Clinical application of gadoterate meglumine in abbreviated dynamic contrast-enhanced magnetic resonance imaging of the breast with ultrafast imaging: a single-center retrospective study

Introduction

Globally, breast cancer is the most commonly diagnosed cancer in women, and in the United States, it ranks as the second most common cause of cancer-related death among females, following lung cancer. Advances in understanding the diverse biological profiles of breast tumors and their varied clinical outcomes have significantly increased the complexity and sophistication required for accurate diagnosis. Digital mammography, also called full-field digital mammography (FFDM), has been the most widely available imaging modality for screening breast cancer. The major drawback of FFDM is the effect of summation of breast tissue which can obscure some breast lesions. Mammography remains an economically efficient modality and has played a crucial role in facilitating earlier detection of breast malignancies; however, its sensitivity is notably limited, dropping below 50% in women with dense breast tissue and reaching only around 60% in those with non-dense yet structurally complex breasts (1).

Currently, dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is considered the most sensitive imaging technique for breast cancer detection, providing a significant diagnostic benefit compared to mammography (2). Magnetic resonance imaging (MRI) identifies approximately 14.7 to 15.5 cases of breast cancer per 1,000 examinations in both high-risk and average-risk populations, outperforming mammography and ultrasound (US) in detection yield (2,3). MRI is also substantially more effective than that of digital breast tomosynthesis (DBT) and US, which had a supplement cancer detection rate of 1.2 per 1,000 cases for DBT and 3.5–4.4 per 1,000 cases for US, respectively (4,5).

The interval cancer rate associated with MRI is markedly lower, ranging from 0% to 11% (2,6-9), as compared with other modalities (10,11). Moreover, MRI contributes significantly to improved survival outcomes by enabling the early detection of biologically aggressive and high-grade breast tumors at smaller sizes and earlier stages, often before axillary lymph node involvement, outperforming other imaging modalities (10,11). Given these cumulative advantages, broadening DCE-MRI’s involvement as a stand-alone screening tool is worthwhile.

Gadolinium-based contrast agents (GBCAs) exhibit diverse physicochemical characteristics, among which T1 relaxivity is particularly critical; higher T1 relaxivity enhances signal intensity, thereby improving the detection and delineation of lesions in the brain and breast (12-15). The ionic nature of GBCAs represents another key physicochemical property. These agents may exist in either ionic or non-ionic forms, and this variation in molecular charge can influence tissue contrast uptake, particularly in structures rich in negatively charged elements such as mucopolysaccharides (16,17). It is well recognized that malignant breast lesions are abundant in mucopolysaccharide acids (18-20).

Gadoterate meglumine (Dotarem^®, Guerbet, Raleigh, NC, USA), a macrocyclic and ionic GBCA, exhibits lower relaxivity, osmolality, and viscosity relative to gadobenate dimeglumine (21) and is utilized for breast MRI in certain clinical settings (22). Macrocyclic GBCAs feature a pre-formed, rigid ligand architecture that tightly encapsulates the Gd³⁺ ion, resulting in enhanced thermodynamic and kinetic stability. Compared to their linear counterparts, macrocyclic GBCAs demonstrate superior resistance to dechelation, thereby reducing the likelihood of gadolinium release within the body.

Dotarem is employed to enhance the visibility of vascular structures and soft tissues during MRI examinations. In breast imaging, Dotarem facilitates a more detailed and comprehensive visualization of lesions, aiding radiologists in the early detection and characterization of breast abnormalities by clearer delineation of lesions’ conspicuity (22-25). Intravenous gadoteric acid has been shown in this noninterventional surveillance research to be a safe and efficient contrast agent for use in magnetic resonance (MR) mammography (22). Some investigations using Dotarem for breast MRI imaging have been conducted (22,26-28). However, there has been paucity of quantitative kinetic analysis of signal enhancement due to Dotarem in breast imaging. There have been no prior studies evaluating the effectiveness of Dotarem with high temporal resolution sampling—ultrafast DCE-MRI. The present study provides valuable new information concerning use of Dotarem pharmacokinetics to improve breast cancer. The aim of this study was to evaluate the feasibility and clinical application of Dotarem in DCE-MRI of the breast for breast cancer detection, focusing on quantitative pharmacokinetic analysis with ultrafast DCE-MRI. We present this article in accordance with the STARD reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-653/rc).

