Skip to main content
Insights into Imaging
Download PDF

Optimizing ultrafast dynamic contrast-enhanced MRI scan duration in the differentiation of benign and malignant breast lesions

Insights into Imaging volume 15, Article number: 112 (2024) Cite this article

Abstract

Objective

To determine the optimal scan duration for ultrafast DCE-MRI in effectively differentiating benign from malignant breast lesions.

Methods

The study prospectively recruited participants who underwent breast ultrafast DCE-MRI from September 2021 to March 2023. A 30-phase breast ultrafast DCE-MRI on a 3.0-T MRI system was conducted with a 4.5-s temporal resolution. Scan durations ranged from 40.5 s to 135.0 s, during which the analysis is performed at three-phase intervals, forming eight dynamic sets (scan duration [SD]40.5s: 40.5 s, SD54s: 54.0 s, SD67.5s: 67.5 s, SD81s: 81.0 s, SD94.5s: 94.5 s, SD108s: 108.0 s, SD121.5s: 121.5 s, and SD135s: 135.0 s). Two ultrafast DCE-MRI parameters, maximum slope (MS) and initial area under the curve in 60 s (iAUC), were calculated for each dynamic set and compared between benign and malignant lesions. Areas under the receiver operating characteristic curve (AUCs) were used to assess their diagnostic performance.

Results

A total of 140 women (mean age, 47 ± 11 years) with 151 lesions were included. MS and iAUC from eight dynamic sets exhibited significant differences between benign and malignant lesions (all p < 0.05), except iAUC at SD40.5s. The AUC of MS (AUC = 0.804) and iAUC (AUC = 0.659) at SD67.5s were significantly higher than their values at SD40.5s (AUC = 0.606 and 0.516; corrected p < 0.05). No significant differences in AUCs for MS and iAUC were observed from SD67.5s to SD135s (all corrected p > 0.05).

Conclusions

Ultrafast DCE-MRI with a 67.5-s scan duration appears optimal for effectively differentiating malignant from benign breast lesions.

Critical relevance statement

By evaluating scan durations (40.5–135 s) and analyzing two ultrafast DCE-MRI parameters, we found a scan duration of 67.5 s optimal for discriminating between these lesions and offering a balance between acquisition time and diagnostic efficacy.

Key Points

  • Ultrafast DCE-MRI can effectively differentiate malignant from benign breast lesions.

  • A minimum of 67.5-sec ultrafast DCE-MRI scan duration is required to differentiate benign and malignant lesions.

  • Extending the scan duration beyond 67.5 s did not significantly improve diagnostic accuracy.

Graphical Abstract

Introduction

Breast cancer is currently the most prevalent cancer in women worldwide, and has an increasing incidence [1]. Accurate differential diagnosis of breast lesions is of paramount clinical importance as it directly influences patient management, treatment decisions, and prognosis. Among the various imaging modalities employed in differentiating benign from malignant breast lesions, dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has become the most sensitive modality over the past two decades [2]. Remarkably, DCE-MRI excels, especially in the realm of malignancy exclusion, with a consistently high negative predictive value (NPV) typically exceeding 90% [3,4,5]. The conventional kinetic analysis in DCE-MRI involves constructing time-intensity curves based on pre-contrast, initial, and delayed phase images to improve the specificity in assessing breast lesions, ending up with a considerable scan time commitment of 6–10 min [6]. As a result, the prolonged scan duration of DCE-MRI scans has limited its routine clinical use and broader applications [7, 8].

Ultrafast DCE-MRI has been introduced as a relatively novel imaging technique that documents the early influx of contrast agents with exceptional temporal resolution while preserving necessary diagnostic spatial resolution [9, 10]. This technique allows for acquiring images at multiple time points, particularly in the early post-contrast phase (≤ 2 min), and generates a plurality of kinetic parameters reflective of wash-in. Previous studies have reported promising results of ultrafast DCE-MRI parameters in discriminating between benign and malignant breast lesions [11,12,13,14]. For instance, Onishi et al demonstrated that the initial area under gadolinium contrast agent concentration (IAUGC) was significantly greater in breast cancer compared to benign lesions [12]. Additionally, Mann et al suggested that the maximum slope (MS) of malignant breast lesions is higher than that of benign lesions, providing a basis for differentiating malignant from benign breast lesions [13].

Despite these advancements, the standardization of ultrafast DCE-MRI scan duration remains unresolved. Currently, the acquisition times vary across institutions and studies, ranging from 60 s to 135 s (Table 1) [11, 12, 14,15,16,17,18,19,20,21,22,23,24,25]. A comparative analysis of these varying scan durations within the same cohort is needed to identify the optimal time frame for effectively discriminating between benign and malignant breast lesions.

