Advanced diffusion models in head and neck squamous cell carcinoma patients: Goodness of fit, relationships among diffusion parameters and comparison with dynamic contrast-enhanced perfusion

Fujima, Noriyuki; Sakashita, Tetsuya; Homma, Akihiro; Shimizu, Yukie; Yoshida, Atsushi; Harada, Taisuke; Tha, Khin Khin; Kudo, Kohsuke; Shirato, Hiroki

doi:10.1016/j.mri.2016.10.024

Cited by 40 publications

(29 citation statements)

References 24 publications

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“…Our present analyses revealed that the combination of D 2 and D 3 in tri-exponential fitting is an indicator that could be used for the prediction of high tumor growth rates. Using a tri-exponential fitting model for HNSCC, another research group speculated that D 2 fairly or moderately reflects the diffusion of the segment in the extracellular extravascular space (EES), whereas D 3 also reflects the cellular component [7]. Based on our present findings, we also speculate that HNSCCs with a high growth rate will have decreased water diffusivity in the EES caused by the strongly elevated tumor cellular density in its microenvironment.…”

Section: Discussionsupporting

confidence: 66%

“…The biological characteristics of these two groups might be somewhat different, and thus a further subgroup analysis is needed. However, the diffusion parameters of these two subtypes were reported to not differ significantly [7]. We also found that the information of the primary site was not significantly associated with the tumor growth rate in a univariate analysis.…”

Section: Discussionmentioning

confidence: 57%

“…The ranges of these diffusion parameters were all observed to be around 1.0 × 10 −3 mm/s 2 in this study. An earlier report also stated that these parameters were well correlated with each other [7]. These parameters might reflect almost the same structure, and thus can be used interchangeably.…”

Section: Discussionmentioning

confidence: 83%

“…Assessments of the ADC, SEM and DKI usually target the tissue diffusion (except for the perfusion-related signal), and we therefore used the signal intensity of six b-values (0, 200, 400, 800, 1000 and 2000 s/mm 2 ) for the parameter calculations of ADC, DKI and SEM. To perform these parameter calculations, we used the following equations [7]:

\frac{S_{(b)}}{S_{0}} = e^{- b \cdot ADC}

\frac{S_{(b)}}{S_{0}} = f \cdot e^{- b \cdot D *} + (1 - f) \cdot e^{- b \cdot D}

\frac{S_{(b)}}{S_{0}} = f_{1} \cdot e^{- b \cdot D_{1}} + f_{2} \cdot e^{- b \cdot D_{2}} + f_{3} \cdot e^{- b \cdot D_{3}}

\frac{S_{(b)}}{S_{0}} = \exp [- b * D_{k} + \frac{1}{6} * b^{2} * D_{k}^{2} * K]

\frac{S_{(b)}}{S_{0}} = e^{- {(b \cdot DDC)}^{α}}

where S (b) is the signal intensity at the b-value denoted by the subscript, S 0 is the signal intensity at the b-value of 0, and b is b-factor, in all five equations. In the tri-exponential fitting of Eq.…”

Section: Methodsmentioning

confidence: 99%

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Non-invasive prediction of the tumor growth rate using advanced diffusion models in head and neck squamous cell carcinoma patients

et al. 2017

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We assessed parameters of advanced diffusion weighted imaging (DWI) models for the prediction of the tumor growth rate in 55 head and neck squamous cell carcinoma (HNSCC) patients. The DWI acquisition used single-shot spin-echo echo-planar imaging with 12 b-values (0−2000). We calculated 14 DWI parameters using mono-exponential, bi-exponential, tri-exponential, stretched exponential and diffusion kurtosis imaging models. We directly measured the tumor growth rate from two sets of different-date imaging data. We divided the patients into a discovery group (n = 40) and validation group (n = 15) based on their MR acquisition dates. In the discovery group, we performed univariate and multivariate regression analyses to establish the multiple regression equation for the prediction of the tumor growth rate using diffusion parameters. The equation obtained with the discovery group was applied to the validation group for the confirmation of the equation's accuracy. After the univariate and multivariate regression analyses in the discovery-group patients, the estimated tumor growth rate equation was established by using the significant parameters of intermediate diffusion coefficient D2 and slow diffusion coefficient D3 obtained by the tri-exponential model. The discovery group's correlation coefficient between the estimated and directly measured tumor growth rates was 0.74. In the validation group, the correlation coefficient (r = 0.66) and intra-class correlation coefficient (0.65) between the estimated and directly measured tumor growth rates were respectively good. In conclusion, advanced DWI model parameters can be a predictor for determining HNSCC patients’ tumor growth rate.