Methods

This was a retrospective observational cross-sectional study performed at the Breast Imaging Center and MRI Research Center (MRIRC), Biological Science Division, Department of Radiology, University of Chicago, conducted between November 7^th, 2020, and May 30^th, 2023. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments, also in compliance with the Health Insurance Portability and Accountability Act. The study was approved by the Institutional Review Board of the University of Chicago (No. IRB19-2074), and the requirement for written informed consent was waived by institutional policy and the retrospective study design.

Patient recruitment

The patient cohort consisted of women who underwent breast ultrafast DCE-MRI with Dotarem at Department of Radiology, University of Chicago during the study period for various diagnostic indications, including breast pain, breast lumpiness, palpable breast masses, or nipple discharge. Women included in this study were those who underwent ultrafast DCE-MRI of the breast due to either suspicious imaging findings suggestive of malignancy—classified as Breast Imaging Reporting and Data System (BI-RADS) category 4 or 5—or a confirmed diagnosis of breast cancer (BI-RADS category 6). As defined by the BI-RADS lexicon, category 4 lesions present an estimated malignancy probability ranging from 2% to 95%, whereas category 5 lesions are associated with a likelihood of malignancy exceeding 95%. A total of 50 women were initially recruited for this study to evaluate breast lesions and calculate semi-quantitative MRI parameters. One patient was excluded from this study due to a spine condition that impaired her ability to maintain a stable prone position, resulting in suboptimal image quality. This yielded a final cohort of 49 eligible patients. All index lesions were confirmed by core needle biopsy for histopathologic diagnosis.

MRI acquisition

All MRI scans were conducted with patients positioned prone, utilizing a 3.0 Tesla Philips TX platform (Achieva or Ingenia; Philips Healthcare, Best, The Netherlands) equipped with a specialized 16-channel bilateral breast coil (MammoTrak, Philips Healthcare). A standard dose of Dotarem (0.1 mM/kg) was administered intravenously through the antecubital vein using an automated power injector (Spectris Solaris EP, Medrad, PA, USA) at a flow rate of 2.5 mL/s, followed by a 20 mL saline flush. Abbreviated DCE-MRI that includes an ultrafast DCE-MRI sequence in the axial orientation. The acquisition parameters for the ultrafast and high spatial resolution sequence are summarized in Table 1. The ultrafast DCE-MRI protocol included five pre-contrast acquisitions followed by nineteen post-contrast dynamic phases.

Image analysis

Image data were processed using a custom-developed MATLAB-based platform in conjunction with the open-source software 3D Slicer (29). Motion correction for ultrafast DCE-MRI was achieved through a non-rigid image registration technique (30). A semi-automatic volumetric segmentation of the lesions was conducted using the ‘Level Tracing’ algorithm in 3D Slicer, applied to subtraction ultrafast DCE-MRI (the last post-contrast image-pre-contrast image) by a radiologist with 5 years of experience of breast imaging. The ‘Level Tracing’ algorithm delineated lesion boundaries by automatically identifying voxels of similar intensity within the same imaging slice. The volumetric tumor region was then captured using a seeded region growing algorithm, which expanded the initial seed area into a three-dimensional (3D) tumor volume (31). A slice of normal background parenchymal enhancement (BPE) at the central part of the contralateral breast was selected randomly without any suspicious lesion or benign pathology and then segmented. Then a region of interest (ROI)-based data analysis was used for the quantitative analysis.