Table 1 Summary of ultrafast DCE-MRI scan duration in different studies
Full size table

Therefore, our study seeks to explore how different scan durations in ultrafast DCE-MRI affect the performance of its derived parameters in distinguishing between benign and malignant breast lesions. The goal is to establish the optimal scan duration that minimizes acquisition time while retaining robust discriminatory ability.

Materials and methods

Participants

This prospective study received approval from our institutional review committee, and all participants provided written informed consent for their involvement. The study was conducted from September 2021 to March 2023, during which eligible participants were enrolled. The indications for performing MRI were based on clinical criteria [26,27,28] such as (i) diagnosis and preoperative assessment: suspicious mammography and/or ultrasound findings, palpable breast masses, or screening for multi-focal lesions; (ii) screening for populations with high-risk factors of breast cancer, including family history, dense breast tissue, breast abnormalities, or symptoms suggestive of breast biopsy. The inclusion criteria for this study were as follows: (i) suspicious findings on mammography and/or ultrasound [Breast Imaging Reporting and Data System [28] (BI-RADS) categories ≥ 4]; (ii) no history of breast surgery before MRI examination; (iii) no prior biopsies or previous treatment for breast cancer before MRI examination; and (iv) pathological confirmation of the breast lesions as benign or malignant. The exclusion criteria included: (i) incomplete pathological/medical information (n = 12); (ii) poor image quality (n = 3); and (iii) pathological confirmed malignancies other than invasive ductal carcinoma (n = 6). Finally, 140 participants with 151 lesions were included, with 11 subjects presenting bilateral lesions. The flowchart of patient selection is shown in Fig. 1.

Fig. 1
figure 1

Flow diagram of participant selection. n Represents the number of participants, and n* represents the number of lesions

Full size image

Ultrafast MRI acquisition protocol

The MRI scans were conducted utilizing a 3 Tesla MRI system (MAGNETOM Prisma, Siemens Healthineers, Erlangen, Germany) with a dedicated 16-channel bilateral breast coil. All participants were imaged in a head-first prone position. Breast compression was not applied. The MRI protocol included routine axial T1-weighted, T2-weighted, diffusion-weighted, and ultrafast DCE-MRI with a T1 map. The ultrafast DCE-MRI was performed with a research application using a compressed sensing (CS) accelerated T1-weighted gradient echo sequence with Dixon water-fat separation based on the volumetric interpolated breath-hold examination sequence (VIBE) with the following protocol parameters: repetition time (TR) = 4.46 msec, echo times (TE) = 1.55/2.73 msec, flip angle = 11°, field of view (FOV) = 360 × 292 mm2, voxel size = 0.9 × 0.9 × 2.5 mm3, bandwidth = 810 Hz/Px. One pre-contrast and 29 post-contrast phases of ultrafast DCE-MRI were acquired at a temporal resolution of 4.5 s/phase. To calculate the tissue concentration curve, a two-flip angle T1 map using the VIBE sequence was obtained prior to the DCE scans: TR/TE = 5.03/1.79 msec, flip angle = 2°/10°, FOV = 360 × 360 mm2, voxel size = 1.4 × 1.4 × 2 mm3, and bandwidth = 260 Hz/Px. The contrast agent used was Gadobutrol meglumine (Jia Di Xian®, Heng Rui), infused intravenously at a dose of 0.2 mL/kg (0.1 mmol/kg) and at a speed of 2.0 mL/s, followed by a 20-mL saline flush at the same rate.

Image analysis

The T1 maps and DCE data were uploaded to a post-processing workstation for semi-quantitative analysis using Tissue 4D software (Siemens Healthineers. Erlangen, Germany). Acknowledging the variable scan durations observed in prior studies, ranging from 60 s to 135 s [11, 12, 14,15,16,17,18,19,20,21,22,23,24,25], we tailored the scan duration ranging from 40.5 s (4.5 s × 9 phases) to 135 s (4.5 s × 30 phases). The imaging protocol was designed to consist of one pre-contrast CS-VIBE phase combined with different post-contrast phases (1–9, 1–12, 1–15, 1–18, 1–21, 1–27, and 1–30), forming a total of eight dynamic datasets. Each dataset corresponded to specific scan durations of 40.5 s (SD40.5s), 54 s (SD54s), 67.5 s (SD67.5s), 81 s (SD81s), 94.5 s (SD94.5s), 108 s (SD108s), 121.5 s (SD121.5s), and 135 s (SD135s) as shown in Fig. 2.