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Section: Discussionsupporting

confidence: 66%

Section: Discussionmentioning

confidence: 57%

Section: Discussionmentioning

confidence: 83%

\frac{S_{(b)}}{S_{0}} = e^{- b \cdot ADC}

\frac{S_{(b)}}{S_{0}} = f \cdot e^{- b \cdot D *} + (1 - f) \cdot e^{- b \cdot D}

\frac{S_{(b)}}{S_{0}} = f_{1} \cdot e^{- b \cdot D_{1}} + f_{2} \cdot e^{- b \cdot D_{2}} + f_{3} \cdot e^{- b \cdot D_{3}}

\frac{S_{(b)}}{S_{0}} = \exp [- b * D_{k} + \frac{1}{6} * b^{2} * D_{k}^{2} * K]

\frac{S_{(b)}}{S_{0}} = e^{- {(b \cdot DDC)}^{α}}

Section: Methodsmentioning

confidence: 99%

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Non-invasive prediction of the tumor growth rate using advanced diffusion models in head and neck squamous cell carcinoma patients

et al. 2017

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“…In addition, it is also informative to compare different non-Gaussian diffusion models using the same high-b diffusion data sets. A comprehensive comparison between different non-Gaussian diffusion models has been performed in healthy subjects (23) and cancer patients (24) previously.…”

Section: Introductionmentioning

confidence: 99%

A comparative assessment of preclinical chemotherapeutic response of tumors using quantitative non-Gaussian diffusion MRI

Smith

et al. 2017

Magnetic Resonance Imaging

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Background Diffusion-weighted MRI (DWI) signal attenuation is often not mono-exponential (i.e. non-Gaussian diffusion) with stronger diffusion weighting. Several non-Gaussian diffusion models have been developed and may provide new information or higher sensitivity compared with the conventional apparent diffusion coefficient (ADC) method. However the relative merits of these models to detect tumor therapeutic response is not fully clear. Methods Conventional ADC, and three widely-used non-Gaussian models, (bi-exponential, stretched exponential, and statistical model), were implemented and compared for assessing SW620 human colon cancer xenografts responding to barasertib, an agent known to induce apoptosis via polyploidy. Bayesian Information Criterion (BIC) was used for model selection among all three non-Gaussian models. Results All of tumor volume, histology, conventional ADC, and three non-Gaussian DWI models could show significant differences between control and treatment groups after four days of treatment. However, only the non-Gaussian models detected significant changes after two days of treatment. For any treatment or control group, over 65.7% of tumor voxels indicate the bi-exponential model is strongly or very strongly preferred. Conclusion Non-Gaussian DWI model-derived biomarkers are capable of detecting tumor earlier chemotherapeutic response of tumors compared with conventional ADC and tumor volume. The bi-exponential model provides better fitting compared with statistical and stretched exponential models for the tumor and treatment models used in the current work.

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Multiparameter diffusion‐weighted imaging for characterizing pathological patterns in lupus nephritis patients: A preliminary study

Zhang

Lin

et al. 2019

Magnetic Resonance Imaging

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Background Microstructural changes of lupus nephritis (LN) kidney such as inflammatory cell infiltration or fibrosis could influence water molecular movement or diffusion, which indicates that diffusion‐weighted imaging (DWI) may become a valuable tool in evaluation of this disease. Purpose To explore whether multiparameter diffusion‐weighted imaging (mDWI) could contribute to characterize pathological patterns in LN patients. Study Type Retrospective. Population Twenty‐two patients with LN. Field Strength/Sequence Multi‐b value DWI was performed with a 3.0 T scanner. Assessment Apparent diffusion coefficient (ADC)m, perfusion‐related diffusion coefficient (Df), molecular diffusion coefficient (Ds), perfusion fraction (f), ADCs, α, ADCk, and mean kurtosis (MK) were calculated by monoexponential, biexponential, stretched‐exponential, and kurtosis models fits, respectively. Statistical Tests Independent sample t‐test, Pearson analysis and receiver operating characteristic (ROC). Results In the whole group, the activity index (AI) correlated significantly with alpha values in the medulla (rho = –0.54, P = 0.03). The chronicity index (CI) correlated significantly with Ds values in the medulla (rho = –0.61, P = 0.02). No significant association was found between any other diffusion parameter and histologic grade with all P > 0.05. For differentiating proliferative LN (Class III or IV) from Class V, the area under the ROC curve (AUC) of alpha in the medulla was 0.833 (P = 0.023). Data Conclusion mDWI might be used for the characterization of pathological patterns in LN patients. Level of Evidence: 3 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019;50:1075–1084.

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Advanced diffusion models in head and neck squamous cell carcinoma patients: Goodness of fit, relationships among diffusion parameters and comparison with dynamic contrast-enhanced perfusion

Cited by 40 publications

References 24 publications

Non-invasive prediction of the tumor growth rate using advanced diffusion models in head and neck squamous cell carcinoma patients

Non-invasive prediction of the tumor growth rate using advanced diffusion models in head and neck squamous cell carcinoma patients

A comparative assessment of preclinical chemotherapeutic response of tumors using quantitative non-Gaussian diffusion MRI

Multiparameter diffusion‐weighted imaging for characterizing pathological patterns in lupus nephritis patients: A preliminary study

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