Average signal enhancement in each lesion and the selected normal parenchymal ROIs was calculated. The signal enhancement ratio (SER) over time per ROI, PSE_ROI(t)=Spost(t)−Spre¯Spre¯×100%, was fitted with an empirical mathematical model (EMM):

PSE_ROI(t)=A⋅(α⋅(t−t0))21+(α⋅(t−t0))2⋅(t>t0)[1]

where Spost(t) denotes the average signal intensity over the ROI at time 't', and Spre is the average signal intensity over ROI from the pre-contrast image, A is the upper limit of initial contrast agent uptake, α(s−1) is the rate of contrast agent uptake, t0 is the bolus arrival time (BAT, s). Then, the maximum enhancement slope is calculated as A*α(s−1).

The secondary parameter, area under the enhancement-time curve over 30 seconds (AUC30) for PSE(t), was calculated by integrating Eq. [1], i.e.,

AUC30=∫0t30A⋅(αt)21+(αt)2dt[2]

where t₃₀ =30 s.

The time to reach 90% of the maximum enhancement (T90) was calculated and used as a surrogate metric for time-to-peak enhancement. In additional to EMM, Tofts model was used to obtain quantitative pharmacokinetic parameters (32). Contrast agent concentration curve C(t) (in minutes) in lesions and normal parenchyma ROI was calculated using a previously published method (33) and fitted with the Tofts model:

C(t)=∫0tCp(τ)e−Ktransve⋅(1−τ)dτ[3]

where K^trans (min⁻¹) refers to the transfer constant, v_e denotes the fractional volume of the extravascular extracellular space, and Cp(t) is the arterial input function (AIF) that was traced over an ROI within the central area of the descending thoracic aorta and average from three adjacent slices for individual patient. BAT in the aorta was calculated using Eq. [1]. BATs in lesions and parenchyma were calculated relative to the BAT in the thoracic aorta.

Statistical analysis

All statistical computations were carried out using STATA statistical software version 16 (Stata Corp, College Station, TX, USA). The threshold for statistical significance was set at a P value of less than 0.05. Continuous variables were tested for normality through visual inspection of a histogram and a normal density plot. Data that are normally distributed are reported as mean and standard deviation (SD), while non-normally distributed data are summarized using median and interquartile range (IQR).

Percentage values, mean, and SD of clinicopathological data were calculated for patients’ variables. The mean and SD of all kinetic parameters for each lesion dataset were calculated over ROI in the lesion region. To compare mean values of various kinetic parameters between malignant tumors and background parenchyma, as well as average ratios between malignant lesions and benign lesions or BPE, appropriate unpaired statistical tests were applied based on data distribution and variance. Welch’s t-test was used for normally distributed variables due to its robustness in handling unequal variances and sample sizes. For non-normally distributed data, the Mann-Whitney U test was employed as a nonparametric alternative for comparing independent groups.

Sensitivity and specificity were calculated at the optimal cutoff point, as determined by the maximum Youden’s index, along with their corresponding 95% confidence intervals (CIs).

To address the limitations associated with a small sample size and class imbalance—particularly the underrepresentation of benign lesions—we first calculated the area under the receiver operating characteristic (ROC) curve (AUC) values for each kinetic parameter to assess the discriminating performance of malignant lesions from background parenchymal enhancement (BPE) and benign lesions. Then, we applied nonparametric bootstrap resampling (1,000 iterations) to compute their corresponding 95% CIs. This resampling technique enables the estimation of variability in classifier performance under conditions of data imbalance and enhances the robustness of AUC interpretation by providing a distribution-based measure of uncertainty.

Results

In total, 49 eligible patients presenting with pathologically proven breast lesions, including suspicious abnormalities and known cases of breast malignancy, were enrolled in this study (Figure 1). There are 47 patients with 60 malignant lesions (cancer cohort) and 2 patients with benign lesions (one is chronic mastitis; the other is a phyllodes tumor, borderline subtype). The age of all patients ranged from 34 to 81 years, with the median of all patients being 49 years and the median in the cancer cohort being 52 years. Patient demographic data and lesion characteristics are summarized in Table 2.

