Discrimination of Breast Cancer from Healthy Breast Tissue Using a Three-component Diffusion-weighted MRI Model

Andreassen, Maren M. Sjaastad; Rodríguez‐Soto, Ana E.; Conlin, Christopher C.; Vidić, Igor; Seibert, Tyler M.; Wallace, Anne M.; Zare, Somaye; Kuperman, Joshua; Abudu, Boya; Ahn, Grace S.; Hahn, Michael E.; Jerome, Neil P.; Østlie, Agnes; Bathen, Tone Frost; Ojeda‐Fournier, Haydee; Goa, Pål Erik; Rakow‐Penner, Rebecca; Dale, Anders M.

doi:10.1158/1078-0432.ccr-20-2017

Cited by 19 publications

(33 citation statements)

References 51 publications

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“…Combination of multiple RSI outputs is ongoing work in our laboratory and has shown potential for accurate automatic classification of breast lesions. 49 Future work includes the use of RSI-derived signal contributions and advanced computer algorithms to evaluate the diagnostic value of multi-exponential models in an independent cohort. Altogether, these data may be used to aid in radiological differentiation between benign tissues and malignant breast lesions without the use of intravenous contrast agents.…”

Section: Discussionmentioning

confidence: 99%

“…Based on the plots from biexponential and 3‐component models in Figure 2B, it becomes evident that C 1,3 has a higher tumor conspicuity compared to C 2,3 . Combination of multiple RSI outputs is ongoing work in our laboratory and has shown potential for accurate automatic classification of breast lesions 49 . Future work includes the use of RSI‐derived signal contributions and advanced computer algorithms to evaluate the diagnostic value of multi‐exponential models in an independent cohort.…”

Section: Discussionmentioning

confidence: 99%

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Characterization of the diffusion signal of breast tissues using multi‐exponential models

Rodríguez‐Soto

Andreassen

Fang

et al. 2021

Magnetic Resonance in Med

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Restriction spectrum imaging (RSI) decomposes the diffusion-weighted MRI signal into separate components of known apparent diffusion coefficients (ADCs). The number of diffusion components and optimal ADCs for RSI are organ-specific and determined empirically. The purpose of this work was to determine the RSI model for breast tissues. Methods:The diffusion-weighted MRI signal was described using a linear combination of multiple exponential components. A set of ADC values was estimated to fit voxels in cancer and control ROIs. Later, the signal contributions of each diffusion component were estimated using these fixed ADC values. Relative-fitting residuals and Bayesian information criterion were assessed. Contrast-to-noise ratio between cancer and fibroglandular tissue in RSI-derived signal contribution maps was compared to DCE imaging.Results: A total of 74 women with breast cancer were scanned at 3.0 Tesla MRI.The fitting residuals of conventional ADC and Bayesian information criterion suggest that a 3-component model improves the characterization of the diffusion signal over a biexponential model. Estimated ADCs of triexponential model were

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Characterization of the diffusion signal of breast tissues using multi‐exponential models

Rodríguez‐Soto

Andreassen

Fang

et al. 2021

Magnetic Resonance in Med

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“…Magnetic resonance imaging (MRI) is widely used for evaluating breast cancer because of its non-invasive nature and excellent soft tissue contrast (1)(2)(3)(4). Particularly, single diffusion encoding along a single direction per shot has been incorporated as a key imaging technique into routine breast MRI examination to complement dynamic contrastenhanced (DCE) MRI, since single diffusion encoding can produce contrast in tissues without using gadolinium contrast medium injections and images can be acquired rapidly (5)(6)(7)(8).…”

Section: Introductionmentioning

confidence: 99%

Clinical experience of tensor-valued diffusion encoding for microstructure imaging by diffusional variance decomposition in patients with breast cancer