Fig. 2
figure 2

Flow chart of ultrafast DCE-MRI data processing. The image set that showed the pre-contrast CS-VIBE was marked as the first phase. The pre-contrast sequences and different phases of post-contrast sequences (1–9, 1–12, 1–15, 1–21, 1–24, 1–27, and 1–30) were used to form eight sets of dynamic series with scan duration ranging from 40.5 s t0 135.0 s. CS-VIBE T1-weighted compressed-sensing volume interpolated breath-hold examination

Full size image

To obtain the ultrafast DCE-MRI parameters for each lesion, two radiologists (W.W. and X.C. with seven and six years of experience in breast MRI, respectively) blinded to pathological reports independently outlined the tumor volumes of interest (VOIs). In case of discrepancies between the delineations made by the two radiologists, a consensus approach was adopted to reach a final agreement. The VOIs were drawn on the central slice of the tumor’s largest section and automatically extended to surrounding slices to form a spherical VOI [29]. The radiologists then refined these volumetric segmentations as needed, avoiding areas of liquefaction, necrosis, or cystic changes. To ensure an accurate delineation of lesion contours, for each dynamic set, the VOIs were drawn at the frame with the most obvious tumor enhancement. In cases where lesions did not exhibit early-phase enhancement, VOIs were positioned taking the 30th frame of the DCE scan as reference.

We focused on two key parameters from ultrafast DCE-MRI: MS and initial area under the curve in 60 s (iAUC). MS was defined as the slope of the concentration curve’s steepest segment. iAUC represented the initial area under the concentration curve after 60 s of contrast injection, except for SD40.5s and SD54s (hereafter referred to as iAUC40.5s and iAUC54s, respectively), in which the iAUC was calculated only till the last scan timepoint. Both parameters were independently recorded for each lesion by the radiologists. Considering the senior radiologist’s extensive experience and expertize in interpreting DCE-MRI images for breast lesions, the data from the senior radiologist were used for statistical analysis. Additionally, to assess intra-observer agreement, the senior radiologist re-evaluated 30 randomly selected participants after three months.

Clinicopathologic information

Data collected from the medical record were participant age (years), menopausal status, histological type, estrogen/progesterone receptor status, human epidermal growth factor receptor 2 (HER2) status, Ki67 status, lymph node metastasis, and lesion size (mm) [30, 31]. Lesions were categorized as benign or malignant based on pathology, with invasive ductal carcinoma classified as “malignant” and breast mastitis and other benignancies classified as “benign”.

Statistical analysis

Statistical analyses were conducted using the SPSS (version 26, IBM) and MedCalc software (version 20.022, MedCalc). The normality of ultrafast DCE-MRI parameters was tested using the Kolmogorov–Smirnov test. Differences in ultrafast DCE-MRI parameters between benign and malignant groups were compared using the Student’s t test if normally distributed and the Mann–Whitney U test if non-normally distributed. Paired t tests with Bonferroni correction were employed to discern significant differences in these parameters for benign and malignant breast lesions among eight scan durations, assuming a normal distribution. Nonparametric Cochran’s Q tests were utilized otherwise. Diagnostic performance was evaluated via receiver-operating characteristic (ROC) curve analysis, with the area under the ROC curve (AUC) calculated and compared using the DeLong test. Sensitivity, specificity, positive predictive value (PPV), NPV, and accuracy were also calculated. Additionally, the cutoff values were determined using the Youden index. The inter- and intra-observer agreements for the ultrafast DCE-MRI parameters were evaluated using intraclass correlation coefficients (ICCs). Statistical significance was considered at p < 0.05.

Results

Participant characteristics

In total, 151 pathologically proven breast lesions in 140 women (mean age, 47 ± 11 years; range, 19–77 years) were analyzed. Among these participants, 129 had a single lesion, and 11 had bilateral lesions. Table 2 summarizes the detailed clinicopathological information of the participants. The 151 lesions were pathologically diagnosed as 55 benignancy (36%) and 96 malignancy (64%). Of the 55 benign lesions, 39 (70%) were fibroadenomas, six (11%) were adenoses, two (4%) were hyperplasia, one (2%) were phyllodes tumors, two (4%) were intraductal papilloma, and five (9%) were mastitis. All the 96 malignant lesions (100%) were invasive ductal carcinomas (IDC). The mean sizes of benign and malignant tumors were 22.3 ± 11.7 mm and 31.3 ± 15.5 mm, respectively, with three lesions (two benign and one malignant) being smaller than 10 mm.

Table 2 Participant and tumor characteristics
Full size table

Differences in ultrafast DCE-MRI parameters between benign and malignant lesions

As shown in Table 3 and Fig. 3, ultrafast DCE-MRI parameters varied between benign and malignant lesions across different scan durations. Malignant lesions consistently exhibited higher MS and iAUC values than benign lesions. At SD40.5s, the mean MS in malignant lesions was significantly higher than in benign lesions (0.3 ± 0.3 vs 0.2 ± 0.3, p = 0.03). No differences were observed in iAUC between benign and malignant lesions (0.04 ± 0.04 vs 0.04 ± 0.04, p = 0.75). From SD54s onwards, both the MS and iAUC values of the malignant group were consistently and significantly larger than those of the benign group across all scan durations (all p < 0.05).