Figure 1 Study flow diagram of patient enrollment. DCE-MRI, dynamic contrast-enhanced magnetic resonance imaging; MRI, magnetic resonance imaging.

The average and SD of kinetic parameters of 60 malignant lesions and 2 benign lesions with 47 BPE and average ratio of malignant lesions to BPE were summarized in Table 3. Two cases were excluded from BPE cohort due to silicone implant insertions. All kinetic parameters except v_e are significantly different in malignant lesions versus BPE based on the Welch’s t-test and the Mann-Whitney U test (P<0.05).

A scatter plot of the average percentage in signal enhancement from the ROIs for all malignant lesions and 47 ROIs for BPE with respective EMM fits is presented in Figure 2. Most malignant lesions were more strongly enhanced than parenchyma, especially within the first minute after contrast injection.

Figure 2 Scattered plot of average signal enhancement for all malignant lesions (red) (n=60) and BPE (blue) (n=47) with respective EMM fits. BPE, background parenchymal enhancement; EMM, empirical mathematical model.

Diagnostic accuracy in terms of sensitivity and specificity with a 95% CI of each kinetic parameter for discriminating breast malignant lesions from background parenchyma and benign lesions is demonstrated in Table 4.

Figure 3 showed the ROC curves for the evaluated kinetic parameters: A, α, A*α, AUC30, BAT, K^trans, and v_e. The highest discriminative performance of malignant lesions from BPE and benign lesions with AUC values were observed for A*α and AUC30, both at 0.963, with slightly different 95% CI (0.918–0.995 and 0.917–0.996, respectively), indicating comparable but not statistically identical performance. A and K^trans also showed strong diagnostic accuracy, with AUCs of 0.955 (95% CI: 0.915–0.985) and 0.938 (95% CI: 0.885–0.977), respectively. Rate of contrast agent uptake (α) demonstrated moderate performance (AUC =0.868; 95% CI: 0.793–0.928), while BAT and v_e performed poorly (AUCs =0.580 and 0.579). Optimal thresholds for each parameter are indicated on the curves. These results highlight A*α, AUC30, A, and K^trans as the most effective diagnostic markers in this analysis, as evidenced by their high AUCs and narrow CIs.

Figure 3 ROC curve analysis of multiple kinetic parameters in differentiating malignant lesions from BPE and benign lesions. ROC curves are shown for seven parameters: A, α, A*α, AUC30, BAT, K^trans, and v_e. Each curve includes a 95% CI generated by 1,000 bootstrap iterations. Optimal diagnostic thresholds were determined by maximizing the Youden index and are marked on each curve. A, upper limit of initial contrast agent uptake; α, rate of contrast agent uptake; A*α, maximum enhancement slope; AUC, area under receiver operating characteristic curve; AUC30, area under the enhancement-time curve over 30 seconds; BAT, bolus arrival time; BPE, background parenchymal enhancement; CI, confidence interval; ROC, receiver operating characteristic; K^trans, transfer constant; v_e, fractional volume of the extravascular extracellular space.

An example case of the patients with malignant breast lesions was demonstrated in Figure 4, which shows the differentiation of breast tumors from BPE.

Figure 4 A 51-year-old woman presented with a palpable mass in the left breast. Her abbreviated breast MRI showed a 28 mm × 25 mm × 41 mm irregular enhancing mass at the outer part of the left breast. (A-C) Breast DCE-MRI with ultrafast DCE-MRI in axial T1WI. (A) A slice of patient image at the last time points of the ultrafast series. (B) A slice of subtraction image (post-contrast-pre-contrast) at the last time points of the ultrafast series; tumor ROI is marked as red. (C) MIP of subtraction image (post-contrast-pre-contrast); tumor ROI is marked as red. (D) A plot of PSE for the tumor (red) and BPE (blue). The surgical pathologic result was proven to be IDC, grade 1. Scale bar =10 mm. DCE-MRI, dynamic contrast-enhanced magnetic resonance imaging; BPE, background parenchymal enhancement; IDC, invasive ductal carcinoma; MIP, maximum intensity projection; PSE, percentage signal enhancement; ROI, region of interest; T1WI, T1-weighted image.

