Background parenchymal enhancement and uptake as breast cancer imaging biomarkers: A state-of-the-art review

Performance of breast MRI for high-risk screening during lactation

OBJECTIVES

To assess the diagnostic performance of breast MRI during lactation in the setting of high-risk breast cancer screening.

MATERIALS AND METHODS

Screening breast MRIs performed between April 2008 and March 2024 were retrospectively reviewed. Background parenchymal enhancement (BPE) grade was compared between lactating patients and patients who recently stopped lactating using the Mann-Whitney test. Breast Imaging Reporting and Data System (BI-RADS) scores prevalence rates were compared between lactating patients and controls encompassing young non-lactating patients using the Chi-square test. Diagnostic performance was calculated for patients with a biopsy reference or a 1-year radiologic follow-up.

RESULTS

One-hundred forty-two screening breast MRIs were performed in lactating patients (n = 104, median age, 36.0 ± 6.0 years). Marked BPE appeared in 82% of cases (116/142), with a higher BPE grade in exams performed during lactation as compared with those performed in patients who had recently ceased lactating (p < 0.001). Screening MRIs performed during lactation had a higher rate of BI-RADS 3 scores (40/142, 28% vs. 683/8922, 7%, p < 0.001) and a lower rate of BI-RADS 1/2 scores (88/142, 62% vs. 7549/8922, 84.6%, p = 0.002) compared with those performed in controls (n = 8922). One pregnancy-associated breast cancer was detected, and one interval-cancer occurred. All MRI-guided biopsies were negative (n = 13). Screening breast MRI during lactation had 50% sensitivity (1/2), 60% specificity (72/120), 2.0% positive predictive value (1/49), and 98.6% negative predictive value (71/82).

CONCLUSION

The efficacy of breast MRI for high-risk screening during lactation is limited by prominent BPE, leading to an increased rate of BI-RADS 3 categorization and diminished overall specificity.

KEY POINTS

Question Studies on breast MRI during lactation were solely focused on studies conducted in patients with known cancer but not in the screening setting. Findings Screening breast MRI during lactation usually results in marked background parenchymal enhancement, negatively impacting its diagnostic performance. Clinical relevance Despite the lower performance, and amidst the significant risk of pregnancy-associated breast cancer, this screening approach remains relevant for lactating patients with high-risk profiles, such as BReast Cancer (BRCA) carriers. Radiologists should be familiar with the normal appearance of breast MRI during lactation.

Hormonal Regulation of Background Parenchymal Enhancement at Contrast-enhanced Mammography

Background Background parenchymal enhancement (BPE) is an important diagnostic and prognostic imaging biomarker. Although hormonal regulation of BPE at breast MRI has been investigated, information regarding hormonal regulation of BPE at contrast-enhanced mammography (CEM) remains scarce. Purpose To investigate how BPE at CEM changes across various short- and long-term physiologic and pharmacologic hormonal effects, including menopausal status, lactation, hormone replacement therapy (HRT), and tamoxifen therapy and its cessation. Materials and Methods This retrospective study included CEM examinations performed between December 2012 and January 2024. A computational search was performed to identify CEMs performed in patients with various hormonal statuses and several subgroups of patients were identified, including premenopausal, postmenopausal, lactating, HRT, and tamoxifen subgroups. For patients who received tamoxifen therapy, the first follow-up image at treatment cessation was included, when available. The four ordinal BPE grades, ranging from minimal to marked, as reported in the official radiologic reports were used for analysis. Subgroup comparisons were performed using the Kruskal-Wallis rank sum test and χ2 test or Fisher exact test. Results A total of 507 female patients (mean age, 49.8 years ± 10.8 [SD]; range, 25-75 years) were included. Premenopausal patients (n = 200) exhibited higher BPE compared with postmenopausal patients (n = 200) (median grade, 1.0 [IQR, 0-2.0] vs 0 [IQR, 0-1.0]; P < .001). Lactating patients (n = 16) exhibited higher BPE (median grade, 3.0; IQR, 2.0-3.0) compared with nonlactating controls (median grade, 1.0; IQR, 0-2.0; P < .001). Patients receiving HRT (n = 14) exhibited higher BPE (median grade, 1.5; IQR, 0-3.0) compared with postmenopausal controls (median grade, 0; IQR, 0-1.0; P < .001). Patients receiving tamoxifen therapy (n = 77) exhibited lower BPE (median grade, 1.0; IQR, 0-2.0) compared with nontreated control patients (9% of patients with high BPE vs 31% for controls, P < .001) and increased BPE (median grade, 2.0; IQR, 1.5-2.5; P = .003) at the cessation of tamoxifen therapy. Conclusion Hormonal effects, including menopausal status, lactation, HRT, and tamoxifen therapy, influenced the degree of BPE at CEM. © RSNA, 2025 Supplemental material is available for this article. See also the editorial by Slanetz in this issue.

