# Advanced Magnetic Resonance Imaging Modalities for Breast Cancer Diagnosis: An Overview of Recent Findings and Perspectives

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## Abstract

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## 1. Introduction

_{2}-weighted imaging, dynamic contrast-enhanced imaging (DCE-MRI), diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI), and MR spectroscopy (MRS). Although routine MRI has a high sensitivity (80–100%) among these techniques, it lacks characterization specificity for breast cancer [3]. Even though there is still a substantial link between DCE-MRI and tumor vascular structures, there is no confirmation that this approach is connected with tumor cellular proliferation [4]. Moreover, DCE-MRI specificity can be as low as 37% or as high as 97%. However, the development of DCE-MRI involves higher costs than other techniques, and it cannot be utilized with contrast agents for patients with renal dysfunction [5]. Therefore, multimodality imaging may cover some limitations of MRI models [6]. Diffusion-weighted imaging is helpful to measure the portability of water particles diffusing in tissue. Its technological benefits include a fast acquisition period (usually three min), vast accessibility to various commercial scanners, and no requirement for administering contrast agents [7]. On the other hand, its limitation is sensitivity to artifacts such as T

_{2}shine through, T

_{2}blackout, ghosting, blurring, and distortions [8].

_{2}relaxation times (liver). It can be combined with reading techniques, such as echo-planar imaging (EPI) or spiral imaging. To mitigate the effects of subject motion and maintain a high signal-to-noise ratio (SNR), the EPI method is used to achieve fast image acquisition. Echo-planar imaging is vulnerable to artifacts such as ghosting, chemical shift, and distortions [26]. It has been shown that parallel imaging techniques to reduce echo train lengths minimize susceptibility-related EPI artifacts in DWI and improve image quality, particularly at a field strength of 3 T [27]. Echo-planar imaging, nevertheless, is constrained by noise and typically uses thicker slices compared to CE-T

_{1}imaging [3]. Theoretically, stimulated echoes have just half the signal compared to spin echoes. Furthermore, rapid image formation is possible regarding the very short imaging of T

_{R}SSFP, which has high sensitivity to flow and diffusion [28].

## 2. DWI in the Field of Diagnostic Breast Cancer

^{−3}mm

^{2}/s [29]. DWI can be obtained by placing two additional diffusion-sensitizing gradients on each side of a spin-echo sequence’s 180° radiofrequency (RF) pulse. The diffusion weighting’s magnitude is achieved as follows:

^{2}is suggested to distinguish between normal and cancer tissues when time constraints allow only diffusion images obtained at a couple of amounts of b [16]; however, high b-value DWI accompanied by conventional MRI sequences can facilitate the diagnosis closely to DCE-MRI for breast cancer detection [31]. In many studies, it has been hypothetically shown that the ideal pair amounts of b to alleviate noise effects are in the range of 0 to 1000 s/mm

^{2}in the brain (with moderately slow diffusion), while 0 and 800 s/mm

^{2}are ideal in most low-water-content tissue such as breast [32,33]. Signal decay varies according to the baseline T

_{2}signal in benign and malignant breast cancer, as indicated in Figure 1.

_{1}- and T

_{2}-weighted, and higher-water-content lesions may have a high brilliance on DWI pictures; however, this is because of their exceptionally high T

_{2}signal and is not associated with diffusion (T

_{2}shine-through effect). One way to deal with this issue is to measure the ADC, which relies solely on diffusion [34]. Moreover, increasing the number of excitations in a fast DWI protocol has no diagnostic value according to Mori et al. They found no meaningful differences between one and four excitations in terms of lesion detectability or mean and minimum ADC value [35].

_{2}* decay during readout. To decrease distortions, using some approaches such as high receiver bandwidth, parallel imaging, readout-segmented multi-shot EPI sequences [37], combined EPI acquisitions with integrated dynamic shimming [38], and reduced field of view (rFOV) [39] can be helpful.