Discussion

Kinetic parameters for detecting breast lesions in MRI images

Assuming that breast cancers are highly permeable, the mean K^trans (0.41 min⁻¹) is equivalent to blood flow of 40 mLs/100 gms of tissue/minute—which is similar to—but somewhat higher than measurements of breast cancer blood flow using other methods (34-37). Other groups have reported K^trans either much higher (36) or much lower (34,35,37) than measurements obtained using other methods. In addition, the large range of K^trans values are consistent with the inter-tumor variation in aggressiveness and angiogenesis.

Numerous prior studies have proposed that quantitative kinetic analysis holds promise for improving the detection of malignant lesions. Xu et al. (36) studied the difference in mass performance in DCE-MRI [gadopentetate dimeglumine (Gd-DTPA); Magnevist] characteristics between low-risk and non-low-risk breast cancer recurrences, revealing that low-risk tumors showed significantly lower K^trans than non-low-risk breast cancer recurrences. The mean K^trans of low-risk invasive ductal carcinoma (IDC) tumors is 0.928 (SD =0.630), versus that of non-low-risk tumors, which is 1.275 (SD =0.665, P=0.043).

In 2019, Wu et al. (34) conducted a quantitative evaluation of vascular characteristics obtained from ultrafast DCE-MRI using MultiHance to differentiate malignant from benign breast tumors. Applying the Tofts-Kety model pharmacokinetics model, they reported a mean K^trans value of 0.073 for malignant lesions, with a range of 0.013 to 0.14.

In 2023, DiCarlo et al. (35) designed a method to select an optimal time point to measure DCE-MRI signal intensity and analyzed the pharmacokinetic parameter K^trans with the Kety-Tofts model. Their analysis reported a median K^trans for low-risk IDC of 0.07 with an IQR of 0.04.

There are very few studies measured the contrast pharmacokinetic analysis for breast lesion and BPE. In 2017, Kim et al. (37) assessed separation of benign and malignant breast lesions based on routine diagnostic breast MRI exams and with a single dose of Gd-DTPA (Magnevist) and MRI-guided biopsy when necessary. The extended Tofts model was utilized to derive kinetic parameters, and dynamic enhancement in the background parenchyma was assessed using a fibroglandular tissue mask created through principal component analysis (PCA). This analysis revealed that patients with malignancy had significantly higher median K^trans values in both the malignant lesion (0.081 min⁻¹) and background parenchyma (0.032 min⁻¹) compared to those without malignancy, who demonstrated median values of 0.056 and 0.017 min⁻¹, respectively.

We used the AIF of individual patients and calculated the concentration as a function of time of the thoracic aorta. Use of a rapidly sampled AIF for each patient corrects of interpatient variability and increases sensitivity to rapid enhancement in cancers. These could be one reason why there has been a very large difference between cancer and parenchyma for K^trans compared to the previous studies in Kim et al. (37,38).

Moreover, according to the AUC of each kinetic parameter for discriminating malignancies from BPE and benign lesions, it was found that A*α, AUC30, A, and K^trans demonstrated excellent discriminating performance for cancer detection. Furthermore, α is good level for differentiation between malignancies from BPE and benign lesions. BAT and v_e do not provide reliable discrimination with the current data acquisition methods. BAT is noise-sensitive and dependent on temporal resolution. The accuracy of v_e may be impacted by the limited contrast washout period, given the postcontrast ultrafast DCE-MRI series spans only 65 to 85 seconds. In the future validation, a unidirectional model may be more appropriate for a truncated ultrafast DCE-MRI series, particularly if additional time points can be sampled within the first 15 second post-contrast, when it is reasonable to assume negligible water exchange from the extravascular extracellular space into the blood plasma compartment (39). Improved methods for data acquisition may result in more accurate measurements of BAT and v_e.