Asymmetric background parenchymal enhancement on contrast-enhanced mammography: associated factors, diagnostic workup, and clinical outcome

OBJECTIVES

To summarize our institutional experience with contrast-enhanced mammography (CEM) exams reporting asymmetric background parenchymal enhancement (BPE).

MATERIALS AND METHODS

Consecutive CEMs performed between December 2012 and July 2023 were retrospectively reviewed to identify exams reporting asymmetric BPE. Associated factors, the level of reporting certainty, BI-RADS score, diagnostic workup, and clinical outcome were summarized. BPE grades and BI-RADS were compared between initial CEM vs. immediate MRI and 6-month follow-up CEM, when indicated, using the Sign test.

RESULTS

Overall, 175/12,856 (1.4%) CEMs (140 female patients, mean age, 46 ± 8.0 years) reported asymmetric BPE. Reporting certainty was mostly high (n = 86), then moderate (n = 59) and low (n = 30). Associated factors included contralateral irradiation (n = 94), recent ipsilateral breast treatment (n = 14), and unilateral breastfeeding (n = 4). BI-RADS scores were 0 (n = 21), 1/2 (n = 75), 3 (n = 67), 4 (n = 3), and 6 (n = 1), or given for a finding other than asymmetric BPE (n = 8). Initial diagnostic-workup often included targeted-US (n = 107). Immediate MRI (n = 65) and/or 6-month CEM follow-up (n = 69) downgraded most cases, with a significant decrease in BPE grade compared to the initial CEM (p < 0.01 for both). On follow-up, two underlying cancers were diagnosed in the area of questionable asymmetric BPE.

CONCLUSION

Apparent asymmetric BPE is most often a benign finding with an identifiable etiology. However, rarely, it may mask an underlying malignancy presenting as non-mass enhancement, thus requiring additional scrutiny.

CLINICAL RELEVANCE STATEMENT

The variability in the diagnostic-workup of apparent asymmetric background parenchymal enhancement stresses the clinical challenge of this radiological finding. Further studies are required to verify these initial observations and to establish standardized management guidelines.

KEY POINTS

Apparent asymmetric background parenchymal enhancement usually represents a benign clinical correlate, though rarely it may represent malignancy. Evaluation of asymmetric background parenchymal enhancement varied considerably in the metrics that were examined. Targeted US and MRI can be useful in evaluating unexplained asymmetric background parenchymal enhancement.

Non-enhancing asymmetries on screening contrast-enhanced mammography: Is further diagnostic workup required?

OBJECTIVES

Asymmetries on screening contrast-enhanced mammography (CEM) often lead to patient recall. However, in diagnostic settings, negative CEM has effectively classified these as normal or benign, questioning the need for further workup of non-enhancing asymmetries (NEAs).

MATERIAL AND METHODS

A computational search of all screening CEM examinations performed between December-2012 and June-2021 was conducted to identify cases reporting NEAs. Their diagnostic workup was reviewed, and the positive predictive value for cancer was statistically compared to that of enhancing asymmetries on screening CEMs.

RESULTS

During the study period, 97 cases of 106 NEAs were identified among 3,482 screening CEM exams (2.8 %). NEAs were classified as asymmetry (n = 83), focal asymmetry (n = 22), and global asymmetry (n = 1), with no cases of developing asymmetry. The mean size of NEAs was 1.0 ± 0.7 cm (range: 0.3-4.9 cm). Diagnostic workup for NEAs included additional mammographic views (AMV) (n = 63), AMV plus ultrasound (n = 30), AMV plus MRI (n = 1), and all three modalities (n = 3), leading to four biopsies. None of the NEAs were malignant on follow-up, as opposed to enhancing asymmetries (P < 0.05).