Table 3 Comparison of multiple ultrafast DCE-MRI parameters between benign and malignant breast lesions at eight scan durations
Full size table
Fig. 3
figure 3

Line plots of ultrafast DCE-MRI parameter values in relation to scan duration for all benign lesions (blue) and malignant lesions (red). MS maximum slope, iAUC initial area under the curve in 60 s

Full size image

Changes in ultrafast DCE-MRI parameters with different scan durations

Figures 3 and 4 illustrate the trends in ultrafast DCE-MRI parameter values for benign and malignant lesions over varying scan durations. For both benign and malignant lesions, MS values increased from SD40.5s to SD81s. In benign lesions, MS significantly differs between the initial durations (SD40.5s and SD54s) and subsequent ones, and between SD67.5s and SD108s (all corrected p < 0.05), except between SD40.5s and SD54s, and for all pairs from SD67.5s to SD135s (all corrected p > 0.05). Malignant lesions also exhibit significant MS differences between SD40.5s, SD54s, and all subsequent scan durations (all corrected p < 0.05), with no significant differences for all pairs from SD67.5s to SD135s (all corrected p > 0.05). Similar patterns are observed in mean iAUC values. In benign lesions, iAUC significantly differs between the initial durations (SD40.5s, SD54s, and SD67.5s) and subsequent ones (all corrected p < 0.05), except between SD67.5s and SD81s, and for all pairs from SD81s to SD135s (all corrected p > 0.05). Malignant lesions show significant iAUC differences between the initial durations (SD40.5s, SD54s, SD67.5s, and SD81s) and subsequent ones (all corrected p < 0.05), with no significant differences between SD81s and SD94.5s, and for all pairs from SD94.5s to SD135s (all corrected p > 0.05) (Table 4).

Fig. 4
figure 4

A Ultrafast breast DCE-MRI scans in a 49-year-old woman with pathologically confirmed benign fibroadenoma in the right breast. The first two rows (a–h) show color maps of the MS from SD40.5s to SD135s. The last two rows (i–p) show color maps of iAUC from SD40.5s to SD135s. B Ultrafast breast DCE-MRI scans in a 53-year-old woman with pathologically confirmed invasive ductal carcinoma in the left breast. The first two rows (a–h) show color maps of the MS from SD40.5s to SD135s. The last two rows (i–p) show color maps of the iAUC in 60 s from SD40.5s to SD135s. MS maximum slope, iAUC initial area under the curve in 60 s, SD scan duration

Full size image
Table 4 Paired comparison test results for the ultrafast DCE-MRI parameters in relation to scan duration
Full size table

Performance of the ultrafast DCE-MRI parameter among different scan duration

Table 5 and Fig. 5 depict the AUC values for MS and iAUC across various scan durations. There was a gradual increase in AUC values for both parameters with longer scan durations. The AUC for MS at SD40.5s was significantly lower than its AUC values from SD67.5s to SD135s (all corrected p < 0.05). Additionally, MS at SD54s exhibited a significantly lower AUC than its AUC at SD81s (corrected p = 0.03). There were no significant differences among AUC pairs for MS from SD67.5s to SD135s (all corrected p > 0.05). Similarly, the AUC of iAUC at SD40.5s was significantly lower than that from SD54s to SD135s (all corrected p < 0.05). No significant differences in AUC values were observed for iAUC from SD54s to SD135s (all corrected p > 0.05) (Tables S1 and S2).

Table 5 Diagnostic performance of multiple ultrafast DCE-MRI parameters with eight scan durations
Full size table
Fig. 5
figure 5

Graphs show the receiver operating characteristic (ROC) curve of ultrafast DCE-MRI parameters with different scan durations. MS40.5s = MS with a scan duration of 40.5 s, MS54s = MS with a scan duration of 54 s, MS67.5s = MS with a scan duration of 67.5 s, MS81s = MS with a scan duration of 81 s, MS94.5s = MS with a scan duration of 94.5 s, MS108s = MS with a scan duration of 108 s, MS121.5s = MS with a scan duration of 121.5 s, MS135s = MS with a scan duration of 135 s. iAUC definitions for different scan durations are the same as MS above. MS maximum slope, iAUC initial area under the curve in 60 s

Full size image

Inter- and intra-observer reliabilities for ultrafast DCE-MRI parameters

The ICCs for inter- and intra-observer reliability of the MS and iAUC measurements across eight scan durations were all > 0.70, indicating robust reproducibility of these measurements.

