Quantitative Volumetric K-Means Cluster Segmentation of Fibroglandular Tissue and Skin in Breast MRI

Introduction

The amount of fibroglandular tissue (FGT) including epithelial and stromal elements is an independent marker of breast cancer risk in conjunction with age, body mass index, and genetic predisposition [1, 2]. Traditionally, the amount of FGT has been evaluated visually from mammograms in which mammographic breast density (MBD) describes the proportion of FGT to fatty tissue. The Breast Imaging Reporting and Data System (BI-RADS®) 4th edition subdivided mammographic breast density (BD) into four quartiles [3]. However, the agreement between radiologists is only moderate and is especially challenging in the middle two density categories [4]. The new BI-RADS® 5th edition still categorizes MBD into four subgroups, i.e., almost entirely fatty, scattered areas of fibroglandular density, heterogeneously dense, or extremely dense [5]. Nevertheless, it is acknowledged that there is a clear need for further research into volume-based, reproducible percentage cutoff points.

Quantitative assessment of the percentage area of FGT in mammograms has been shown to associate more strongly to the individual’s breast cancer risk than can be achieved by its visual assessment [6, 7]. Automated quantitative MBD measurement methods have recently been developed [8] and two clinically applied software programs [9, 10] are now approved by the US Food and Drug Administration. The two-dimensional methods may suffer from tissue overlap [11]. Tomosynthesis, in which multiple low-dose images are used to reconstruct a 3D model, has been postulated to define more accurately tissue structures [12]. In addition to the breast cancer risk, the MBD may be a prognostic factor in patients with newly diagnosed breast cancer, i.e., one study suggested that patients with breast densities lower than 10% MBD have higher mortality rates than patients with denser breasts [13].

Magnetic resonance imaging (MRI) of the breast is being increasingly exploited in clinical practice with a wide range of clinical and screening indications [14]. Therefore, an accurate assessment of FGT as an adjunct to clinical breast MRI would be clinically valuable. The excellent soft tissue contrast in MRI provides clear distinguishing between fibroglandular and fatty tissues and allows a three-dimensional characterization of FGT volume without tissue compression or radiation exposure [15]. As a three-dimensional imaging modality, MRI is not limited by tissue overlap and could thus help to estimate breast cancer risk in high-risk populations [16]. The revised fifth edition of the BI-RADS® includes a recommendation to include a visual estimation of FGT with breast MRI. The categories for assessing the amount of FGT of breasts from MRI parallel those applied in MBD, i.e., almost entirely fat, scattered FGT, heterogeneous FGT, and extreme FGT. However, subjective visual estimation of FGT with MRI has revealed only moderate intra-/inter-rater agreement [17, 18].

Automated observer-independent quantitative measurements of FGT are therefore needed also in MRI if we are to achieve a more standardized risk evaluation. Although quantitative measurements are currently under extensive research, there is however no consensus on the optimal method to quantify FGT in MRI, with several automatic and semi-automatic algorithms now available [19–26]. Additionally, no consensus exists on the optimal sequence from which FGT volumes are to be determined. Dixon sequences were reported to show the highest correlation and reproducibility, yet T1 sequences reported comparable accuracies [27]. Nevertheless, observed proportions of FGT in MRI have often been inconsistent when compared to those of MBD and the reproducibility has varied, depending on which parameters are being determined [28–30]. The aim of the present study was to develop a clinically feasible and highly reproducible method to quantify FGT with MRI and to correlate these values to automatically acquired measurements from digital mammography and tomosynthesis.

Materials and Methods

Results

A total of 72 women (mean age 52.4 ± 12.3) were included in this study. All MRI examinations (n = 72) were performed within 4 months of the mammography or the tomosynthesis assessment. Twenty-nine patients (39 breasts) underwent tomosynthesis and 43 patients (66 breasts) underwent digital mammography. Unilateral breast malignancies were diagnosed in 39 patients.

Inter-rater agreement was tested using 2-mm-thick slices and proved to be excellent for MRI-based BVs (ICC 0.985, 95% CI 0.934–0.994), FGT volumes (ICC 0.979, 95% CI 0.958–0.989), and FGT-% values (ICC 0.994, 95% CI 0.990–0.997). Processing time varied from 6 to 13 min depending on slice thickness and breast size.

