Histogram analysis of diffusion kurtosis imaging in the differentiation of malignant from benign breast lesions

Liu, Wei; Wei, Chaogang; Bai, Jiayuan; Gao, Xin; Zhou, Lijuan

doi:10.1016/j.ejrad.2019.06.008

Cited by 22 publications

(19 citation statements)

References 29 publications

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“…In the study of Kishimoto et al 7, they explored the performance of high-resolution DWI in visualizing breast cancer and their extent using readout-segmented echo-planar imaging, and found that malignant mass lesions were depicted with excellent agreement with the pathological results, but half of the non-mass lesions cannot be identified. Besides, due to the natural barriers from cell membranes and cellular compartments that restrict water movement, the logarithmic signal intensity decay will deviate from the plot of the monoexponential model, especially in a high b-value range (8).…”

Section: Introductionmentioning

confidence: 99%

“…Jensen et al (11) first introduced the DKI model in 2005 and developed new fields in brain, liver, and prostate imaging afterward (12)(13)(14). The investigation of breast tumors using the DKI model reached a peak in 2019 and became an important research area thereafter (8,(15)(16)(17)(18)(19)(20). The model provides microscopic information regarding the deviation of water diffusion from Gaussian distribution with the mean kurtosis (MK) and mean diffusivity (MD), a kurtosiscorrected diffusion coefficient.…”

Section: Introductionmentioning

confidence: 99%

“…However, the diagnostic performance of DKI in the breast was not consistent, and its applications remain debatable. For example, most studies (8,18,20) have suggested that breast cancer has a higher MK and a lower MD than benign lesions, while Park et al (16) reported that the difference in the MK between them is insignificant. Some studies (25)(26)(27) have reported that the DKIderived MK or MD manifested better diagnostic performance than the DWI-derived ADC, while Palm et al (15) found that DKI did not improve the differentiation performance for breast lesions in clinical protocols.…”

Section: Introductionmentioning

confidence: 99%

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The Diagnostic Performance of Diffusion Kurtosis Imaging in the Characterization of Breast Tumors: A Meta-Analysis

Peng

et al. 2020

Front. Oncol.

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Rationale and Objectives: Diffusion kurtosis imaging (DKI) is a promising imaging technique, but the results regarding the diagnostic performance of DKI in the characterization and classification of breast tumors are inconsistent among published studies. This study aimed to pool all published results to provide more robust evidence of the differential diagnosis between malignant and benign breast tumors using DKI. Methods: Studies on the differential diagnosis of breast tumors using DKI-derived parameters were systemically retrieved from PubMed, Embase, and Web of Science without a time limit. Review Manager 5.3 was used to calculate the standardized mean differences (SMDs) and 95% confidence intervals of the mean kurtosis (MK), mean diffusivity (MD), and apparent diffusion coefficient (ADC). Stata 12.0 was used to pool the sensitivity, specificity, and diagnostic odds ratio (DOR) as well as the publication bias and heterogeneity of each parameter. Fagan's nomograms were plotted to predict the post-test probabilities. Results: Thirteen studies including 867 malignant and 460 benign breast lesions were analyzed. Most of the included studies showed a low to unclear risk of bias and low concerns regarding applicability. Breast cancer showed a higher MK (SMD = 1.23, P < 0.001) but a lower MD (SMD = −1.29, P < 0.001) and ADC (SMD = −1.21, P < 0.001) than benign tumors. The MK (SMD = −1.36, P = 0.006) rather than the MD (SMD = 0.29, P = 0.20) or ADC (SMD = 0.26, P = 0.24) can further differentiate invasive ductal carcinoma from ductal carcinoma in situ. The DKI-derived MK (sensitivity = 90%, specificity = 88%, DOR = 66) and MD (sensitivity = 86% and specificity = 88%, DOR = 46) demonstrated superior diagnostic performance and post-test probability (65, 64, and 56% for MK, MD, and ADC) in differentiating malignant from benign breast lesions, with a higher sensitivity and specificity than the DWI-derived ADC (sensitivity = 85% and specificity = 83%, DOR = 29). Li et al. Characterizing Breast Tumor With DKI Conclusion: The DKI-derived MK and MD demonstrate a comparable diagnostic performance in the discrimination of breast tumors based on their microstructures and non-Gaussian characteristics. The MK can further differentiate invasive ductal carcinoma from ductal carcinoma in situ.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Diagnostic Performance of Diffusion Kurtosis Imaging in the Characterization of Breast Tumors: A Meta-Analysis

Peng

et al. 2020

Front. Oncol.