CONCLUSION

NEAs detected on CEM were relatively uncommon and were usually investigated with additional mammographic views and US, yielding no cancer. Ruling out malignancy based on lack of enhancement without further workup may reduce patient recall rates and improve CEMs specificity.

Vi-PLUS: Pioneering Plane-Wave Ultrasound to Assess Breast Glandular Tissue in Healthy Women-A Pilot Study

BACKGROUND/OBJECTIVES

This study pioneers the application of the ViPLUS module, utilizing plane-wave ultrasound to measure breast tissue viscosity and elasticity. The primary goal was to establish normal reference values for viscosity in healthy women. Secondary objectives included exploring correlations between breast tissue viscosity and breast density categories, hormonal influences, and menstrual cycle phases.

METHODS

A prospective study was conducted on 245 asymptomatic women. Viscosity and elasticity measurements were obtained using the ViPLUS module, ensuring high reliability with stringent quality control measures. Data were statistically analyzed to evaluate correlations and group differences.

RESULTS

The median viscosity value for normal breast parenchyma was 1.7 Pa.s, with no significant variations based on breast density, menopausal status, or menstrual cycle phase. A strong correlation (rho = 0.866, p < 0.001) was observed between elasticity and viscosity values.

CONCLUSIONS

The findings suggest that breast viscosity is consistent across diverse physiological states, indicating its potential as an independent diagnostic marker. This parameter could be pivotal in future breast cancer screening strategies, especially for younger women and those with dense breasts.

Extremely dense breasts: A comprehensive review of increased cancer risk and supplementary screening methods

Women with extremely dense breasts account for approximately 10% of the screening population and face an increased lifetime risk of developing breast cancer. At the same time, the sensitivity of mammography, the first-line screening modality, is significantly reduced in this breast density group, owing to the masking effect of the abundant fibroglandular tissue. Consequently, this population has garnered increasing scientific attention due to the unique diagnostic challenge they present. Several research initiatives have attempted to address this diagnostic challenge by incorporating supplemental imaging modalities such as ultrasound, MRI, and contrast-enhanced mammography. Each of these modalities offers different benefits as well as limitations, both clinically and practically, including considerations of availability and costs. The purpose of this article is to critically review the background, latest scientific evidence, and future directions for the use of the various supplemental screening techniques for women with extremely dense breasts.

Diagnostic Accuracy of Screening Contrast-enhanced Mammography for Women with Extremely Dense Breasts at Increased Risk of Breast Cancer

Background Mammogram interpretation is challenging in female patients with extremely dense breasts (Breast Imaging Reporting and Data System [BI-RADS] category D), who have a higher breast cancer risk. Contrast-enhanced mammography (CEM) has recently emerged as a potential alternative; however, data regarding CEM utility in this subpopulation are limited. Purpose To evaluate the diagnostic performance of CEM for breast cancer screening in female patients with extremely dense breasts. Materials and Methods This retrospective single-institution study included consecutive CEM examinations in asymptomatic female patients with extremely dense breasts performed from December 2012 to March 2022. From CEM examinations, low-energy (LE) images were the equivalent of a two-dimensional full-field digital mammogram. Recombined images highlighting areas of contrast enhancement were constructed using a postprocessing algorithm. The sensitivity and specificity of LE images and CEM images (ie, including both LE and recombined images) were calculated and compared using the McNemar test. Results This study included 1299 screening CEM examinations (609 female patients; mean age, 50 years ± 9 [SD]). Sixteen screen-detected cancers were diagnosed, and two interval cancers occured. Five cancers were depicted at LE imaging and an additional 11 cancers were depicted at CEM (incremental cancer detection rate, 8.7 cancers per 1000 examinations). CEM sensitivity was 88.9% (16 of 18; 95% CI: 65.3, 98.6), which was higher than the LE examination sensitivity of 27.8% (five of 18; 95% CI: 9.7, 53.5) (P = .003). However, there was decreased CEM specificity (88.9%; 1108 of 1246; 95% CI: 87.0, 90.6) compared with LE imaging (specificity, 96.2%; 1199 of 1246; 95% CI: 95.0, 97.2) (P < .001). Compared with specificity at baseline, CEM specificity at follow-up improved to 90.7% (705 of 777; 95% CI: 88.5, 92.7; P = .01). Conclusion Compared with LE imaging, CEM showed higher sensitivity but lower specificity in female patients with extremely dense breasts, although specificity improved at follow-up. © RSNA, 2024 See also the editorial by Lobbes in this issue.

