Literature DB >> 24924835

Diffusion kurtosis imaging study of prostate cancer: preliminary findings.

Chiharu Tamura1, Hiroshi Shinmoto, Shigeyoshi Soga, Teppei Okamura, Hiroki Sato, Tomoyuki Okuaki, Yuxi Pang, Shigeru Kosuda, Tatsumi Kaji.   

Abstract

PURPOSE: To evaluate the differences in parameters of diffusion kurtosis imaging (DKI) between prostate cancer, benign prostatic hyperplasia (BPH), and benign peripheral zone (PZ).
MATERIALS AND METHODS: Twenty-four foci of prostate cancer, 41 BPH nodules (14 stromal and 27 nonstromal hyperplasia), and 20 benign PZ from 20 patients who underwent radical prostatectomy were investigated. Diffusion-weighted imaging (DWI) was performed using 11 b-values (0-1500 s/mm(2) ). DKI model relates DWI signal decay to parameters that reflect non-Gaussian diffusion coefficient (D) and deviations from normal distribution (K). A mixed model analysis of variance and receiver operating characteristic (ROC) analyses were performed to assess the statistical significance of the metrics of DKI and apparent diffusion coefficient (ADC).
RESULTS: K was significantly higher in prostate cancer and stromal BPH than in benign PZ (1.19 ± 0.24 and 0.99 ± 0.28 versus 0.63 ± 0.23, P < 0.001 and P < 0.001, respectively). K showed a trend toward higher levels in prostate cancer than in stromal BPH (1.19 ± 0.24 versus 0.99 ± 0.28, P = 0.051). On the ROC analyses, a significant difference in area under the curve was not observed between K and ADC, however, K showed the highest sensitivity among three parameters.
CONCLUSION: DKI may contribute to the imaging diagnosis of prostate cancer, especially in the differential diagnosis of prostate cancer and BPH.
© 2013 Wiley Periodicals, Inc.

Entities:  

Keywords:  benign prostatic hyperplasia; diffusion; kurtosis; magnetic resonance imaging; prostate cancer

Mesh:

Year:  2013        PMID: 24924835     DOI: 10.1002/jmri.24379

Source DB:  PubMed          Journal:  J Magn Reson Imaging        ISSN: 1053-1807            Impact factor:   4.813


  30 in total

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Review 5.  Diffusion MRI of cancer: From low to high b-values.

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9.  Quantitative diffusion MRI using reduced field-of-view and multi-shot acquisition techniques: Validation in phantoms and prostate imaging.

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Journal:  Magn Reson Imaging       Date:  2018-04-17       Impact factor: 2.546

10.  Machine learning-based analysis of MR radiomics can help to improve the diagnostic performance of PI-RADS v2 in clinically relevant prostate cancer.

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Journal:  Eur Radiol       Date:  2017-04-03       Impact factor: 5.315

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