underwent diffusion-weighted MRI followed by radical prostatectomy. The nADC of the prostate cancer was calculated as ADC (cancer)/ADC (reference) by using the obturator internus muscle, urine in the bladder, and a 20-ml saline bottle placed on the groin as references. We performed receiver operating characteristic (ROC) analysis to identify the optimum reference for nADC calculations.
RESULTS:
To differentiate between GS = 6 and GS > 6 prostate cancer, the area under the ROC curve of the nADC obtained with a saline bottle as reference was best (0.85) and significantly better than the area under the ADC ROC curve (0.71).
CONCLUSIONS:
nADC is superior to ADC for estimating the aggressiveness of prostate cancer. It is a noninvasive technique that aids in the selection of appropriate treatments.
AuthorsRyo Itatani, Tomohiro Namimoto, Akira Yoshimura, Kazuhiro Katahira, Seiichiro Noda, Nobuyuki Toyonari, Kosuke Kitani, Yasuyuki Hamada, Mitsuhiko Kitaoka, Yasuyuki Yamashita
JournalJapanese journal of radiology (Jpn J Radiol) Vol. 32 Issue 12 Pg. 685-91 (Dec 2014) ISSN: 1867-108X [Electronic] Japan
PMID25323833 (Publication Type: Journal Article)
Topics
  • Aged
  • Aged, 80 and over
  • Area Under Curve
  • Diffusion Magnetic Resonance Imaging
  • Humans
  • Male
  • Middle Aged
  • Neoplasm Grading
  • Preoperative Care
  • Prostate (pathology, surgery)
  • Prostatectomy
  • Prostatic Neoplasms (pathology, surgery)
  • ROC Curve
  • Reproducibility of Results

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