_{R}and decreasing acquisition time. Although parallel imaging can reduce artifacts by decreasing the length of the echo train and hence reducing the T

_{2}* blurring, it is constrained by the hardware of the RF coil [40].

## 3. Comparison of DWI with Other Modalities

_{2}-weighted imaging (DWI + T

_{2}WI) to detect non-palpable breast cancer in asymptomatic women. As indicated in Figure 2, the lesion was not visible on two-view mammography. The mass was enhanced on post-contrast MRI, easily identified on MIP. The mass appears to have a high signal on DWI and low ADC on DWI and ADC maps. According to their report, DWI + T

_{2}WI showed a higher area under the curve (AUC) (AUC = 0.73; sensitivity = 50%) than mammography alone (AUC = 0.64; sensitivity = 40%), but lower than DCE-MRI (AUC = 0.93; sensitivity = 86%). Moreover, a combination of mammography and DWI + T

_{2}WI showed greater sensitivity (69%) than mammography alone (40%). Having said that, false-positive outcomes using MRI images in high-risk lesions are substantially different from false-positive findings by mammography in low-risk lesions, according to Kuhl et al. [50].

_{2}-weighted image descriptors are most closely related to breast cancer diagnosis. Their study set the b value to 50 and 850 s/mm

^{2}. On DWI, malignant lesions exhibited a significantly lower average ADC mean (0.90 × 10

^{−3}mm

^{2}/s) than benign lesions (1.43 × 10

^{−3}mm

^{2}/s). They showed that DCE-MRI and DWI quantitative and qualitative variables are included in a multi-parametric MRI modality for breast cancer diagnosis. Indeed, they noted that models using the American College of Radiology (ACR) provide high diagnostic accuracy. Breast Imaging Reporting and Data System (BI-RADS) descriptors of margins and enhancement kinetics on DCE-MRI and ADC mean (either with DWI using a cutoff value or as a continuous variable) are mainly connected with a breast cancer diagnosis. Conventional T

_{2}-weighted imaging did not remarkably contribute to breast cancer diagnosis [51].

## 4. Different Models in DWI

_{int}can be assessed by a mono-exponential fit applied to the high b value range.

_{K}is the kurtosis corrected diffusion coefficient.

_{p}is quantified non-Gaussianity and D

_{p}is diffusivity.

_{d}is the diffusion gradient amplitude, δ is the diffusion gradient pulse lobe duration, Δ is diffusion gradient pulse separation, D is the diffusion coefficient, b is the fractional-order derivative in space, and µ is a spatial parameter.

^{2}. According to their study, diffusion signal models provided parameters with a high area under the curve (AUC > 0.9) for classifying benign and malignant lesions. In their reports, the highest AUC of 0.99 was achieved for f (bi-exponential), K (kurtosis), and 0.989 for D (fractional calculus). Additionally, non-Gaussian representations are required for fitting the DWI curve at high b values in breast lesions. Moreover, the single voxel analysis showed that the SNR provided high classification accuracy for the statistical and fractional calculus diffusion model. Meanwhile, the other non-Gaussian representations gave lower classification accuracy than the mono-exponential model [55].

## 5. DWI in Treatment Evaluation of Breast Cancer

## 6. DTI in the Diagnosis of Breast Cancer

_{1}, mean radial diffusivity [(λ

_{2}+ λ

_{3})/2], and empirical difference [λ

_{1}− λ

_{3}].

^{2}. The mean ADC value of malignant and benign lesions was 1.06 × 10

^{−3}± 0.24 mm

^{2}/s and 1.54 × 10

^{−3}± 0.22 mm

^{2}/s respectively, whereas it was 1.77 × 10

^{−3}± 0.20 mm

^{2}/s for normal tissue. They reported that ADC measurements had lower malignant lesions values than the benign and normal breast [4].

