Diffusion-weighted imaging of prostate cancer using a statistical model based on the gamma distribution

Shinmoto, Hiroshi; Oshio, Koichi; Tamura, Chiharu; Soga, Shigeyoshi; Okamura, Teppei; Yamada, Kentaro; Kaji, Tastumi; Mulkern, Robert V.

doi:10.1002/jmri.24761

Cited by 14 publications

(24 citation statements)

References 19 publications

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“…Oshio et al reported a higher f 1 fraction for prostate cancer relative to the normal peripheral zone. In the Shinmoto et al study, a significantly higher f 1 fraction was reported in prostate cancer relative to benign prostatic hyperplasia as in the actual study comparing benign and malignant breast lesions. In the same study, a higher f 3 fraction was reported in benign prostatic hyperplasia relative to prostate cancer and that was also found in the present study comparing benign and malignant breast lesions.…”

Section: Discussionmentioning

confidence: 79%

“…Another approach is the application of the gamma function to the statistical distribution of ADC values: the gamma distribution (GD) model . This model is expected to reflect the probability of the distribution of water molecule mobility in different regions of the tissue .…”

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confidence: 99%

“…This model is expected to reflect the probability of the distribution of water molecule mobility in different regions of the tissue . Based on this statistical approach, one can also obtain the fraction of intracellular, extracellular, and perfusion components of tissue . All this information, obtained through only one model, may surpass the advantages offered by other models.…”

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confidence: 99%

“…The GD model has been applied to the brain and prostate cancer tissue . Several advantages were stated by Oshio et al, such as the simple mathematical processing, the possibility to relate this model to other quantitative diffusion models, and its fitting process stability .…”

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confidence: 99%

“…7,10 Another approach is the application of the gamma function to the statistical distribution of ADC values: the gamma distribution (GD) model. [11][12][13] This model is expected to reflect the probability of the distribution of water molecule mobility in different regions of the tissue. 14,15 Based on this statistical approach, one can also obtain the fraction of intracellular, extracellular, and perfusion components of tissue.…”

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confidence: 99%

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Gamma Distribution Model in the Evaluation of Breast Cancer Through Diffusion‐Weighted MRI: A Preliminary Study

Borlinhas

Loução

Conceição

et al. 2018

Magnetic Resonance Imaging

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Background The gamma distribution (GD) model is based on the statistical distribution of the apparent diffusion coefficient (ADC) parameter. The GD model is expected to reflect the probability of the distribution of water molecule mobility in different regions of tissue, but also the intra‐ and extracellular diffusion and perfusion components (f1, f2, f3 fractions). Purpose To assess the GD model in the characterization and diagnostic performance of breast lesions. Study Type Prospective. Population In all, 48 females with 24 benign and 33 malignant breast lesions. Field Strength/Sequence A diffusion‐weighted sequence (b = 0–3000 s/mm2) with a 3 T scanner. Assessment For each group of benign, malignant, invasive, and in situ breast lesions, the ADC was obtained. Also, θ and k parameters (scale and shape of the statistic distribution, respectively), f1, f2, and f3 fractions were obtained from fitting the GD model to diffusion data. Statistical Tests Lesion types were compared regarding diffusion parameters using nonparametric statistics and receiver operating characteristic curve diagnostic performance. Results The majority of GD parameters (k, f1, f2, f3 fractions) showed significant differences between benign and malignant lesions, and between in situ and invasive lesions (f1, f2, f3 fractions) (P ≤ 0.001). The best diagnostic performances were obtained with ADC and f1 fraction in benign vs. malignant lesions (area under curve [AUC] = 0.923 and 0.913, sensitivity = 93.9% and 81.8%, specificity = 79.2% and 91.7%, accuracy = 87.7% and 86.0%, respectively). In invasive lesions vs. in situ lesions, the best diagnostic performance was obtained with f1 fraction, which outperformed ADC results (AUC = 0.978 and 0.941, and sensitivity = 91.3% for both parameters, specificity = 100.0% and 90.0%, accuracy = 93.9% and 90.9%, respectively). Data Conclusion This work shows that the GD model provides information in addition to the ADC parameter, suggesting its potential in the diagnosis of breast lesions. Level of Evidence 2: Technical Efficacy Stage 2 J. Magn. Reson. Imaging 2019;50:230–238.

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confidence: 99%

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confidence: 99%

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confidence: 99%

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confidence: 99%

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Gamma Distribution Model in the Evaluation of Breast Cancer Through Diffusion‐Weighted MRI: A Preliminary Study

Borlinhas

Loução

Conceição

et al. 2018

Magnetic Resonance Imaging

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Information‐based ranking of 10 compartment models of diffusion‐weighted signal attenuation in fixed prostate tissue

et al. 2016

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This study compares the theoretical information content of single‐ and multi‐compartment models of diffusion‐weighted signal attenuation in prostate tissue. Diffusion‐weighted imaging (DWI) was performed at 9.4 T with multiple diffusion times and an extended range of b values in four whole formalin‐fixed prostates. Ten models, including different combinations of isotropic, anisotropic and restricted components, were tested. Models were ranked using the Akaike information criterion. In all four prostates, two‐component models, comprising an anisotropic Gaussian component and an isotropic restricted component, ranked highest in the majority of voxels. Single‐component models, whether isotropic (apparent diffusion coefficient, ADC) or anisotropic (diffusion tensor imaging, DTI), consistently ranked lower than multi‐component models. Model ranking trends were independent of voxel size and maximum b value in the range tested (1.6–16 mm3 and 3000–10 000 s/mm2). This study characterizes the two major water components previously identified by biexponential models and shows that models incorporating both anisotropic and restricted components provide more information‐rich descriptions of DWI signals in prostate tissue than single‐ or multi‐component anisotropic models and models that do not account for restricted diffusion. Copyright © 2016 John Wiley & Sons, Ltd.

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The relationship between diffusion heterogeneity and microstructural changes in high-grade gliomas using Monte Carlo simulations

Lee

Bennett

Debbins

et al. 2022

Magnetic Resonance Imaging

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Diffusion-weighted imaging of prostate cancer using a statistical model based on the gamma distribution

Abstract: A statistical model based on the gamma distribution proved suitable for describing diffusion signal decay curves of PCa. This approach may provide better correlation between diffusion signal decay and histological information in the prostate gland.

Cited by 14 publications

References 19 publications

Gamma Distribution Model in the Evaluation of Breast Cancer Through Diffusion‐Weighted MRI: A Preliminary Study

Gamma Distribution Model in the Evaluation of Breast Cancer Through Diffusion‐Weighted MRI: A Preliminary Study

Information‐based ranking of 10 compartment models of diffusion‐weighted signal attenuation in fixed prostate tissue

The relationship between diffusion heterogeneity and microstructural changes in high-grade gliomas using Monte Carlo simulations

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