Tracking the mammary architectural features and detecting breast cancer with magnetic resonance diffusion tensor imaging

Diffuse optical spectroscopy of lactating and non-lactating human mammary physiology

Breastfeeding provides widely recognized advantages for infant and maternal health. Unfortunately, many women experience trouble with breastfeeding. Nevertheless, few suitable imaging modalities are available to study human lactation and determine the possible causes of breastfeeding problems. In this study, we apply broadband, quantitative diffuse optical spectroscopy (DOS) for this purpose. We present a study of fourteen lactating and eight similarly aged, premenopausal, non-lactating women to investigate the feasibility of DOS to study the optical and physiological differences between 1) lactating and non-lactating breasts, 2) the areolar and non-areolar region within the breast, and 3) lactating breasts before and after milk extraction. Our study shows that i) the median total hemoglobin concentration [tHb] of the lactating breast is 51% higher than for the non-lactating breast. ii) the median [tHb] of the lactating breast is 37% higher in the areolar region compared to the non-areolar region. iii) lactating breasts exhibit a positive median difference of 8% in [tHb] after milk extraction. Our findings are consistent with the expected physiological changes that occur during the lactation period. Importantly, we show that DOS provides unique insight into breast tissue composition and physiology, serving as a foundation for future application of the technique in lactation research.

MRI can accurately diagnose breast cancer during lactation

OBJECTIVE

To test the diagnostic performance of breast dynamic contrast-enhanced (DCE) MRI during lactation.

MATERIALS AND METHODS

Datasets of 198 lactating patients, including 66 pregnancy-associated breast cancer (PABC) patients and 132 controls, who were scanned by DCE on 1.5-T MRI, were retrospectively evaluated. Six blinded, expert radiologists independently read a single DCE maximal intensity projection (MIP) image for each case and were asked to determine whether malignancy was suspected and the background-parenchymal-enhancement (BPE) grade. Likewise, computer-aided diagnosis CAD MIP images were independently read by the readers. Contrast-to-noise ratio (CNR) analysis was measured and compared among four consecutive acquisitions of DCE subtraction images.

RESULTS

For MIP-DCE images, the readers achieved the following means: sensitivity 93.3%, specificity 80.3%, positive-predictive-value 70.4, negative-predictive-value 96.2, and diagnostic accuracy of 84.6%, with a substantial inter-rater agreement (Kappa = 0.673, p value < 0.001). Most false-positive interpretations were attributed to either the MIP presentation, an underlying benign lesion, or an asymmetric appearance due to prior treatments. CAD's derived diagnostic accuracy was similar (p = 0.41). BPE grades were significantly increased in the healthy controls compared to the PABC cohort (p < 0.001). CNR significantly decreased by 11-13% in each of the four post-contrast images (p < 0.001).

CONCLUSION

Breast DCE MRI maintains its high efficiency among the lactating population, probably due to a vascular-steal phenomenon, which causes a significant reduction of BPE in cancer cases. Upon validation by prospective, multicenter trials, this study could open up the opportunity for breast MRI to be indicated in the screening and diagnosis of lactating patients, with the aim of facilitating an earlier diagnosis of PABC.

KEY POINTS

• A single DCE MIP image was sufficient to reach a mean sensitivity of 93.3% and NPV of 96.2%, to stress the high efficiency of breast MRI during lactation. • Reduction in BPE among PABC patients compared to the lactating controls suggests that several factors, including a possible vascular steal phenomenon, may affect cancer patients. • Reduction in CNR along four consecutive post-contrast acquisitions highlights the differences in breast carcinoma and BPE kinetics and explains the sufficient conspicuity on the first subtracted image.

Probing lipids relaxation times in breast cancer using magnetic resonance spectroscopic fingerprinting

OBJECTIVES

To investigate the clinical relevance of the relaxation times of lipids within breast cancer and normal fibroglandular tissue in vivo, using magnetic resonance spectroscopic fingerprinting (MRSF).

METHODS

Twelve patients with biopsy-confirmed breast cancer and 14 healthy controls were prospectively scanned at 3 T using a protocol consisting of diffusion tensor imaging (DTI), MRSF, and dynamic contrast-enhanced (DCE) MRI. Single-voxel MRSF data was recorded from the tumor (patients) - identified using DTI - or normal fibroglandular tissue (controls), in under 20 s. MRSF data was analyzed using in-house software. Linear mixed model analysis was used to compare the relaxation times of lipids in breast cancer VOIs vs. normal fibroglandular tissue.