Background parenchymal enhancement on contrast-enhanced mammography: associations with breast density and patient's characteristics

PURPOSE

To evaluate if background parenchymal enhancement (BPE) on contrast-enhanced mammography (CEM), graded according to the 2022 CEM-dedicated Breast Imaging Reporting and Data System (BI-RADS) lexicon, is associated with breast density, menopausal status, and age.

METHODS

This bicentric retrospective analysis included CEM examinations performed for the work-up of suspicious mammographic findings. Three readers independently and blindly evaluated BPE on recombined CEM images and breast density on low-energy CEM images. Inter-reader reliability was estimated using Fleiss κ. Multivariable binary logistic regression was performed, dichotomising breast density and BPE as low (a/b BI-RADS categories, minimal/mild BPE) and high (c/d BI-RADS categories, moderate/marked BPE).

RESULTS

A total of 200 women (median age 56.8 years, interquartile range 50.5-65.6, 140/200 in menopause) were included. Breast density was classified as a in 27/200 patients (13.5%), as b in 110/200 (55.0%), as c in 52/200 (26.0%), and as d in 11/200 (5.5%), with moderate inter-reader reliability (κ = 0.536; 95% confidence interval [CI] 0.482-0.590). BPE was minimal in 95/200 patients (47.5%), mild in 64/200 (32.0%), moderate in 25/200 (12.5%), marked in 16/200 (8.0%), with substantial inter-reader reliability (κ = 0.634; 95% CI 0.581-0.686). At multivariable logistic regression, premenopausal status and breast density were significant positive predictors of high BPE, with adjusted odds ratios of 6.120 (95% CI 1.847-20.281, p = 0.003) and 2.416 (95% CI 1.095-5.332, p = 0.029) respectively.

CONCLUSION

BPE on CEM is associated with well-established breast cancer risk factors, being higher in women with higher breast density and premenopausal status.

Intraindividual Comparison of Different Methods for Automated BPE Assessment at Breast MRI: A Call for Standardization

Background The level of background parenchymal enhancement (BPE) at breast MRI provides predictive and prognostic information and can have diagnostic implications. However, there is a lack of standardization regarding BPE assessment. Purpose To investigate how well results of quantitative BPE assessment methods correlate among themselves and with assessments made by radiologists experienced in breast MRI. Materials and Methods In this pseudoprospective analysis of 5773 breast MRI examinations from 3207 patients (mean age, 60 years ± 10 [SD]), the level of BPE was prospectively categorized according to the Breast Imaging Reporting and Data System by radiologists experienced in breast MRI. For automated extraction of BPE, fibroglandular tissue (FGT) was segmented in an automated pipeline. Four different published methods for automated quantitative BPE extractions were used: two methods (A and B) based on enhancement intensity and two methods (C and D) based on the volume of enhanced FGT. The results from all methods were correlated, and agreement was investigated in comparison with the respective radiologist-based categorization. For surrogate validation of BPE assessment, how accurately the methods distinguished premenopausal women with (n = 50) versus without (n = 896) antihormonal treatment was determined. Results Intensity-based methods (A and B) exhibited a correlation with radiologist-based categorization of 0.56 ± 0.01 and 0.55 ± 0.01, respectively, and volume-based methods (C and D) had a correlation of 0.52 ± 0.01 and 0.50 ± 0.01 (P < .001). There were notable correlation differences (P < .001) between the BPE determined with the four methods. Among the four quantitation methods, method D offered the highest accuracy for distinguishing women with versus without antihormonal therapy (P = .01). Conclusion Results of different methods for quantitative BPE assessment agree only moderately among themselves or with visual categories reported by experienced radiologists; intensity-based methods correlate more closely with radiologists' ratings than volume-based methods. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Mann in this issue.

