Clinical applicability and relevance of fibroglandular tissue segmentation on routine T1 weighted breast MRI

Proton density water fraction as a reproducible MR-based measurement of breast density

PURPOSE

To introduce proton density water fraction (PDWF) as a confounder-corrected (CC) MR-based biomarker of mammographic breast density, a known risk factor for breast cancer.

METHODS

Chemical shift encoded (CSE) MR images were acquired using a low flip angle to provide proton density contrast from multiple echo times. Fat and water images, corrected for known biases, were produced by a six-echo CC CSE-MRI algorithm. Fibroglandular tissue (FGT) volume was calculated from whole-breast segmented PDWF maps at 1.5T and 3T. The method was evaluated in (1) a physical fat-water phantom and (2) normal volunteers. Results from two- and three-echo CSE-MRI methods were included for comparison.

RESULTS

Six-echo CC-CSE-MRI produced unbiased estimates of the total water volume in the phantom (mean bias 3.3%) and was reproducible across protocol changes (repeatability coefficient [RC] = 14.8 cm³ and 13.97 cm³ at 1.5T and 3.0T, respectively) and field strengths (RC = 51.7 cm³ ) in volunteers, while the two- and three-echo CSE-MRI approaches produced biased results in phantoms (mean bias 30.7% and 10.4%) that was less reproducible across field strengths in volunteers (RC = 82.3 cm³ and 126.3 cm³ ). Significant differences in measured FGT volume were found between the six-echo CC-CSE-MRI and the two- and three-echo CSE-MRI approaches (p = 0.002 and p = 0.001, respectively).

CONCLUSION

The use of six-echo CC-CSE-MRI to create unbiased PDWF maps that reproducibly quantify FGT in the breast is demonstrated. Further studies are needed to correlate this quantitative MR biomarker for breast density with mammography and overall risk for breast cancer.

Fully automatic quantification of fibroglandular tissue and background parenchymal enhancement with accurate implementation for axial and sagittal breast MRI protocols

PURPOSE

To propose and evaluate a fully automated technique for quantification of fibroglandular tissue (FGT) and background parenchymal enhancement (BPE) in breast MRI.

METHODS

We propose a fully automated method, where after preprocessing, FGT is segmented in T1-weighted, nonfat-saturated MRI. Incorporating an anatomy-driven prior probability for FGT and robust texture descriptors against intensity variations, our method effectively addresses major image processing challenges, including wide variations in breast anatomy and FGT appearance among individuals. Our framework then propagates this segmentation to dynamic contrast-enhanced (DCE)-MRI to quantify BPE within the segmented FGT regions. Axial and sagittal image data from 40 cancer-unaffected women were used to evaluate our proposed method vs a manually annotated reference standard.

RESULTS

High spatial correspondence was observed between the automatic and manual FGT segmentation (mean Dice similarity coefficient 81.14%). The FGT and BPE quantifications (denoted FGT% and BPE%) indicated high correlation (Pearson's r = 0.99 for both) between automatic and manual segmentations. Furthermore, the differences between the FGT% and BPE% quantified using automatic and manual segmentations were low (mean differences: -0.66 ± 2.91% for FGT% and -0.17 ± 1.03% for BPE%). When correlated with qualitative clinical BI-RADS ratings, the correlation coefficient for FGT% was still high (Spearman's ρ = 0.92), whereas that for BPE was lower (ρ = 0.65). Our proposed approach also performed significantly better than a previously validated method for sagittal breast MRI.

CONCLUSIONS

Our method demonstrated accurate fully automated quantification of FGT and BPE in both sagittal and axial breast MRI. Our results also suggested the complexity of BPE assessment, demonstrating relatively low correlation between segmentation and clinical rating.

Comparison between qualitative and quantitative assessment of background parenchymal enhancement on breast MRI

BACKGROUND

Potential clinical implications of the level of background parenchymal enhancement (BPE) on breast MRI are increasing. Currently, BPE is typically evaluated subjectively. Tests of concordance between subjective BPE assessment and computer-assisted quantified BPE have not been reported.

PURPOSE OR HYPOTHESIS

To compare subjective radiologist assessment of BPE with objective quantified parenchymal enhancement (QPE).

STUDY TYPE

Cross-sectional observational study.

POPULATION

Between 7/24/2015 and 11/27/2015, 104 sequential patients (ages 23 - 81 years, mean 49 years) without breast cancer underwent breast MRI and were included in this study.

FIELD STRENGTH/SEQUENCE

3T; fat suppressed axial T2, axial T1, and axial fat suppressed T1 before and after intravenous contrast.

ASSESSMENT

Four breast imagers graded BPE at 90 and 180 s after contrast injection on a 4-point scale (a-d). Fibroglandular tissue masks were generated using a phantom-validated segmentation algorithm, and were co-registered to pre- and postcontrast fat suppressed images to define the region of interest. QPE was calculated.

STATISTICAL TESTS

Receiver operating characteristic (ROC) analyses and kappa coefficients (k) were used to compare subjective BPE with QPE.

RESULTS

ROC analyses indicated that subjective BPE at 90 s was best predicted by quantified QPE ≤20.2 = a, 20.3-25.2 = b, 25.3-50.0 = c, >50.0 = d, and at 180 s by quantified QPE ≤ 32.2 = a, 32.3-38.3 = b, 38.4-74.5 = c, >74.5 = d. Agreement between subjective BPE and QPE was slight to fair at 90 s (k = 0.20-0.36) and 180 s (k = 0.19-0.28). At higher levels of QPE, agreement between subjective BPE and QPE significantly decreased for all four radiologists at 90 s (P ≤ 0.004) and for three of four radiologists at 180 s (P ≤ 0.004).

DATA CONCLUSION

Radiologists were less consistent with QPE as QPE increased.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2018;47:1685-1691.