Discussion

This study was designed to assess the impact of scan duration on parameter estimation and diagnostic performance in breast ultrafast DCE-MRI, aiming to identify the shortest yet effective scan duration for distinguishing between benign and malignant breast lesions. The analysis involved 140 participants who underwent ultrafast DCE-MRI, and we scrutinized the ultrafast protocols acquired over a range of scan durations, from 40.5 s to 135 s. Our findings revealed that at a scan duration of 67.5 s, both MS and iAUC achieved impressive AUCs of 0.804 and 0.659, respectively, for differentiating malignant from benign breast lesions. Notably, starting from SD67.5s, both MS and iAUC demonstrated consistent discriminatory capabilities, with no significant difference compared to those obtained at longer scan durations, establishing SD67.5s as the optimal scan duration for this imaging technique.

In this study, significant differences were observed in MS and iAUC between benign and malignant breast lesions starting from a scan duration of 54 s. Malignant lesions exhibited higher MS and iAUC values, indicative of more rapid contrast enhancement compared to benign lesions. MS reflects the rate of contrast agent entry into tumor tissues [23], often associated with higher vascular density and vascular permeability related to neovascularization and inflammatory responses [11]. Similarly, iAUC characterizes the total amount of the contrast agent entering the tumor tissue within the initial 60 s [12] correlating with blood flow, perfusion, and angiogenesis. Elevated MS and iAUC values in malignant breast lesions may signify increased tumor growth and metabolic activity due to heightened demand for blood supply in the tumor tissue. Consistent with our findings, similar conclusions have been reported in previous studies, further supporting the utility of these parameters in distinguishing between benign and malignant breast lesions [11,12,13].

We observed an increase in both MS and iAUC values with the extension of scan duration, stabilizing around SD94.5s. We infer that as the contrast agent perfuses through tissues, the initial enhanced stages of the scan can capture variations in blood flow dynamics and vascular permeability [32]. Notably, stabilizing these parameters after SD94.5s may signify reaching a peak enhancement, indicating that sufficient contrast agents have permeated the tissues. The minor fluctuations in the later stages could be attributed to our study’s manual delineation of VOIs. Manual delineation introduces inherent variability in region selection, contributing to slight deviations in parameter values.

The diagnostic performance of MS in distinguishing benign and malignant breast lesions in our study aligns with previous findings [13, 17, 20]. Honda et al achieved an AUC of 0.76 using CS-VIBE sequences with a 60-s scan duration and a temporal resolution of 3.7 s/phase [20]. Mann et al [13] and Pelissier et al [17] used time-resolved angiography with stochastic trajectories (TWIST)-VIBE sequences with a temporal resolution of 4.32 s/phase and 7.1 sec/phase (scan duration of 78 s and 86.4 s, respectively), respectively, reporting AUCs of 0.94 and 0.829, respectively. In our study, employing CS-VIBE with a 4.5 s/phase temporal resolution, we obtained an AUC of 0.804 with a 67.5-s scan duration. Despite differences regarding parameter settings for ultrafast DCE-MRI protocol, our findings are consistent with prior studies.

Furthermore, our findings suggested that extending scan durations to 81 s and even 135 s did not significantly enhance MS’s diagnostic performance. This underscores the limited benefit of further prolonging scan durations beyond a certain threshold. Utilizing MS derived from the 67.5-s ultrafast DCE-MRI protocol, we identified that about 70.91% of unnecessary biopsies could potentially be avoided. This result demonstrates promise and provides valuable insights for clinical decision-making.

Additionally, it is noteworthy that the majority of malignancies recruited in this study were IDCs, with only a few cases of ductal carcinoma in situ (DCIS) being represented. Therefore, to ensure the homogeneity of the study sample, we focused our analysis solely on IDC. However, it is essential to acknowledge that previous ultrafast DCE-MRI studies in discriminating benign and malignant breast lesions have included a broad spectrum of histological types. Different pathological subtypes of breast cancer exhibit considerable variations in their enhancement characteristics [33, 34]. As such, further work on DCIS and other types of invasive breast cancer breast cancer is required to ensure this timepoint is generalizable to all breast cancer cases.

The ultrafast DCE-MRI parameters are subject to numerous factors, including scan duration, contrast agent characteristics, and technical settings [35,36,37]. Our study can only offer preliminary insights into optimizing breast ultrafast DCE-MRI protocols, particularly with CS-VIBE techniques and 4.5-s temporal resolution. The result may not be universally applicable. Despite these limitations, the study employed a continuous and frequent acquisition approach with high spatial resolution. This allowed us to determine the impact of scan duration on parameter estimation and diagnostic performance. Meanwhile, the importance of considering the intricacies of scan protocols and the interplay of various technical and biological factors in optimizing ultrafast DCE-MRI for breast lesion characterization must be underscored.