Ultrafast and BPE

In this study, the use of Dotarem resulted in a lower mean percentage enhancement of background parenchymal tissue compared to values reported by Pineda et al. (38), who employed gadobenate dimeglumine (MultiHance) as the contrast agent. This comparison was derived from scatterplot evaluations of average signal enhancement in both malignant lesions and BPE, along with their respective fits based on the EMM. Given the observation that godoterate meglumine (Dotarem) had lower BPE signal enhancement, it may be advantageous for identifying suspicious breast lesions during breast MRI screening settings, particularly in patients who are young or in premenopausal status and may have fluctuating BPE during the menstrual cycle (40). For low-grade ductal carcinoma in situ (DCIS), which inherently had little to mild enhancement, there might also be advantages.

Ultrafast usefulness and future application

Ultrafast sampling for DCE-MRI is needed to measure blood flow in tumors accurately, differentiate between more and less aggressive cancers, and enhance the contrast between cancer and benign (34,38). For the average breast cancer in the present cohort, the initial 30% of enhancement (which contains critical diagnostic information) occurs within 5–7 seconds. Our current sampling rate, 3.5 seconds per image, gives us 1–2 data points during the important phase of enhancement. This is helpful, but we hope to increase to even higher temporal resolution to obtain over twice as many data points during the first 5 seconds of enhancement. In addition, the average width of the AIF is about 7 seconds, and our current sampling rate gives us about 2 points to sample most of the AIF. Again, this is useful, but significantly higher temporal resolution would be beneficial.

The combination of early kinetic parameters derived from ultrafast DCE-MRI and morphologic detail obtained from high-spatial-resolution post-contrast imaging offers a complementary approach that may improve the dynamic characterization and diagnostic performance of breast lesion assessment on MRI (38).

The current study has some limitations. First, the dataset in this study is relatively small. A larger sample size should be explored in future studies. Second, not every case had a baseline MRI scan before receiving treatment. In certain cases, patients had already undergone neoadjuvant chemotherapy, which may have impacted the kinetic parameters under analysis. Third, this patient cohort has a limited number of benign lesions. In addition, there is a substantial class imbalance of malignant and benign lesions. This imbalance reflects the clinical nature of our retrospective diagnostic cohort, which included patients referred for MRI due to suspicious findings rather than routine screening. As a result, the robustness of specificity estimates and the generalizability of AUC values are limited. A larger benign cohort would provide better clarity in assessing the discriminating power between malignant and benign lesions. Furthermore, AIFs were extracted on a subject-specific basis from thoracic arteries, whose dimensions were smaller than the 3×3 voxel regions employed to calculate the time course of average signal intensity. The accuracy of AIF estimation may be compromised by partial volume effects and motion artifacts, potentially leading to reduced measurements of both the enhancement rate and peak amplitude. This measurement inaccuracy could lead to overestimated values of K^trans and v_e. Additionally, a larger dataset with a diverse range of pathologic subtypes would facilitate the examination of correlations between lesion characteristics (such as cancer classification or grading) and could offer new insights into kinetic parameters for distinguishing between cancer subtypes.

Conclusions

Based on quantitative pharmacokinetic analysis in ultrafast DCE-MRI, the study demonstrated the viability of utilizing Dotarem as an intravenous GBCA for DCE-MRI of the breast with preserved clinical success in breast cancer detection.

Acknowledgments

The authors thank to Guerbet, USA for support and Brenda Gonzales for assistance as research coordinator.

Footnote

Reporting Checklist: The authors have completed the STARD reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-653/rc

Data Sharing Statement: Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-653/dss

Funding: This work was supported by Guerbet, USA (No. CRS1001252).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-653/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments, also in compliance with the Health Insurance Portability and Accountability Act. The study was approved by the Institutional Review Board of the University of Chicago (No. IRB19-2074), and the requirement for written informed consent was waived by institutional policy and the retrospective study design.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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