Intertechnique reproducibility is shown in Tables 1, 2, 3, and 4 and in Fig. 3. Moderate to excellent correlations (r = 0.659–0.967, P < 0.001) were achieved between MRI-based measurements and digital mammography for all parameters. When the MRI-based measurements were compared to tomosynthesis, the BVs showed excellent correlations (r = 0.866–0.959, P < 0.001). Regarding the FGT-%s and volumes, correlations were moderate to high (r = 0.528–0.778, P < 0.001). Comparison of earlier and present study is presented in Table 5.

We evaluated our method using three different slice thicknesses: 0.7 mm (257 slices), 2 mm (90 slices), and 4 mm (45 slices). Measures correlated strongly with each other; r = 0.962–0.994 for BVs, r = 0.916–0.932 for FGT volumes, and r = 0.942–0.968 for FGT-%. The possibility of loss of fine structures of breasts by examining thicker slices proved to have no impact or only a minor effect on the measurements.

For MRI, the skin volumes were separately measured and the ratios between skin volumes and total breast volumes were calculated. The percentage of skin volume ranged between 2.74 and 35.2% with a mean value of 9.3%.

Discussion

The key finding of the present study was that our MRI-based method to assess FGT volumes and proportions achieved high interobserver reproducibility and convincing correlations to mammographic breast density measurements when using 4-mm-thick MRI slices. Our method also represents a novel way to measure skin as an independent tissue type, which may lead to a more accurate assessment of breast composition and this, in our opinion, reflects a more reliable volumetric measurement. To the best of our knowledge, no results concerning volumetric skin segmentation using active contour segmentation have been published.

At present, there is no consensus on the best technique to perform a quantitative breast tissue analysis with MRI. Compared to automatic programs, manual segmentation is more time-consuming, requires training, and is subject to observer-biased interpretation. The clinical feasibility of quantitative semi-automatic MRI techniques depends on the accuracy and robustness of the breast segmentation. Accordingly, a wide variety of MRI segmentation techniques exist and several methods have been proposed as being best for the assessment of breast volume and FGT volume. Klifa et al. and Wang et al. have evaluated a fuzzy c-means (FCM)-based method [15, 30], and Kang et al. have used a K-means clustering method [21] while Nie et al. proposed a two-step method, firstly locating the skin border and lungs by a FCM algorithm and subsequently applying an adaptive FCM algorithm to extract the FGT [20]. A deep-learning method has also been developed for breast and FGT segmenting from MRI in order to outperform existing methods relying on atlases, template matching, or edge and surface detection [39]. The thickness of MR image slices varies between studies and could be a factor contributing to different results. In our study, high agreement of FGT-%, BV, and FGT between slice thicknesses was noted. Due to faster active contour segmenting, thicker slices may be advocated over thin slice thicknesses.

Even though our study had a small sample size, statistically significant results regarding inter-rater agreement were achieved. Our method proved to have a high inter-rater correlation between all measurements (ICC 0.979–0.997). Van der Waal et al. compared visual agreement of BI-RADS assessment between readers and agreement between automated MBD software. Agreement of visual evaluation of BI-RADS categories from mammograms (kappa score 0.80–0.84) has slightly lower reliability than that of our method. Differences between commercial automatic MBD assessing software Quantra and Volpara are even greater with ICC of 0.64 (95% CI − 0.07–0.88) and 0.55 (95% CI 0.24–0.72) when comparing estimates of percent dense volume and absolute dense volume, respectively [40].

Previously, the various MRI segmentation methods have been compared to the performance of mammography and tomosynthesis in the assessment of quantitative breast factors (Table 5). Although different procedures (e.g., segmenting) are mostly done using computer algorithms, some operator inputs are still required, such as delineating breast from the image or selection of seed points for the region growing tool. However, even though there has been extensive research conducted on the intertechnique reproducibility between MRI, mammography, and tomosynthesis, the inter-rater agreements have rarely been reported (Table 5). In one publication, 11 cases were evaluated with an FCM-based segmentation algorithm and the average standard deviation of MRI-FGT-% between observers was reported to be 3.9% [20]. In another study, the breasts were imaged and segmented automatically before and after the administration of intravenous contrast medium. The correlation between these pre- and post-contrast breast density quantifications ranged between r = 0.98 and 0.99 [18]. Our results demonstrate that the MRI-based breast volumes, FGT volumes, and FGT-% values are highly reproducible. Even though the suggested semi-automatic method requires user input, its influence on the results is minimal.