Several limitations in this study must be acknowledged. First and foremost, the study featured a relatively small sample size from a single center, with an uneven sample distribution, and the malignant lesions included only IDCs. A second limitation pertains to the absence of an analysis regarding the influence of different temporal resolutions on diagnostic performance. Understanding how varying temporal resolutions might impact the discrimination of benign and malignant breast lesions could offer valuable insights. Exploring this aspect in subsequent research would be of interest. Thirdly, due to the manual delineation of VOIs, there was an inherent margin of error in the placement and selection of regions for analysis. This can contribute to some degree of parameter value fluctuation. Future studies could focus on reducing this source of variability by implementing more advanced techniques such as automated VOI selection or employing advanced image processing algorithms.

In conclusion, ultrafast DCE-MRI with a scan duration of 67.5 s provides a promising non-invasive method for distinguishing between benign and malignant breast lesions, potentially reducing unnecessary biopsies. However, given the exploratory nature and limited sample size of this study, as well as the inclusion of only IDC as malignant breast lesions, this proposed protocol has yet to be implemented in clinical practice. Future studies involving multi-center, large-sample cohorts that include DCIS and other types of invasive breast cancer are warranted to validate our results.

Data availability

The dataset used or analyzed during the current study is available from the corresponding author upon reasonable request.

Abbreviations

AUC:

Area under the receiver operating characteristic curve

CS:

Compressed sensing

DCE-MRI:

Dynamic contrast-enhanced magnetic resonance imaging

DCIS:

Ductal carcinoma in situ

FOV:

Field of view

HER2:

Human epidermal growth factor receptor 2

iAUC:

Initial area under the curve in 60 s

IDC:

Invasive ductal carcinoma

MS:

Maximum slope

NPV:

Negative predictive value

PPV:

Positive predictive value

ROC:

Receiver operating characteristic curve

TE:

Echo times

TR:

Repetition time

VIBE:

Volume interpolated breath-hold examination

VOI:

Volume of interest

References

  1. Siegel RL, Miller KD, Wagle NS, Jemal A (2023) Cancer statistics, 2023. CA Cancer J Clin 73:17–48

    Article  PubMed  Google Scholar 

  2. Mann RM, Hooley R, Barr RG, Moy L (2020) Novel approaches to screening for breast cancer. Radiology 297:266–285

    Article  PubMed  Google Scholar 

  3. Kaiser CG, Reich C, Dietzel M et al (2015) DCE-MRI of the breast in a stand-alone setting outside a complementary strategy—results of the TK-study. Eur Radiol 25:1793–1800

    Article  PubMed  Google Scholar 

  4. Spick C, Szolar DHM, Preidler KW, Tillich M, Reittner P, Baltzer PA (2015) Breast MRI used as a problem-solving tool reliably excludes malignancy. Eur J Radiol 84:61–64

    Article  PubMed  Google Scholar 

  5. Bennani-Baiti B, Bennani-Baiti N, Baltzer PA (2016) Diagnostic performance of breast magnetic resonance imaging in non-calcified equivocal breast findings: results from a systematic review and meta-analysis. PLoS One 11:e0160346

    Article  PubMed  PubMed Central  Google Scholar 

  6. Magny SJ, Shikhman R, Keppke AL (2023) Breast imaging reporting and data system. In: StatPearls [Internet], 5th edn. StatPearls Publishing, Treasure Island (FL)

  7. Mann RM, Kuhl CK, Kinkel K, Boetes C (2008) Breast MRI: guidelines from the European Society of Breast Imaging. Eur Radiol 18:1307–1318

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Berg WA, Blume JD, Cormack JB et al (2008) Combined screening with ultrasound and mammography vs mammography alone in women at elevated risk of breast cancer. JAMA 299:2151–2163

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Vandermeer FQ, Bluemke DA (2007) Breast MRI: state of the art. Cancer Invest 25:384–392

    Article  PubMed  Google Scholar 

  10. Kataoka M, Iima M, Miyake KK, Matsumoto Y (2022) Multiparametric imaging of breast cancer: an update of current applications. Diagn Interv Imaging 103:574–583

    Article  PubMed  Google Scholar 

  11. Goto M, Sakai K, Yokota H et al (2019) Diagnostic performance of initial enhancement analysis using ultra-fast dynamic contrast-enhanced MRI for breast lesions. Eur Radiol 29:1164–1174

    Article  PubMed  Google Scholar 

  12. Onishi N, Sadinski M, Gibbs P et al (2020) Differentiation between subcentimeter carcinomas and benign lesions using kinetic parameters derived from ultrafast dynamic contrast-enhanced breast MRI. Eur Radiol 30:756–766

