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Evaluation of Fitting Models for Prostate Tissue Characterization using Extended-range b-factor Diffusion-weighted Imaging

Institution:
1Institute of Clinical Sciences, Sahlgrenska Academy, Gothenburg University, Gothenburg, Sweden.
2Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Publisher:
Wiley
Publication Date:
Apr-2018
Journal:
Magn Reson Med
Volume Number:
79
Issue Number:
4
Pages:
2346-58
Citation:
Magn Reson Med. 2018 Apr;79(4):2346-58.
PubMed ID:
28718517
PMCID:
PMC5771983
Keywords:
biexponential model, diffusion weighted imaging, gamma distribution model, kurtosis model, prostate cancer, stretched exponential model
Appears in Collections:
NCIGT, Prostate Group, SPL
Sponsors:
P41 EB015898/EB/NIBIB NIH HHS/United States
Generated Citation:
Langkilde F., Kobus T., Fedorov A., Dunne R., Tempany C.M., Mulkern R.V., Maier S.E. Evaluation of Fitting Models for Prostate Tissue Characterization using Extended-range b-factor Diffusion-weighted Imaging. Magn Reson Med. 2018 Apr;79(4):2346-58. PMID: 28718517. PMCID: PMC5771983.
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To compare the fitting and tissue discrimination performance of biexponential, kurtosis, stretched exponential, and gamma distribution models for high b-factor diffusion-weighted images in prostate cancer. METHODS: Diffusion-weighted images with 15 b-factors ranging from b = 0 to 3500 s/mm2 were obtained in 62 prostate cancer patients. Pixel-wise signal decay fits for each model were evaluated with the Akaike Information Criterion (AIC). Parameter values for each model were determined within normal prostate and the index lesion. Their potential to differentiate normal from cancerous tissue was investigated through receiver operating characteristic analysis and comparison with Gleason score. RESULTS: The biexponential slow diffusion fraction fslow , the apparent kurtosis diffusion coefficient ADCK , and the excess kurtosis factor K differ significantly among normal peripheral zone (PZ), normal transition zone (TZ), tumor PZ, and tumor TZ. Biexponential and gamma distribution models result in the lowest AIC, indicating a superior fit. Maximum areas under the curve (AUCs) of all models ranged from 0.93 to 0.96 for the PZ and from 0.95 to 0.97 for the TZ. Similar AUCs also result from the apparent diffusion coefficient (ADC) of a monoexponential fit to a b-factor sub-range up to 1250 s/mm2 . For kurtosis and stretched exponential models, single parameters yield the highest AUCs, whereas for the biexponential and gamma distribution models, linear combinations of parameters produce the highest AUCs. Parameters with high AUC show a trend in differentiating low from high Gleason score, whereas parameters with low AUC show no such ability. CONCLUSION: All models, including a monoexponential fit to a lower-b sub-range, achieve similar AUCs for discrimination of normal and cancer tissue. The biexponential model, which is favored statistically, also appears to provide insight into disease-related microstructural changes.