Diffusion-weighted Imaging of Breast Cancer with the Sensitivity Encoding Technique: Analysis of the Apparent Diffusion Coefficient Value

Kuroki, Yoshio; Nasu, Katsuhiro; Kuroki, Seiko; Murakami, Koji; Hayashi, Takayuki; Sekiguchi, Ryuzou; Nakamura, Shigeru

doi:10.2463/mrms.3.79

Cited by 172 publications

(162 citation statements)

References 27 publications

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“…Recently there have been several reports concerning the usefulness of DWI in differentiating benign and malignant breast lesions (11)(12)(13)(14)(15). According to the report by Rubesova et al (13), they found a threshold between malignant and benign lesions for a highest sensitivity and specificity (both 86%) around 1.13 ϫ 10 Ϫ3 mm 2 /s, which is similar to our results.…”

Section: Discussionsupporting

confidence: 91%

“…Concerning MR protocols other than DCE-MRI, diffusion-weighted MR imaging (DWI) and MR spectroscopy (MRS) have recently been reported to demonstrate usefulness in differentiating benign from malignant breast lesions (11)(12)(13)(14)(15)(16)(17)(18). Since the report by Kinoshita et al (11) in 2002, recent studies using DWI have shown that apparent diffusion coefficient (ADC) values can be useful in differentiating between benign and malignant breast lesions (11)(12)(13)(14)(15).…”

mentioning

confidence: 99%

“…Since the report by Kinoshita et al (11) in 2002, recent studies using DWI have shown that apparent diffusion coefficient (ADC) values can be useful in differentiating between benign and malignant breast lesions (11)(12)(13)(14)(15). Diffusion-weighted MRI is a specific modality that produces in vivo images of biological tissues weighted with the local microstructural characteristics of water diffusion.…”

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confidence: 99%

“…Thus, the effects of motion and diffusion become visible with DWI, and intravoxel incoherent motion imaging enables quantitative assessment of water diffusion in the tissue because it is used to measure the ADC, which reflects the mobility of water molecules in the tissue (15,19). In addition, diffusion has generally been shown to decrease in tissues with high cellularity such as malignant tumors; accordingly, some cancer shows low ADC values compared with normal tissue (11)(12)(13)(14)(15).…”

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confidence: 99%

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Enhanced mass on contrast‐enhanced breast MR imaging: Lesion characterization using combination of dynamic contrast‐enhanced and diffusion‐weighted MR images

Yabuuchi

Matsuo

Okafuji

et al. 2008

Magnetic Resonance Imaging

176

166

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Purpose:To evaluate the diagnostic accuracy of a combination of dynamic contrast-enhanced MR imaging (DCE-MRI) and diffusion-weighted MR imaging (DWI) in characterization of enhanced mass on breast MR imaging and to find the strongest discriminators between carcinoma and benignancy. Materials and Methods:We analyzed consecutive breast MR images in 270 patients; however, 13 lesions in 93 patients were excluded based on our criteria. We analyzed tumor size, shape, margin, internal mass enhancement, kinetic curve pattern, and apparent diffusion coefficient (ADC) values. We applied univariate and multivariate analyses to find the strongest indicators of malignancy and calculate a predictive probability for malignancy. We added the corresponding categories to these prediction probabilities for malignancy and calculated diagnostic accuracy when we consider category 4b, 4c, and 5 lesions as malignant and category 4a, 3, and 2 lesions as benign. In a validation study, 75 enhancing lesions in 71 patients were examined consecutively.Results: Irregular margin, heterogeneous internal enhancement, rim enhancement, plateau time-intensity curve (TIC) pattern, and washout TIC pattern were the strongest indicators of malignancy as well as past studies, and ADC values less than 1.1 ϫ 10 Ϫ3 mm 2 /s were also the strongest indicators of malignancy. In a validation study, sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 92% (56/61), 86% (12/14), 97% (56/58), 71% (12/17), and 91% (68/75), respectively. Conclusion:The combination of DWI and DCE-MRI could produce high diagnostic accuracy in the characterization of enhanced mass on breast MR imaging.