RESULTS

Seven distinguished lipid metabolite peaks were identified and their relaxation times were recorded. Of them, several exhibited statistically significant changes between controls and patients, with strong significance (p < 10^-3) recorded for several of the lipid resonances at 1.3 ppm (T₁ = 355 ± 17 ms vs. 389 ± 27 ms), 4.1 ppm (T₁ = 255 ± 86 ms vs. 127 ± 33 ms), 5.22 ppm (T₁ = 724 ± 81 ms vs. 516 ± 62 ms), and 5.31 ppm (T₂ = 56 ± 5 ms vs. 44 ± 3.5 ms, respectively).

CONCLUSIONS

The application of MRSF to breast cancer imaging is feasible and achievable in clinically relevant scan time. Further studies are required to verify and comprehend the underling biological mechanism behind the differences in lipid relaxation times in cancer and normal fibroglandular tissue.

KEY POINTS

•The relaxation times of lipids in breast tissue are potential markers for quantitative characterization of the normal fibroglandular tissue and cancer. •Lipid relaxation times can be acquired rapidly in a clinically relevant manner using a single-voxel technique, termed MRSF. •Relaxation times of T₁ at 1.3 ppm, 4.1 ppm, and 5.22 ppm, as well as of T₂ at 5.31 ppm, were significantly different between measurements within breast cancer and the normal fibroglandular tissue.

Diffusion tensor imaging and diffusion-weighted imaging on axillary lymph node status in breast cancer patients

PURPOSE This article will examine the usefulness of diffusion tensor imaging (DTI) and diffusion-weighted imaging (DWI) on the assessment of axillary lymph nodes (ALN) of breast cancer patients. METHODS Axillary lymph nodes in 66 breast cancer patients were examined by DTI and DWI, and the largest lymph node with increased cortical thickness in axilla was selected. Morphological features, apparent diffusion coefficient (ADC), volume anisotropy, and fractional anisotropy values were measured by using a special software. Imaging findings and histopathological results were recorded. RESULTS Metastatic ALN were detected in 43 (65.1%) patients. Cortical thickness of the metastatic ALN was significantly higher than the non-metastatic ALNs (P < .001), and the long-axis-to-shortaxis ratio was significantly lower in metastatic ALNs (P < .001). There was a statistically significant difference between the ALN status and fatty hilum presence (P < .001). Apparent diffusion coefficient values of metastatic ALNs were statistically lower than those of non-metastatic ALNs (P < .001) using a cutoff value of 1.26 × 10-3 mm2 /s for b=500 ADC and 1.21 × 10-3 mm2 /s for b=800 ADC which had 97.7% sensitivity and 91.3% specificity. Fractional anisotropy and volume anisotropy values were significantly different between both groups. A cutoff value of 0.47 for b-500 fractional anisotropy had 83.7% sensitivity, 69.6% specificity 69.6% positive predictive value, and 83.7% negative predictive value. A cutoff value of 0.33 for b=500 volume anisotropy had 76.7% sensitivity, 78.3% specificity, 86.8% positive predictive value, and 64.3% negative predictive value. CONCLUSION Apparent diffusion coefficient value of metastatic ALNs was found to be significantly lower than those of non-metastatic ALN, and DTI metrics of metastatic ALN were found to be significantly higher than those of non-metastatic ALN. Overall, ADC had a better diagnostic performance than morphological features, fractional anisotropy, and volume anisotropy. Diffusion tensor imagingderived diffusion metrics may be used to complement breast magnetic resonance imaging in the future after further standardization of the imaging parameters.

Breast MRI during pregnancy and lactation: clinical challenges and technical advances

The breast experiences substantial changes in morphology and function during pregnancy and lactation which affects its imaging properties and may reduce the visibility of a concurrent pathological process. The high incidence of benign gestational-related entities may further add complexity to the clinical and radiological evaluation of the breast during the period. Consequently, pregnancy-associated breast cancer (PABC) is often a delayed diagnosis and carries a poor prognosis. This state-of-the-art pictorial review illustrates how despite currently being underutilized, technical advances and new clinical evidence support the use of unenhanced breast MRI during pregnancy and both unenhanced and dynamic-contrast enhanced (DCE) during lactation, to serve as effective supplementary modalities in the diagnostic work-up of PABC.