Cho¹,

Baek²,

Szczepankiewicz³

et al. 2022

Quant Imaging Med Surg

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Background: Diffusion-weighted imaging plays a key role in magnetic resonance imaging (MRI) of breast tumors. However, it remains unclear how to interpret single diffusion encoding with respect to its link with tissue microstructure. The purpose of this retrospective cross-sectional study was to use tensor-valued diffusion encoding to investigate the underlying microstructure of invasive ductal carcinoma (IDC) and evaluate its potential value in a clinical setting. Methods: We retrospectively reviewed biopsy-proven breast cancer patients who underwent preoperative breast MRI examination from July 2020 to March 2021. We reviewed the MRI of 29 patients with 30 IDCs, including analysis by diffusional variance decomposition enabled by tensor-valued diffusion encoding. The diffusion parameters of mean diffusivity (MD), total mean kurtosis (MK T ), anisotropic mean kurtosis (MK A ), isotropic mean kurtosis (MK I ), macroscopic fractional anisotropy (FA), and microscopic fractional anisotropy (μFA) were estimated. The parameter differences were compared between IDC and normal fibroglandular breast tissue (FGBT), as well as the association between the diffusion parameters and histopathologic items. Results: The mean value of MD in IDCs was significantly lower than that of normal FGBT (1.07±0.27 vs. 1.34±0.29, P<0.001); however, MK T , MK A , MK I , FA, and μFA were significantly higher (P<0.005). Among all the diffusion parameters, MK I was positively correlated with the tumor size on both MRI and pathological specimen (r s =0.38, P<0.05 vs. r s =0.54, P<0.01), whereas MK T had a positive correlation with the tumor size in the pathological specimen only (r s =0.47, P<0.02). In addition, the lymph node (LN) metastasis group had significantly higher MK T , MK A , and μFA compared to the metastasis negative group (P<0.05). Conclusions: Tensor-valued diffusion encoding enables a useful non-invasive method for characterizing

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“…[14][15][16] A recent trend to utilize gadolinium-free MRI techniques in clinical scenarios where use of GBCA is a challenge, like pregnancy, severe contrast allergies, and lactational status [17][18][19] rely on Diffusion-(GBCA-free)-MRI techniques (DWI/DTI-MRI) that are based on the intrinsic cellular contrast. [20][21][22][23][24][25][26] Certain breast Diffusion-MRI protocols have also been used for high-risk screening and as a problem-solving tool in selected cases, 27,28 however it should not be used as stand-alone technique yet, as clinical protocols are still under evaluation. 29,30 This review provides considerations that may be helpful for clinical practice of both general MRI and breast subspecialty radiologists.…”

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confidence: 99%

What’s Hot in Breast MRI

Scaranelo

2021

Can Assoc Radiol J

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Several articles in the literature have demonstrated a promising role for breast MRI techniques that are more economic in total exam time than others when used as supplement to mammography for detection and diagnosis of breast cancer. There are many technical factors that must be considered in the shortened breast MRI protocols to cut down time of standard ones, including using optimal fat suppression, gadolinium-chelates intravascular contrast administrations for dynamic imaging with post processing subtractions and maximum intensity projections (MIP) high spatial and temporal resolution among others. Multiparametric breast MRI that includes both gadolinium-dependent, i.e., dynamic contrast-enhanced (DCE-MRI) and gadolinium-free techniques, i.e., diffusion-weighted/diffusion-tensor magnetic resonance imaging (DWI/DTI) are shown by several investigators that can provide extremely high sensitivity and specificity for detection of breast cancer. This article provides an overview of the proven indications for breast MRI including breast cancer screening for higher than average risk, determining chemotherapy induced tumor response, detecting residual tumor after incomplete surgical excision, detecting occult cancer in patients presenting with axillary node metastasis, detecting residual tumor after incomplete breast cancer surgical excision, detecting cancer when results of conventional imaging are equivocal, as well patients suspicious of having breast implant rupture. Despite having the highest sensitivity for breast cancer detection, there are pitfalls, however, secondary to false positive and false negative contrast enhancement and contrast-free MRI techniques. Awareness of the strengths and limitations of different approaches to obtain state of the art MR images of the breast will facilitate the work-up of patients with suspicious breast lesions.

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Discrimination of Breast Cancer from Healthy Breast Tissue Using a Three-component Diffusion-weighted MRI Model

Cited by 19 publications

References 51 publications

Characterization of the diffusion signal of breast tissues using multi‐exponential models

Characterization of the diffusion signal of breast tissues using multi‐exponential models

Clinical experience of tensor-valued diffusion encoding for microstructure imaging by diffusional variance decomposition in patients with breast cancer

What’s Hot in Breast MRI

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