_{1}maps before and after administration of Gd-based contrast agents (GBCAs). The mean size of cancer extracted from λ

_{1}maps before administration of GBCAs remained statistically indistinguishable from the size determined following administration. The cancers showed remarkably lower DDCs, mean diffusivity, and intensity after GBCA administration and no alteration in maximal anisotropy in comparison with prior GBCA administration. For all parameters, except for λ

_{3}, the mean AUC values before and after GBCA administration did not vary considerably [75].

_{1}, λ

_{2}, λ

_{3}, mean diffusivity, and λ

_{1}–λ

_{3}significantly declined among lactating patients. Additionally, FA significantly increased in breast cancer associated with pregnancy compared to the normal lactating parenchyma region of interest. The contrast-to-noise in eigenvalues (λ

_{1}, λ

_{2}, λ

_{3}) and mean diffusivity were substantially superior to DCE in the lactating cohort [76].

## 7. DTI in Treatment Evaluation of Breast Cancer

_{1}, λ

_{2}, and λ

_{3}, and the mean diffusivity. They found that the maximal anisotropy and l1–l3 had lower levels in cancerous locations than normal tissue, which were reported in other studies, as well [80,81]. They also reported an increase in the eigenvalues and mean diffusivity in response to neoadjuvant chemotherapy. Indeed, they showed that DTI can monitor alterations in the size and diffusion tensor parameters of breast cancer in response to neoadjuvant chemotherapy with an accuracy comparable to that of DCE [79].

_{1}, λ

_{2}, λ

_{3}) and ADC than those without pCR. Moreover, they observed that while there was a weak correlation in early percentage changes in tumor FA with pCR, the correlation with the final improvement in tumor volume with pCR is related to therapy [82].

## 8. Amide Proton Transfer-Weighted Imaging in Breast Cancer Diagnosis

## 9. Diffusion Kurtosis Imaging in Breast Cancer Diagnosis

^{2}and at least 15 diffusion gradient directions, the diffusion kurtosis imaging method can trace numerous structures inside a single voxel, for example, crossing white matter fibers in the brain. In the case of breast imaging, diffusion kurtosis imaging is susceptible to intracellular structures such as membranes and organelles [61] and can offer a diffusion heterogeneity index sensitive to tumor microstructure in addition to a mean kurtosis map [62]. Notably, when the unsuppressed fat signal is corrected for, diffusion kurtosis analysis of the breast improves [63].

## 10. Magnetic Resonance Spectroscopy

## 11. Perspectives (Future Directions)

_{2}-weighted techniques have shown that the use of DCE and DWI in diagnosing breast cancer is beneficial [48,49,98], but T

_{2}-weighted MRI cannot be significantly helpful [51]. Indeed, DWI plus T

_{2}-weighted MRI are less sensitive than DCE [5]. In addition to the diagnostic context, DWI has been evaluated in therapy planning, such as the evaluation of tumor response to treatment.

_{1}, λ

_{2}, λ

_{3}, mean diffusivity, and λ1–λ3 are significantly reduced. Moreover, FA is significantly increased in breast cancer associated with pregnancy compared to normal lactating parenchyma [76]. DTI study in therapy has also shown that eigenvalues and mean diffusivity increase in response to neoadjuvant chemotherapy [79].

## 12. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Data Availability Statement

## Conflicts of Interest

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**Figure 1.**Signal decay of benign and malignant breast tissue in diffusion weighting imaging depending on baseline T

_{2}signal [33]. “Reprinted with permission from Ref. [33]. 2020, springer”. More details on “Copyright and Licensing” are available via the following link: https://link.springer.com/article/10.1007/s00330-019-06510-3.

**Figure 2.**A 58-year old woman with dense breasts and invasive ductal carcinoma. (

**a**) X-ray mammogram CC view and (

**b**) MLO view, (

**c**) DCE maximum intensity projection, (

**d**) axial T

_{1}-weighted fat saturated DCE-MRI, (

**e**) axial DWI, and (

**f**) ADC map [5]. “Reprinted with permission from Ref. [5]. 2011, springer”. More details on “Copyright and Licensing” are available via the following link: https://link.springer.com/article/10.1007/s00330-010-1890-8. White arrows indicated lesion in the images.