Background Breast Parenchymal Signal During Menstrual Cycle on Diffusion-Weighted MRI: A Prospective Study in Healthy Premenopausal Women

OBJECTIVE

To prospectively investigate the influence of the menstrual cycle on the background parenchymal signal (BPS) and apparent diffusion coefficient (ADC) of the breast on diffusion-weighted MRI (DW-MRI) in healthy premenopausal women.

MATERIALS AND METHODS

Seven healthy premenopausal women (median age, 37 years; range, 33-49 years) with regular menstrual cycles participated in this study. DW-MRI was performed during each of the four phases of the menstrual cycle (four examinations in total). Three radiologists independently assessed the BPS visual grade on images with b-values of 800 sec/mm² (b800), 1200 sec/mm² (b1200), and a synthetic 1500 sec/mm² (sb1500). Additionally, one radiologist conducted a quantitative analysis to measure the BPS volume (%) and ADC values of the BPS (ADCBPS) and fibroglandular tissue (ADCFGT). Changes in the visual grade, BPS volume (%), ADCBPS, and ADCFGT during the menstrual cycle were descriptively analyzed.

RESULTS

The visual grade of BPS in seven women varied from mild to marked on b800 and from minimal to moderate on b1200 and sb1500. As the b-value increased, the visual grade of BPS decreased. On b800 and sb1500, two of the seven volunteers showed the highest visual grade in the early follicular phase (EFP). On b1200, three of the seven volunteers showed the highest visual grades in EFP. The BPS volume (%) on b800 and b1200 showed the highest value in three of the six volunteers with dense breasts in EFP. Three of the seven volunteers showed the lowest ADCBPS in the EFP. Four of the seven volunteers showed the highest ADCBPS in the early luteal phase (ELP) and the lowest ADCFGT in the late follicular phase (LFP).

CONCLUSION

Most volunteers did not exhibit specific BPS patterns during their menstrual cycles. However, the highest BPS and lowest ADCBPS were more frequently observed in EFP than in the other menstrual cycle phases, whereas the highest ADCBPS was more common in ELP. The lowest ADCFGT was more frequent in LFP.

Explainable Precision Medicine in Breast MRI: A Combined Radiomics and Deep Learning Approach for the Classification of Contrast Agent Uptake

In DCE-MRI, the degree of contrast uptake in normal fibroglandular tissue, i.e., background parenchymal enhancement (BPE), is a crucial biomarker linked to breast cancer risk and treatment outcome. In accordance with the Breast Imaging Reporting & Data System (BI-RADS), it should be visually classified into four classes. The susceptibility of such an assessment to inter-reader variability highlights the urgent need for a standardized classification algorithm. In this retrospective study, the first post-contrast subtraction images for 27 healthy female subjects were included. The BPE was classified slice-wise by two expert radiologists. The extraction of radiomic features from segmented BPE was followed by dataset splitting and dimensionality reduction. The latent representations were then utilized as inputs to a deep neural network classifying BPE into BI-RADS classes. The network's predictions were elucidated at the radiomic feature level with Shapley values. The deep neural network achieved a BPE classification accuracy of 84 ± 2% (p-value < 0.00001). Most of the misclassifications involved adjacent classes. Different radiomic features were decisive for the prediction of each BPE class underlying the complexity of the decision boundaries. A highly precise and explainable pipeline for BPE classification was achieved without user- or algorithm-dependent radiomic feature selection.

Correlation between breast cancer and background parenchymal uptake on 18F-fluorodeoxyglucose positron emission tomography

PURPOSE

This study aimed to investigate differences in background parenchymal uptake (BPU) between patients with and without breast cancer using 18F-fluorodeoxyglucose positron emission tomography.

METHODS

Female patients (n = 130, 62.9 ± 12.7 years) with newly diagnosed breast cancer and 50 healthy participants (59.6 ± 13.3 years) without breast cancer were retrospectively included. BPU was evaluated using the maximum standardized uptake value. Data on participant age, body mass index, blood glucose level, and menopausal status were collected from medical records. Breast density was evaluated using mammography. Logistic regression analysis and receiver operating characteristic curves were used to examine the correlation between breast cancer and various characteristic factors, including BPU.

RESULTS

The BPU of patients with breast cancer was significantly higher than that of controls (P < 0.001). The results of logistic regression analysis regarding the presence of breast cancer demonstrated that BPU and menopausal status showed higher odds ratios of 13.6 and 4.25, respectively. The area under the receiver operating characteristic curve for BPU was 0.751.