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“…Histogram analysis has been applied to the kurtosis model in two studies, in which it was found that histogram metrics within each individual group outperformed the mean values, which are typically used in standard diffusion kurtosis imaging ( 185 , 189 ). Visualization of tumor heterogeneity via histogram analysis may result in identification of the most aggressive portions of the lesions and therefore increase diagnostic accuracy in the discrimination of benign and malignant lesions.…”

Section: Advanced and Emerging Techniquesmentioning

confidence: 99%

Diffusion Breast MRI: Current Standard and Emerging Techniques

et al. 2022

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The role of diffusion weighted imaging (DWI) as a biomarker has been the subject of active investigation in the field of breast radiology. By quantifying the random motion of water within a voxel of tissue, DWI provides indirect metrics that reveal cellularity and architectural features. Studies show that data obtained from DWI may provide information related to the characterization, prognosis, and treatment response of breast cancer. The incorporation of DWI in breast imaging demonstrates its potential to serve as a non-invasive tool to help guide diagnosis and treatment. In this review, current technical literature of diffusion-weighted breast imaging will be discussed, in addition to clinical applications, advanced techniques, and emerging use in the field of radiomics.

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“…Histogram analysis not only calculates the average value of histogram parameters for the whole tumor but also publishes each voxel of the region of interest (ROI) to the histogram [ 11 ]. Accumulating evidence has indicated that it can appropriately quantify the heterogeneity in a tumor [ 12 ]. Thus, histogram analysis might be a noninvasive imaging method for predicting tumor biology.…”

Section: Introductionmentioning

confidence: 99%

Association Between DCE-MRI Perfusion Histogram Parameters and EGFR and VEGF Expressions in Different Lauren Classifications of Advanced Gastric Cancer

Zhao

Wang

et al. 2022

Pathol. Oncol. Res.

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Objective: To investigate the correlations between dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) perfusion histogram parameters and vascular endothelial growth factor (VEGF) and epidermal growth factor receptor (EGFR) expressions in advanced gastric cancer (AGC).Methods: This retrospective study included 80 pathologically confirmed patients with AGC who underwent DCE-MRI before surgery from February 2017 to May 2021. The DCE-MRI perfusion histogram parameters were calculated by Omni Kinetics software in four quantitative parameter maps. Immunohistochemical methods were used to detect VEGF and EGFR expressions and calculate the immunohistochemical score.Results: VEGF expression was relatively lower in patients with intestinal-type AGC than those with diffuse-type AGC (p < 0.05). For VEGF, Receiver operating characteristics (ROC) curve analysis revealed that Quantile 90 of Ktrans, Meanvalue of Kep and Quantile 50 of Ve provided the perfect combination of sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for distinguishing high and low VEGF expression, For EGFR, Skewness of Ktrans, Energy of Kep and Entropy of Vp provided the perfect combination of sensitivity, specificity, PPV and NPV for distinguishing high and low EGFR expression. Ktrans (Quantile 90, Entropy) showed the strongest correlation with VEGF and EGFR in patients with intestinal-type AGC (r = 0.854 and r = 0.627, respectively); Ktrans (Mean value, Entropy) had the strongest correlation with VEGF and EGFR in patients with diffuse-type AGC (r = 0.635 and 0.656, respectively).Conclusion: DCE-MRI perfusion histogram parameters can serve as imaging biomarkers to reflect VEGF and EGFR expressions and estimate their difference in different Lauren classifications of AGC.

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Histogram analysis of diffusion kurtosis imaging in the differentiation of malignant from benign breast lesions

Cited by 22 publications

References 29 publications

The Diagnostic Performance of Diffusion Kurtosis Imaging in the Characterization of Breast Tumors: A Meta-Analysis

The Diagnostic Performance of Diffusion Kurtosis Imaging in the Characterization of Breast Tumors: A Meta-Analysis

Diffusion Breast MRI: Current Standard and Emerging Techniques

Association Between DCE-MRI Perfusion Histogram Parameters and EGFR and VEGF Expressions in Different Lauren Classifications of Advanced Gastric Cancer

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