Breast lesion characterization using whole‐lesion histogram analysis with stretched‐exponential diffusion model

Liu, Chunling; Wang, Kun; Li, Xiaodan; Zhang, Jine; Ding, Jie; Spuhler, Karl; Duong, Timothy Q.; Liang, Changhong; Huang, Chuan

doi:10.1002/jmri.25904

Cited by 28 publications

(42 citation statements)

References 33 publications

(67 reference statements)

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“…K min , D max , and ADC max were precisely representative of the most unrestricted diffusion of necrosis, so they did not show any differences between benign and malignant lesions. Kurtosis and skewness are metrics that describe the distribution pattern of parameters, and the result indicated that there was no difference in the distribution pattern of parameters between the benign and malignant groups, which is consistent with a previous study of histogram analysis with the stretched‐exponential model . Furthermore, ADC histogram parameters (from ADC min to ADC sd ) were significantly lower than D histogram parameters (from D min to D sd ) in benign and malignant lesions.…”

Section: Discussionsupporting

confidence: 89%

“…K max , D min , and ADC min had significantly higher sensitivity than their individual mean value, while specificity did not differ significantly. In a previous study of ADC histogram parameters, Liu et al found that ADC 10 yielded the highest AUC (0.893) for differentiating malignant from benign lesions. Suo et al demonstrated that the AUC of ADC min and ADC 25 were both significantly higher than that of ADC mean .…”

Section: Discussionmentioning

confidence: 98%

“…Therefore, the commonly used K mean and D mean metrics may be limited. Histogram analysis can provide information about whole tumors including minimum, maximum, mean, skewness and kurtosis values of the parameter distributions . More valuable information can be obtained from a histogram analysis of DKI parameters …”

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confidence: 99%

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Histogram analysis of diffusion kurtosis imaging based on whole‐volume images of breast lesions

Yuan

Kong

et al. 2019

Magnetic Resonance Imaging

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Background Breast diffusion kurtosis imaging (DKI) is a novel MRI technique to assess breast cancer but the effectivity still remains to be improved. Purpose To investigate the performance of whole‐volume histogram parameters derived from a DKI model for differentiating benign and malignant breast lesions. Study Type Retrospective. Population In all, 120 patients with breast lesions (62 malignant, 58 benign). Sequence DKI sequence with seven b‐values (0, 500, 1000, 1500, 2000, 2500, and 3000 s/mm2) and DWI sequence with two b‐values (0 and 1000 s/mm2) on 3.0T MRI. Assessment Histogram parameters of the DKI model (K and D) and the DWI model (ADC), including the minimum, maximum, mean, percentile values (25th, 50th, 75th, and 95th), standard deviation, kurtosis and skewness, were calculated by two radiologists for the whole lesion volume. Statistical Tests Student's t‐test was used to compare malignant and benign lesions. The diagnostic performances were evaluated by receiver operating characteristic (ROC) analysis. Results Kmax, Dmin, and ADCmin had the highest area under the curve (AUC) (0.875, 0.830, and 0.847, respectively), sensitivity (85.5%, 74.2%, and 77.4%, respectively), and accuracy (85.0%, 79.2%, and 81.7%, respectively) in their individual histogram parameter groups, and Kmax was found to outperform Dmin and ADCmin. ADC histogram parameters (from ADCmin to ADCsd) were significantly lower than D histogram parameters in all groups. Data Conclusion Kmax, Dmin, and ADCmin were found to be better metrics than the corresponding average values for differentiating benign from malignant tumors. Histogram parameters derived from the DKI model provided more information and had better diagnostic performance than ADC parameters derived from the DWI model. Level of Evidence: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:627–634.

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Section: Discussionsupporting

confidence: 89%

Section: Discussionmentioning

confidence: 98%

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Histogram analysis of diffusion kurtosis imaging based on whole‐volume images of breast lesions

Yuan

Kong

et al. 2019

Magnetic Resonance Imaging

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“…some studies demonstrated the utility of bi-exponential model for differentiating benign and malignant tissues. [5][6][7][8] However, tumor tissue and its microstructure are complex and varied, and it can be less than ideal to characterize them using only two intravoxel proton pools, as in the bi-exponential model. To overcome this limitation of bi-exponential models, a stretched exponential model has been introduced.…”