Multidimensional Diffusion Magnetic Resonance Imaging for Characterization of Tissue Microstructure in Breast Cancer Patients: A Prospective Pilot Study

Diffusion-weighted imaging is a non-invasive functional imaging modality for breast tumor characterization through apparent diffusion coefficients. Yet, it has so far been unable to intuitively inform on tissue microstructure. In this IRB-approved prospective study, we applied novel multidimensional diffusion (MDD) encoding across 16 patients with suspected breast cancer to evaluate its potential for tissue characterization in the clinical setting. Data acquired via custom MDD sequences was processed using an algorithm estimating non-parametric diffusion tensor distributions. The statistical descriptors of these distributions allow us to quantify tissue composition in terms of metrics informing on cell densities, shapes, and orientations. Additionally, signal fractions from specific cell types, such as elongated cells (bin1), isotropic cells (bin2), and free water (bin3), were teased apart. Histogram analysis in cancers and healthy breast tissue showed that cancers exhibited lower mean values of "size" (1.43 ± 0.54 × 10-3 mm2/s) and higher mean values of "shape" (0.47 ± 0.15) corresponding to bin1, while FGT (fibroglandular breast tissue) presented higher mean values of "size" (2.33 ± 0.22 × 10-3 mm2/s) and lower mean values of "shape" (0.27 ± 0.11) corresponding to bin3 (p < 0.001). Invasive carcinomas showed significant differences in mean signal fractions from bin1 (0.64 ± 0.13 vs. 0.4 ± 0.25) and bin3 (0.18 ± 0.08 vs. 0.42 ± 0.21) compared to ductal carcinomas in situ (DCIS) and invasive carcinomas with associated DCIS (p = 0.03). MDD enabled qualitative and quantitative evaluation of the composition of breast cancers and healthy glands.

Physiologic and hypermetabolic breast 18-F FDG uptake on PET/CT during lactation

OBJECTIVE

To investigate the patterns of breast cancer-related and lactation-related ¹⁸F-FDG uptake in breasts of lactating patients with pregnancy-associated breast cancer (PABC) and without breast cancer.

METHODS

¹⁸F-FDG-PET/CT datasets of 16 lactating patients with PABC and 16 non-breast cancer lactating patients (controls) were retrospectively evaluated. Uptake was assessed in the tumor and non-affected lactating tissue of the PABC group, and in healthy lactating breasts of the control group, using maximum and mean standardized uptake values (SUVmax and SUVmean, respectively), and breast-SUVmax/liver-SUVmean ratio. Statistical tests were used to evaluate differences and correlations between the groups.

RESULTS

Physiological uptake in non-breast cancer lactating patients' breasts was characteristically high regardless of active malignancy status other than breast cancer (SUVmax = 5.0 ± 1.7, n = 32 breasts). Uptake correlated highly between the two breasts (r = 0.61, p = 0.01), but was not correlated with age or lactation duration (p = 0.24 and p = 0.61, respectively). Among PABC patients, the tumors demonstrated high ¹⁸F-FDG uptake (SUVmax = 7.8 ± 7.2, n = 16), which was 326-643% higher than the mostly low physiological FDG uptake observed in the non-affected lactating parenchyma of these patients (SUVmax = 2.1 ± 1.1). Overall, ¹⁸F-FDG uptake in lactating breasts of PABC patients was significantly decreased by 59% (p < 0.0001) compared with that of lactating controls without breast cancer.

CONCLUSION

¹⁸F-FDG uptake in lactating tissue of PABC patients is markedly lower compared with the characteristically high physiological uptake among lactating patients without breast cancer. Consequently, breast tumors visualized by ¹⁸F-FDG uptake in PET/CT were comfortably depicted on top of the background ¹⁸F-FDG uptake in lactating tissue of PABC patients.

KEY POINTS

• FDG uptake in the breast is characteristically high among lactating patients regardless of the presence of an active malignancy other than breast cancer. • FDG uptake in non-affected lactating breast tissue is significantly lower among PABC patients compared with that in lactating women who do not have breast cancer. • In pregnancy-associated breast cancer patients, ¹⁸F-FDG uptake is markedly increased in the breast tumor compared with uptake in the non-affected lactating tissue, enabling its prompt visualization on PET/CT.

Current and Emerging Magnetic Resonance-Based Techniques for Breast Cancer

Breast cancer is the most commonly diagnosed cancer among women worldwide, and early detection remains a principal factor for improved patient outcomes and reduced mortality. Clinically, magnetic resonance imaging (MRI) techniques are routinely used in determining benign and malignant tumor phenotypes and for monitoring treatment outcomes. Static MRI techniques enable superior structural contrast between adipose and fibroglandular tissues, while dynamic MRI techniques can elucidate functional characteristics of malignant tumors. The preferred clinical procedure-dynamic contrast-enhanced MRI-illuminates the hypervascularity of breast tumors through a gadolinium-based contrast agent; however, accumulation of the potentially toxic contrast agent remains a major limitation of the technique, propelling MRI research toward finding an alternative, noninvasive method. Three such techniques are magnetic resonance spectroscopy, chemical exchange saturation transfer, and non-contrast diffusion weighted imaging. These methods shed light on underlying chemical composition, provide snapshots of tissue metabolism, and more pronouncedly characterize microstructural heterogeneity. This review article outlines the present state of clinical MRI for breast cancer and examines several research techniques that demonstrate capacity for clinical translation. Ultimately, multi-parametric MRI-incorporating one or more of these emerging methods-presently holds the best potential to afford improved specificity and deliver excellent accuracy to clinics for the prediction, detection, and monitoring of breast cancer.