    Article  PubMed  Google Scholar 

  13. Mann RM, Mus RD, van Zelst J, Geppert C, Karssemeijer N, Platel B (2014) A novel approach to contrast-enhanced breast magnetic resonance imaging for screening: high-resolution ultrafast dynamic imaging. Invest Radiol 49:579–585

    Article  PubMed  Google Scholar 

  14. Ramli Hamid MT, Ab Mumin N, Wong YV, Chan WY, Rozalli FI, Rahmat K (2023) The effectiveness of an ultrafast breast MRI protocol in the differentiation of benign and malignant breast lesions. Clin Radiol 78:444–450

    Article  CAS  PubMed  Google Scholar 

  15. Peter SC, Wenkel E, Weiland E et al (2020) Combination of an ultrafast TWIST-VIBE Dixon sequence protocol and diffusion-weighted imaging into an accurate easily applicable classification tool for masses in breast MRI. Eur Radiol 30:2761–2772

    Article  PubMed  Google Scholar 

  16. Mus RD, Borelli C, Bult P et al (2017) Time to enhancement derived from ultrafast breast MRI as a novel parameter to discriminate benign from malignant breast lesions. Eur J Radiol 89:90–96

    Article  PubMed  Google Scholar 

  17. Pelissier M, Ambarki K, Salleron J, Henrot P (2021) Maximum slope using ultrafast breast DCE-MRI at 1.5 Tesla: a potential tool for predicting breast lesion aggressiveness. Eur Radiol 31:9556–9566

    Article  PubMed  Google Scholar 

  18. Cao Y, Wang X, Shi J et al (2023) Multiple parameters from ultrafast dynamic contrast-enhanced magnetic resonance imaging to discriminate between benign and malignant breast lesions: comparison with apparent diffusion coefficient. Diagn Interv Imaging 104:275–283

    Article  PubMed  Google Scholar 

  19. Lyu Y, Chen Y, Meng L et al (2023) Combination of ultrafast dynamic contrast-enhanced MRI-based radiomics and artificial neural network in assessing BI-RADS 4 breast lesions: potential to avoid unnecessary biopsies. Front Oncol 13:1074060

    Article  PubMed  PubMed Central  Google Scholar 

  20. Honda M, Kataoka M, Onishi N et al (2020) New parameters of ultrafast dynamic contrast-enhanced breast MRI using compressed sensing. J Magn Reson Imaging 51:164–174

    Article  PubMed  Google Scholar 

  21. Ohashi A, Kataoka M, Iima M et al (2020) A multiparametric approach to diagnosing breast lesions using diffusion-weighted imaging and ultrafast dynamic contrast-enhanced MRI. Magn Reson Imaging 71:154–160

    Article  PubMed  Google Scholar 

  22. Wu C, Pineda F, Hormuth 2nd DA, Karczmar GS, Yankeelov TE (2019) Quantitative analysis of vascular properties derived from ultrafast DCE-MRI to discriminate malignant and benign breast tumors. Magn Reson Med 81:2147–2160

    Article  PubMed  Google Scholar 

  23. Ohashi A, Kataoka M, Kanao S et al (2019) Diagnostic performance of maximum slope: a kinetic parameter obtained from ultrafast dynamic contrast-enhanced magnetic resonance imaging of the breast using k-space weighted image contrast (KWIC). Eur J Radiol 118:285–292

    Article  PubMed  Google Scholar 

  24. Lee SJ, Ko KH, Jung HK, Koh JE, Park AY (2020) The additional utility of ultrafast MRI on conventional DCE-MRI in evaluating preoperative MRI of breast cancer patients. Eur J Radiol 124:108841

    Article  PubMed  Google Scholar 

  25. Onishi N, Kataoka M, Kanao S et al (2018) Ultrafast dynamic contrast-enhanced mri of the breast using compressed sensing: breast cancer diagnosis based on separate visualization of breast arteries and veins. J Magn Reson Imaging 47:97–104

    Article  PubMed  Google Scholar 

  26. Gradishar WJ, Moran MS, Abraham J et al (2022) Breast cancer, version 3.2022, NCCN clinical practice guidelines in oncology. J Natl Compr Canc Netw 20:691–722

    Article  PubMed  Google Scholar 

  27. Mann RM, Cho N, Moy L (2019) Breast MRI: state of the art. Radiology 292:520–536

    Article  PubMed  Google Scholar 

  28. Mainiero MB, Moy L, Baron P et al (2017) ACR Appropriateness Criteria® Breast Cancer Screening. J Am Coll Radiol 14:S383–s390

    Article  PubMed  Google Scholar 

  29. Cao Y, Wang X, Li L et al (2023) Early prediction of pathologic complete response of breast cancer after neoadjuvant chemotherapy using longitudinal ultrafast dynamic contrast-enhanced MRI. Diagn Interv Imaging 104:605–614