CONCLUSIONS

Patients with breast cancer showed higher 18F-fluorodeoxyglucose-BPU. Glucose metabolism of mammary glands may correlate with the development of breast cancer.

Prediction of neoadjuvant chemotherapy pathological complete response for breast cancer based on radiomics nomogram of intratumoral and derived tissue

BACKGROUND

Non-invasive identification of breast cancer (BCa) patients with pathological complete response (pCR) after neoadjuvant chemotherapy (NACT) is critical to determine appropriate surgical strategies and guide the resection range of tumor. This study aimed to examine the effectiveness of a nomogram created by combining radiomics signatures from both intratumoral and derived tissues with clinical characteristics for predicting pCR after NACT.

METHODS

The clinical data of 133 BCa patients were analyzed retrospectively and divided into training and validation sets. The radiomics features for Intratumoral, peritumoral, and background parenchymal enhancement (BPE) in the training set were dimensionalized. Logistic regression analysis was used to select the optimal feature set, and a radiomics signature was constructed using a decision tree. The signature was combined with clinical features to build joint models and generate nomograms. The area under curve (AUC) value of receiver operating characteristic (ROC) curve was then used to assess the performance of the nomogram and independent predictors.

RESULTS

Among single region, intratumoral had the best predictive value. The diagnostic performance of the intratumoral improved after adding the BPE features. The AUC values of the radiomics signature were 0.822 and 0.82 in the training and validation sets. Multivariate logistic regression analysis revealed that age, ER, PR, Ki-67, and radiomics signature were independent predictors of pCR in constructing a nomogram. The AUC of the nomogram in the training and validation sets were 0.947 and 0.933. The DeLong test showed that the nomogram had statistically significant differences compared to other independent predictors in both the training and validation sets (P < 0.05).

CONCLUSION

BPE has value in predicting the efficacy of neoadjuvant chemotherapy, thereby revealing the potential impact of tumor growth environment on the efficacy of neoadjuvant chemotherapy.

The role of ATP binding cassette (ABC) transporters in breast cancer: Evaluating prognosis, predicting immunity, and guiding treatment

Breast cancer is currently the most prevalent form of cancer worldwide. Nevertheless, there remains limited clarity regarding our understanding of the tumor microenvironment and metabolic characteristics associated with it. ATP-binding cassette (ABC) transporters are the predominant transmembrane transporters found in organisms. Therefore, it is essential to investigate the role of ABC transporters in breast cancer. Transcriptome data from breast cancer patients were downloaded from the TCGA database. ABC transporter-related genes were obtained from the Genecards database. By LASSO regression, ABC-associated prognostic signature was constructed in breast cancer. Subsequently, immune microenvironment analysis was performed. Finally, cell experiments were performed to verify the function of ABCB7 in the breast cancer cell lines MDA-MB-231 and MCF-7. Using the ABC transporter-associated signature, we calculated a risk score for each breast cancer patient. Patients with breast cancer were subsequently categorized into high-risk and low-risk groups, utilizing the median risk score as the threshold. Notably, patients in the high-risk group exhibited significantly worse prognosis (P<0.05). Additionally, differences were observed in terms of immune cell infiltration levels, immune correlations, and gene expression of immune checkpoints between the two groups. Functional experiments conducted on breast cancer cell lines MDA-MB-231 and MCF-7 demonstrated that ABCB7 knockdown significantly diminished cell activity, proliferation, invasion, and migration. These findings emphasize the significance of understanding ABC transporter-mediated metabolic and transport characteristics in breast cancer, offering promising directions for further research and potential therapeutic interventions.

Generalizable attention U-Net for segmentation of fibroglandular tissue and background parenchymal enhancement in breast DCE-MRI

OBJECTIVES

Development of automated segmentation models enabling standardized volumetric quantification of fibroglandular tissue (FGT) from native volumes and background parenchymal enhancement (BPE) from subtraction volumes of dynamic contrast-enhanced breast MRI. Subsequent assessment of the developed models in the context of FGT and BPE Breast Imaging Reporting and Data System (BI-RADS)-compliant classification.