**Figure 3.**At the b value of 700–2100 s/mm

^{2}, the signal decay is slower, indicating a multi-exponential signal decay pattern of breast carcinoma [54]. Reprinted with permission from Ref. [54]. 2018, Wiley Online Library”. More details on “Copyright and Licensing” are available via the following link: https://onlinelibrary.wiley.com/doi/abs/10.1002/jmri.25904.

Ref. | λ_{1} | λ_{2} | λ_{3} | MD | FA | λ_{1}–λ_{3} |
---|---|---|---|---|---|---|

Noam Nissan et al. [76] in pregnancy-associated breast cancer | 1.17 ± 0.11 | 0.95 ± 0.11 | 0.74 ± 0.11 | 0.95 ± 0.11 | 0.25 ± 0.05± | 0.43 ± 0.07 |

Haran et al. [79] (median % change responders) | 55.7 (43.6–77) | 55.4 (42.3–74.2) | 61.5 (41.3–81.0) | 55.6 (42.4–71.8) | 1.3 (214.3–20.8) | 55.4 (42.4–100.1) |

Onaygil et al. [78] | 1.91 ± 0.30 * 1.27 ± 0.19 ** | 1.68 ± 0.28 * 1.01 ± 0.20 ** | 1.46 ± 0.27 * 0.81 ± 0.24 ** | 1.68 ± 0.27 * 1.03 ± 0.19 ** | 0.14 ± 0.05 * 0.24 ± 0.14 ** | 0.45 ± 0.17 * 0.48 ± 0.25 ** |

_{1}, λ

_{2}, λ

_{3}: eigenvalues, MD: mean diffusivity, FA: fractional anisotropy, λ

_{1}–λ

_{3}: empirical difference, ADC: apparent diffusion coefficient, *: benign, **: malignant.

Ref. | (ADC: ×10^{3} mm^{2}/s) Malignant | (ADC: ×10^{3} mm^{2}/s) Benign |
---|---|---|

Egnell et al. [52] b-values (0, 200, 600, 1200, 1800, 2400, 3000) s/mm ^{2} | =1.04 (0.96–1.20) | =1.75 (1.51–1.86) |

Pereira et al. [3] b-values (0, 250, 500, 750, and 1000) | 0.907 | 1.45 |

Sinha et al. [29] | 1.36 ± 0.36 | 2.01 ± 0.46 |

_{1}, λ

_{2}, λ

_{3}: eigenvalues, MD: mean diffusivity, FA: fractional anisotropy, λ

_{1}–λ

_{3}: empirical difference, ADC: apparent diffusion coefficient.

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Shahbazi-Gahrouei, D.; Aminolroayaei, F.; Nematollahi, H.; Ghaderian, M.; Gahrouei, S.S.
Advanced Magnetic Resonance Imaging Modalities for Breast Cancer Diagnosis: An Overview of Recent Findings and Perspectives. *Diagnostics* **2022**, *12*, 2741.
https://doi.org/10.3390/diagnostics12112741

**AMA Style**

Shahbazi-Gahrouei D, Aminolroayaei F, Nematollahi H, Ghaderian M, Gahrouei SS.
Advanced Magnetic Resonance Imaging Modalities for Breast Cancer Diagnosis: An Overview of Recent Findings and Perspectives. *Diagnostics*. 2022; 12(11):2741.
https://doi.org/10.3390/diagnostics12112741

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2022. "Advanced Magnetic Resonance Imaging Modalities for Breast Cancer Diagnosis: An Overview of Recent Findings and Perspectives" *Diagnostics* 12, no. 11: 2741.
https://doi.org/10.3390/diagnostics12112741