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Section: Discussionsupporting

confidence: 91%

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confidence: 99%

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confidence: 99%

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confidence: 99%

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Enhanced mass on contrast‐enhanced breast MR imaging: Lesion characterization using combination of dynamic contrast‐enhanced and diffusion‐weighted MR images

Yabuuchi

Matsuo

Okafuji

et al. 2008

Magnetic Resonance Imaging

176

166

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“…Many different strategies have been proposed to increase the specificity such as calculating pharmacokinetic contrast parameters (4), T2*-weighted perfusion imaging (5) and MR spectroscopy (5). A promising adjunct MRI technique is diffusion-weighted imaging (DWI) (6)(7)(8)(9)(10)(11)(12)(13)(14)(15). In DCE Breast MRI, lesions are characterized based on morphology, internal enhancement, and kinetic enhancement pattern (16,17).…”

Section: In Recent Years Dynamic Contrast Enhanced (Dce)mentioning

confidence: 99%

Quantitative diffusion weighted imaging for differentiation of benign and malignant breast lesions: The influence of the choice of b‐values

Peters

Vincken

Bosch

et al. 2010

Magnetic Resonance Imaging

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Optimization of intra‐voxel incoherent motion measurement in diffusion‐weighted imaging of breast cancer

Chen

Zhang

Long

et al. 2017

J Applied Clin Med Phys

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PurposeThe purpose of this study was to optimize intra‐voxel incoherent motion (IVIM) measurement in diffusion‐weighted imaging (DWI) of breast cancer by separating perfusion and diffusion effects through the determination of an optimal threshold b‐value, thus benign and cancerous breast tissues can be accurately differentiated using IVIM‐derived diffusion and perfusion parameters.Materials and MethodsTwenty‐eight patients, with biopsy‐confirmed breast cancers, were studied with a 3T MRI scanner, using T1‐weighted dynamic contrast‐enhanced MRI images, and diffusion‐weighted images with nine b‐values, ranging from 0 to 1000 s/mm². IVIM‐derived parameter maps for tissue diffusion coefficients D, perfusion fraction f, and pseudo‐diffusion coefficients D* were computed using the segmented fitting method with optimized threshold b‐value, and the sum of squared residuals (SSR) were calculated for IVIM‐derived parameters in different breast lesions.ResultsThe IVIM analysis method developed in this work can separate perfusion and diffusion effects with the optimal threshold b‐value of 300 s/mm², and the results of diffusion and perfusion parameters from IVIM analysis can be used to differentiate pathological changes in breast tissues. It was found that the averages and standard deviations of the diffusion and perfusion parameters, D, f, D*, are the following, for malignant, benign and normal breast tissues respectively: D (0.813 ± 0.225 × 10−3 mm2/s, 1.437 ± 0.538 × 10−3 mm2/s, 1.838 ± 0.213 × 10−3 mm2/s), f (10.73 ± 3.44%, 7.86 ± 3.70%, 8.92 ± 3.72%), D* (15.23 ± 12.17×10−3 mm²/s, 12.02 ± 3.19 × 10−3 mm2/s, 12.03 ± 7.21 × 10−3 mm2/s).Conclusion IVIM‐derived diffusion and perfusion parameter maps depend highly on the choice of threshold b‐value. Using the methodology developed in this work, and with the optimized threshold b‐value, the diffusion and perfusion parameters of breast tissues can be accurately assessed, making IVIM MRI a technique of choice for differential diagnosis of breast cancer.

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Diffusion-weighted Imaging of Breast Cancer with the Sensitivity Encoding Technique: Analysis of the Apparent Diffusion Coefficient Value

Abstract: SENSE-DWI was of sufficient quality to support diagnosis of breast mass. SENSE-DWI may permit the acquisition of more detailed information about lesions, including tumor cellularity, that is difficult to obtain with conventional techniques.

Cited by 172 publications

References 27 publications

Enhanced mass on contrast‐enhanced breast MR imaging: Lesion characterization using combination of dynamic contrast‐enhanced and diffusion‐weighted MR images

Enhanced mass on contrast‐enhanced breast MR imaging: Lesion characterization using combination of dynamic contrast‐enhanced and diffusion‐weighted MR images

Quantitative diffusion weighted imaging for differentiation of benign and malignant breast lesions: The influence of the choice of b‐values

Optimization of intra‐voxel incoherent motion measurement in diffusion‐weighted imaging of breast cancer

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