Role of Breast MRI in the Evaluation and Detection of DCIS: Opportunities and Challenges

Historically, breast magnetic resonance imaging (MRI) was not considered an effective modality in the evaluation of ductal carcinoma in situ (DCIS). Over the past decade this has changed, with studies demonstrating that MRI is the most sensitive imaging tool for detection of all grades of DCIS. It has been suggested that not only is breast MRI the most sensitive imaging tool for detection but it may also detect the most clinically relevant DCIS lesions. The role and outcomes of MRI in the preoperative setting for patients with DCIS remains controversial; however, several studies have shown benefit in the preoperative evaluation of extent of disease as well as predicting an underlying invasive component. The most common presentation of DCIS on MRI is nonmass enhancement (NME) in a linear or segmental distribution pattern. Maximizing breast MRI spatial resolution is therefore beneficial, given the frequent presentation of DCIS as NME on MRI. Emerging MRI techniques, such as diffusion-weighted imaging (DWI), have shown promising potential to discriminate DCIS from benign and invasive lesions. Future opportunities including advanced imaging visual techniques, radiomics/radiogenomics, and machine learning / artificial intelligence may also be applicable to the detection and treatment of DCIS. Level of Evidence: 3 Technical Efficacy Stage: 3 J. Magn. Reson. Imaging 2019. J. Magn. Reson. Imaging 2020;52:697-709.

Monitoring In-Vivo the Mammary Gland Microstructure during Morphogenesis from Lactation to Post-Weaning Using Diffusion Tensor MRI

Lactation and the return to the pre-conception state during post-weaning are regulated by hormonal induced processes that modify the microstructure of the mammary gland, leading to changes in the features of the ductal / glandular tissue, the stroma and the fat tissue. These changes create a challenge in the radiological workup of breast disorder during lactation and early post-weaning. Here we present non-invasive MRI protocols designed to record in vivo high spatial resolution, T₂-weighted images and diffusion tensor images of the entire mammary gland. Advanced imaging processing tools enabled tracking the changes in the anatomical and microstructural features of the mammary gland from the time of lactation to post-weaning. Specifically, by using diffusion tensor imaging (DTI) it was possible to quantitatively distinguish between the ductal / glandular tissue distention during lactation and the post-weaning involution. The application of the T₂-weighted imaging and DTI is completely safe, non-invasive and uses intrinsic contrast based on differences in transverse relaxation rates and water diffusion rates in various directions, respectively. This study provides a basis for further in-vivo monitoring of changes during the mammary developmental stages, as well as identifying changes due to malignant transformation in patients with pregnancy associated breast cancer (PABC).

Diffusion-weighted breast MRI: Clinical applications and emerging techniques

Diffusion-weighted MRI (DWI) holds potential to improve the detection and biological characterization of breast cancer. DWI is increasingly being incorporated into breast MRI protocols to address some of the shortcomings of routine clinical breast MRI. Potential benefits include improved differentiation of benign and malignant breast lesions, assessment and prediction of therapeutic efficacy, and noncontrast detection of breast cancer. The breast presents a unique imaging environment with significant physiologic and inter-subject variations, as well as specific challenges to achieving reliable high quality diffusion-weighted MR images. Technical innovations are helping to overcome many of the image quality issues that have limited widespread use of DWI for breast imaging. Advanced modeling approaches to further characterize tissue perfusion, complexity, and glandular organization may expand knowledge and yield improved diagnostic tools.

LEVEL OF EVIDENCE

5 J. Magn. Reson. Imaging 2016 J. Magn. Reson. Imaging 2017;45:337-355.

Diffusion weighted imaging: Technique and applications

Diffusion weighted imaging (DWI) is a method of signal contrast generation based on the differences in Brownian motion. DWI is a method to evaluate the molecular function and micro-architecture of the human body. DWI signal contrast can be quantified by apparent diffusion coefficient maps and it acts as a tool for treatment response evaluation and assessment of disease progression. Ability to detect and quantify the anisotropy of diffusion leads to a new paradigm called diffusion tensor imaging (DTI). DTI is a tool for assessment of the organs with highly organised fibre structure. DWI forms an integral part of modern state-of-art magnetic resonance imaging and is indispensable in neuroimaging and oncology. DWI is a field that has been undergoing rapid technical evolution and its applications are increasing every day. This review article provides insights in to the evolution of DWI as a new imaging paradigm and provides a summary of current role of DWI in various disease processes.