Possible Breast Cancer Risk Related to Background Parenchymal Enhancement at Breast MRI: A Meta-Analysis Study

Evaluation of Association Between Menstrual Cycle Timing and Quantitative Background Parenchymal Enhancement on Breast MRI in Premenopausal Women

OBJECTIVES

To evaluate the influence of menstrual cycle timing on quantitative background parenchymal enhancement and to assess an optimal timing of breast MRI in premenopausal women.

METHODS

A total of 197 premenopausal women were enrolled, 120 of which were in the malignant group and 77 in the benign group. Two radiologists depicted the regions of interest (ROI) of the three consecutive biggest slices of glandular tissue in the unaffected side and calculated the ratio (=[SIpost - SIpre]/SIpre) in ROI from the precontrast and early phase to assess BPE quantitatively. Association of BPE with menstrual cycle timing was compared in three categories. The relationships between BPE and age /body mass index (BMI) were also explored.

RESULTS

We found that the BPE ratio presented lower in patients with the follicular phase (day1-14) compared to the luteal phase (day15-30) in the benign group (P = .036). Also, the BPE ratio presented significantly lower in the proliferative phase (day5-14) than the menstrual phase (day1-4) and the secretory phase(day15-30) in the benign group (P = .006). While the BPE ratio was not significantly different among the respective weeks (1-4) of the menstrual cycle in the benign group (P > .05). In the malignant group, the BPE ratio did not significantly differ between/among any menstrual cycle phase or week (all P > .05).

CONCLUSION

It seems more suitable for Asian women whose lesions need to follow up or are suspected of malignant to undergo breast MRI within the 1st to 14th day of the menstrual cycle, especially on the 5th to 14th day.

Breast MRI Background Parenchymal Enhancement Categorization Using Deep Learning: Outperforming the Radiologist

BACKGROUND

Background parenchymal enhancement (BPE) is assessed on breast MRI reports as mandated by the Breast Imaging Reporting and Data System (BI-RADS) but is prone to inter and intrareader variation. Semiautomated and fully automated BPE assessment tools have been developed but none has surpassed radiologist BPE designations.

PURPOSE

To develop a deep learning model for automated BPE classification and to compare its performance with current standard-of-care radiology report BPE designations.

STUDY TYPE

Retrospective.

POPULATION

Consecutive high-risk patients (i.e. >20% lifetime risk of breast cancer) who underwent contrast-enhanced screening breast MRI from October 2013 to January 2019. The study included 5224 breast MRIs, divided into 3998 training, 444 validation, and 782 testing exams. On radiology reports, 1286 exams were categorized as high BPE (i.e., marked or moderate) and 3938 as low BPE (i.e., mild or minimal).

FIELD STRENGTH/SEQUENCE

A 1.5 T or 3 T system; one precontrast and three postcontrast phases of fat-saturated T1-weighted dynamic contrast-enhanced imaging.

ASSESSMENT

Breast MRIs were used to develop two deep learning models (Slab artificial intelligence (AI); maximum intensity projection [MIP] AI) for BPE categorization using radiology report BPE labels. Models were tested on a heldout test sets using radiology report BPE and three-reader averaged consensus as the reference standards.

STATISTICAL TESTS

Model performance was assessed using receiver operating characteristic curve analysis. Associations between high BPE and BI-RADS assessments were evaluated using McNemar's chi-square test (α* = 0.025).

RESULTS

The Slab AI model significantly outperformed the MIP AI model across the full test set (area under the curve of 0.84 vs. 0.79) using the radiology report reference standard. Using three-reader consensus BPE labels reference standard, our AI model significantly outperformed radiology report BPE labels. Finally, the AI model was significantly more likely than the radiologist to assign "high BPE" to suspicious breast MRIs and significantly less likely than the radiologist to assign "high BPE" to negative breast MRIs.

DATA CONCLUSION

Fully automated BPE assessments for breast MRIs could be more accurate than BPE assessments from radiology reports.

LEVEL OF EVIDENCE

4 TECHNICAL EFFICACY STAGE: 3.

Background Parenchymal Enhancement on Breast MRI: Assessment and Clinical Implications

Purpose of Review

To present recent literature regarding the assessment and clinical implications of background parenchymal enhancement on breast MRI.

Recent Findings

The qualitative assessment of BPE remains variable within the literature, as well as in clinical practice. Several different quantitative approaches have been investigated in recent years, most commonly region of interest-based and segmentation-based assessments. However, quantitative assessment has not become standard in clinical practice to date. Numerous studies have demonstrated a clear association between higher BPE and future breast cancer risk. While higher BPE does not appear to significantly impact cancer detection, it may result in a higher abnormal interpretation rate. BPE is also likely a marker of pathologic complete response after neoadjuvant chemotherapy, with decreases in BPE during and after neoadjuvant chemotherapy correlated with pCR. In contrast, pre-treatment BPE does not appear to be predictive of pCR. The association between BPE and prognosis is less clear, with heterogeneous results in the literature.

Summary

Assessment of BPE continues to evolve, with heterogeneity in approaches to both qualitative and quantitative assessment. The level of BPE has important clinical implications, with associations with future breast cancer risk and treatment response. BPE may also be an imaging marker of prognosis, but future research is needed on this topic.