Section: Introductionmentioning

confidence: 99%

Comparison of the Diagnostic Value of Mono-exponential, Bi-exponential, and Stretched Exponential Signal Models in Diffusion-weighted MR Imaging for Differentiating Benign and Malignant Hepatic Lesions

et al. 2021

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To compare the diagnostic value of mono-exponential, bi-exponential, and stretched exponential diffusion-weighted imaging (DWI) for differentiating benign and malignant hepatic lesions. Methods: This prospective study was approved by our Institutional Review Board and the patients provided written informed consent. Magnetic resonance imaging was acquired for 56 patients with suspected liver disease. This identified 90 focal liver lesions with a maximum diameter >10 mm, of which 47 were benign and 43 were malignant. Using home-built software, two radiologists measured the DWI parameters of hepatic lesions for three models: the apparent diffusion coefficient (ADC) from a mono-exponential model; the true diffusion coefficient (D), pseudo-diffusion coefficient (D *), and perfusion fraction (f) from a bi-exponential model; and the distributed diffusion coefficient (DDC) and water molecular diffusion heterogeneity index (a) from a stretched exponential model. The parameters were compared between benign and malignant hepatic lesions. Results: ADC, D, D * , f, and DDC values were significantly lower for malignant hepatic lesions than for benign lesions (P < 0.0001-0.03). Although logistic regression analysis demonstrated that DDC was the only statistically significant parameter for differentiating benign and malignant lesions (P = 0.039), however, the areas under the receiver operating characteristic curve for differentiating benign and malignant lesions were comparable between ADC (0.98) and DDC (0.98) values. Conclusion: DDC values obtained from the stretched exponential model could be also used as a quantitative imaging biomarker for differentiating benign and malignant hepatic lesions, however, the diagnostic performance was comparable with ADC values.

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“…This model enables the measurement of the water diffusion heterogeneity index α, which ranges from 0 to 1 (a value of 1 indicates water diffusion). Accumulating evidence suggests that stretched-exponential DWI is useful for assessing gliomas [11][12][13][14][15][16] and other tumors, including breast [17], pancreatic [18], hepatic [19], prostate [20], bladder [21], and renal cancers [22].…”

Section: Introductionmentioning

confidence: 99%

Differentiation of high-grade from low-grade diffuse gliomas using diffusion-weighted imaging: a comparative study of mono-, bi-, and stretched-exponential diffusion models

et al. 2020

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Purpose Diffusion-weighted imaging (DWI) plays an important role in the preoperative assessment of gliomas; however, the diagnostic performance of histogram-derived parameters from mono-, bi-, and stretched-exponential DWI models in the grading of gliomas has not been fully investigated. Therefore, we compared these models' ability to differentiate between high-grade and low-grade gliomas. Methods This retrospective study included 22 patients with diffuse gliomas (age, 23-74 years; 12 males; 11 high-grade and 11 low-grade gliomas) who underwent preoperative 3 T-magnetic resonance imaging from October 2014 to August 2019. The apparent diffusion coefficient was calculated from the mono-exponential model. Using 13 b-values, the true-diffusion coefficient, pseudo-diffusion coefficient, and perfusion fraction were obtained from the bi-exponential model, and the distributed-diffusion coefficient and heterogeneity index were obtained from the stretched-exponential model. Region-of-interests were drawn on each imaging parameter map for subsequent histogram analyses. Results The skewness of the apparent diffusion, true-diffusion, and distributed-diffusion coefficients was significantly higher in high-grade than in low-grade gliomas (0.67 ± 0.67 vs. − 0.18 ± 0.63, 0.68 ± 0.74 vs. − 0.08 ± 0.66, 0.63 ± 0.72 vs. − 0.15 ± 0.73; P = 0.0066, 0.0192, and 0.0128, respectively). The 10th percentile of the heterogeneity index was significantly lower (0.77 ± 0.08 vs. 0.88 ± 0.04; P = 0.0004), and the 90th percentile of the perfusion fraction was significantly higher (12.64 ± 3.44 vs. 7.14 ± 1.70%: P < 0.0001), in high-grade than in low-grade gliomas. The combination of the 10th percentile of the true-diffusion coefficient and 90th percentile of the perfusion fraction showed the best area under the receiver operating characteristic curve (0.96). Conclusion The bi-exponential model exhibited the best diagnostic performance for differentiating high-grade from low-grade gliomas.

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Breast lesion characterization using whole‐lesion histogram analysis with stretched‐exponential diffusion model

Abstract: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1701-1710.

Cited by 28 publications

References 33 publications

Histogram analysis of diffusion kurtosis imaging based on whole‐volume images of breast lesions

Histogram analysis of diffusion kurtosis imaging based on whole‐volume images of breast lesions

Comparison of the Diagnostic Value of Mono-exponential, Bi-exponential, and Stretched Exponential Signal Models in Diffusion-weighted MR Imaging for Differentiating Benign and Malignant Hepatic Lesions

Differentiation of high-grade from low-grade diffuse gliomas using diffusion-weighted imaging: a comparative study of mono-, bi-, and stretched-exponential diffusion models

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