We observed an excellent correlation between MRI and full-field digital mammography in agreement with previously published data (Table 5). In the earlier studies comparing MRI-based and mammographic measurements, correlations between FGT-% and MBD were in the range 0.71–0.94, between breast volumes 0.94–0.99 and between FGT volumes, they varied between 0.61–0.91 (Table 5). However, we noted that our correlations between tomosynthesis and MRI were slightly lower than those of between mammography and MRI. This contrasts with the studies of Tagliafico et al. and Pertuz et al. who achieved better correlations between tomosynthesis and MRI than between digital mammography and MRI [28, 41]. The difference between MBD from standard dose and synthetic mammograms was evaluated by Conant et al., and an average of 1.7% higher breast density was noted for synthetic mammograms over normal dose mammograms [42].

Differences in qualitative breast factors can be in part caused by the skin. The skin and FGT have similar signal intensities in MRI which complicates reliable FGT-% measurements. Only few studies have reported results of skin segmentation. Nie et al. studied the effect of skin removal on breast density measurement and suggested that the percentage of the skin volume ranged between 5.0 and 15.2% [38]. In our study, a range of 2.7–35.2% (mean 9.3%) was found. The method applied here could measure the skin separately by using active contour segmentation without any significant skin volume leakage into the FGT volume through the nipple area. In many other studies, the skin has been directly segmented as a part of “breast” or adipose tissue, but not separately segmented [22, 23, 41]. Mammographic density measurement software compensates routinely for the penetration through the skin in order to eliminate the impact that the skin has on the estimate of fibroglandular tissue volume and therefore we believe that future developments in volumetric measurements should always include skin segmentation protocol. We obtained excellent reproducibility and significant correlations with digital mammography and thus our results emphasize the importance of excluding skin reliably from FGT. Differences between the mammographic and MRI measurements may be due to difficulties in delineating the breast from subcutaneous fat tissue in the chest wall at MRI or in positioning of the breasts in mammograms [11].

The difference between the mammographic, tomosynthesis, and MRI-based FGT measurements may also be related to differences in breast content especially in mammographically dense breasts, where the projected results of mammography are more dependent on parenchymal patterns than on volume and therefore do not reflect the composition of tissue as well as MRI [16, 19]. Also, mammography is a two-dimensional imaging modality, and therefore suffers from tissue-overlapping and cannot accurately differentiate between overlapping fatty and fibroglandular tissues. The position of the patient and the degree of compression may increase the inaccuracies [43]. Klifa et al. (2010) speculated that breasts with very little FGT (i.e., fatty breasts) display a greater tendency to under- or over-estimate real fibroglandular regions due to the difficulty of segmenting very thin fibroglandular regions within the adipose tissue [16]. In our study population, the majority of patients had low or very low density breasts which may have exerted some influence on our correlations.

Our study has several limitations. First, our patient population is relatively small. Being a tertiary referral hospital, most of the patients were referred with the complete mammographic evaluation already done at other institutions. Our study population consists chiefly of patients who had to undergo repeated examinations according to the recommendation of specialized breast radiologists to re-evaluate additional mammographic findings. The patient sample consists of symptomatic patients with relatively non-dense breasts and is not representative of a normal screening population. Second, the lack of a golden standard as a reference for breast density and FGT volume analysis hinders the detection of systematic errors. According to our clinical practice, the patients underwent either digital mammography or tomosynthesis, but not both, in order to minimize their exposure to radiation. Nevertheless, our results achieved statistical significance, indicating that the hypothesis is applicable and tested method is feasible.

In conclusion, the MRI-based assessment of the proportion of fibroglandular tissue of the breast, including skin segmentation described in the present study, is highly reproducible and has potential to be utilized in assessment of FGT-% in clinical practice and scientific studies. In the future, a quantitative assessment of FGT-% to complement the visual estimation of FGT should be performed on a more regular basis since this technique reveals a component which can be included in the individual patient’s breast cancer risk stratification.

Compliance with Ethical Standards

The Institutional Ethics Board approved this retrospective study; the Chair of the Hospital District waived the need for written informed consent from the patients.