    Article  PubMed  Google Scholar 

  30. Fujii T, Kogawa T, Dong W et al (2017) Revisiting the definition of estrogen receptor positivity in HER2-negative primary breast cancer. Ann Oncol 28:2420–2428

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Cho U, Kim HE, Oh WJ, Yeo MK, Song BJ, Lee A (2016) The long-term prognostic performance of Ki-67 in primary operable breast cancer and evaluation of its optimal cutoff value. Appl Immunohistochem Mol Morphol 24:159–166

    Article  CAS  PubMed  Google Scholar 

  32. Kataoka M, Honda M, Ohashi A et al (2022) Ultrafast dynamic contrast-enhanced MRI of the breast: How is it used? Magn Reson Med Sci 21:83–94

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Grubstein A, Rapson Y, Manor M et al (2022) MRI background parenchymal enhancement in patients with invasive lobular carcinoma: endocrine hormonal treatment effect. Breast Dis 41:317–323

    Article  PubMed  Google Scholar 

  34. Greenwood HI, Heller SL, Kim S, Sigmund EE, Shaylor SD, Moy L (2013) Ductal carcinoma in situ of the breasts: review of MR imaging features. Radiographics 33:1569–1588

    Article  PubMed  Google Scholar 

  35. Clauser P, Helbich TH, Kapetas P et al (2019) Breast lesion detection and characterization with contrast-enhanced magnetic resonance imaging: prospective randomized intraindividual comparison of gadoterate meglumine (0.15 mmol/kg) and gadobenate dimeglumine (0.075 mmol/kg) at 3T. J Magn Reson Imaging 49:1157–1165

    Article  PubMed  Google Scholar 

  36. Sagawa H, Kataoka M, Kanao S et al (2019) Impact of the number of iterations in compressed sensing reconstruction on ultrafast dynamic contrast-enhanced breast MR imaging. Magn Reson Med Sci 18:200–207

    Article  PubMed  Google Scholar 

  37. Hao W, Zhao B, Wang G, Wang C, Liu H (2015) Influence of scan duration on the estimation of pharmacokinetic parameters for breast lesions: a study based on CAIPIRINHA-Dixon-TWIST-VIBE technique. Eur Radiol 25:1162–1171

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

We thank the participants and referring technicians for their selfless and valuable assistance in this study.

Funding

This study has received funding from the Fundamental Research Funds for the Central Universities (2023CDJYGRH-YB04), the Graduate Research and Innovation Foundation of Chongqing (CYS23129 and CYB23070), and the Natural Science Foundation of Chongqing municipality (CSTB2023NSCQ-MSX0787).

Author information

Author notes

  1. Jiuquan Zhang and Xiaoxia Wang contributed equally to the study and are considered as co-corresponding authors.

Authors and Affiliations

  1. School of Medicine, Chongqing University, Chongqing, China

    Ying Cao & Yao Huang

  2. Department of Radiology, Chongqing University Cancer Hospital, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), Chongqing, China

    Ying Cao, Yao Huang, Wei Wang, Huifang Chen, Changchun Li, Junhua Shao, Shi Zhang, Xiaoxia Wang & Jiuquan Zhang

  3. School of Medical Imaging, North Sichuan Medical University, Nanchong, China

    Xianglong Chen

  4. MR Collaborations, Siemens Healthineers Ltd., Chengdu, China

    Ting Yin

  5. MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany

    Dominik Nickel

Authors
  1. Ying Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yao Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xianglong Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Huifang Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ting Yin
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Dominik Nickel
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Changchun Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Junhua Shao
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Shi Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Xiaoxia Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Jiuquan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

YC and XW collected and organized materials and the literature. XC and WW extracted and interpreted the patient data. YC, YH, and HC analyzed the results. YC wrote the manuscript. YC, XW, CL, JS, SZ, and JZ revised the manuscript. TY and DN provided technical support and verified all the data in the study. JZ had final responsibility for the decision to submit for publication. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Xiaoxia Wang or Jiuquan Zhang.

Ethics declarations

Ethics approval and consent to participate

This prospective study was approved by the Institutional Review Board of Chongqing University Cancer Hospital, and written informed consent was obtained from all participants.

Competing interests

TY is an employee of Siemens Healthineers and DN is an employee of Siemens Healthcare GmbH. The remaining authors declare that they have no competing interests.

Additional information

Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cao, Y., Huang, Y., Chen, X. et al. Optimizing ultrafast dynamic contrast-enhanced MRI scan duration in the differentiation of benign and malignant breast lesions. Insights Imaging 15, 112 (2024). https://doi.org/10.1186/s13244-024-01697-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s13244-024-01697-6

Keywords