METHODS

For the training and validation of attention U-Net models, data coming from a single 3.0-T scanner was used. For testing, additional data from 1.5-T scanner and data acquired in a different institution with a 3.0-T scanner was utilized. The developed models were used to quantify the amount of FGT and BPE in 80 DCE-MRI examinations, and a correlation between these volumetric measures and the classes assigned by radiologists was performed.

RESULTS

To assess the model performance using application-relevant metrics, the correlation between the volumes of breast, FGT, and BPE calculated from ground truth masks and predicted masks was checked. Pearson correlation coefficients ranging from 0.963 ± 0.004 to 0.999 ± 0.001 were achieved. The Spearman correlation coefficient for the quantitative and qualitative assessment, i.e., classification by radiologist, of FGT amounted to 0.70 (p < 0.0001), whereas BPE amounted to 0.37 (p = 0.0006).

CONCLUSIONS

Generalizable algorithms for FGT and BPE segmentation were developed and tested. Our results suggest that when assessing FGT, it is sufficient to use volumetric measures alone. However, for the evaluation of BPE, additional models considering voxels' intensity distribution and morphology are required.

CRITICAL RELEVANCE STATEMENT

A standardized assessment of FGT density can rely on volumetric measures, whereas in the case of BPE, the volumetric measures constitute, along with voxels' intensity distribution and morphology, an important factor.

KEY POINTS

• Our work contributes to the standardization of FGT and BPE assessment. • Attention U-Net can reliably segment intricately shaped FGT and BPE structures. • The developed models were robust to domain shift.

Ultrafast DCE-MRI for discriminating pregnancy-associated breast cancer lesions from lactation related background parenchymal enhancement

OBJECTIVE

To investigate the utility of ultrafast dynamic-contrast-enhanced (DCE) MRI in visualization and quantitative characterization of pregnancy-associated breast cancer (PABC) and its differentiation from background-parenchymal-enhancement (BPE) among lactating patients.

MATERIALS AND METHODS

Twenty-nine lactating participants, including 10 PABC patients and 19 healthy controls, were scanned on 3-T MRI using a conventional DCE protocol interleaved with a golden-angle radial sparse parallel (GRASP) ultrafast sequence for the initial phase. The timing of the visualization of PABC lesions was compared to lactational BPE. Contrast-noise ratio (CNR) was compared between the ultrafast and conventional DCE sequences. The differences in each group's ultrafast-derived kinetic parameters including maximal slope (MS), time to enhancement (TTE), and area under the curve (AUC) were statistically examined using the Mann-Whitney test and receiver operator characteristic (ROC) curve analysis.

RESULTS

On ultrafast MRI, breast cancer lesions enhanced earlier than BPE (p < 0.0001), enabling breast cancer visualization freed from lactation BPE. A higher CNR was found for ultrafast acquisitions vs. conventional DCE (p < 0.05). Significant differences in AUC, MS, and TTE values were found between the tumor and BPE (p < 0.05), with ROC-derived AUC of 0.86 ± 0.06, 0.82 ± 0.07, and 0.68 ± 0.08, respectively. The BPE grades of the lactating PABC patients were reduced as compared with the healthy lactating controls (p < 0.005).

CONCLUSION

Ultrafast DCE MRI allows BPE-free visualization of lesions, improved tumor conspicuity, and kinetic quantification of breast cancer during lactation. Implementation of this method may assist in the utilization of breast MRI for lactating patients.

CLINICAL RELEVANCE

The ultrafast sequence appears to be superior to conventional DCE MRI in the challenging evaluation of the lactating breast. Thus, supporting its possible utilization in the setting of high-risk screening during lactation and the diagnostic workup of PABC.

KEY POINTS

• Differences in the enhancement slope of cancer relative to BPE allowed the optimal visualization of PABC lesions on mid-acquisitions of ultrafast DCE, in which the tumor enhanced prior to the background parenchyma. • The conspicuity of PABC lesions on top of the lactation-related BPE was increased using an ultrafast sequence as compared with conventional DCE MRI. • Ultrafast-derived maps provided further characterization and parametric contrast between PABC lesions and lactation-related BPE.

Quantitative background parenchymal enhancement and fibro-glandular density at breast MRI: Association with BRCA status

OBJECTIVES

To investigate whether MRI-based measurements of fibro-glandular tissue volume, breast density (MRBD), and background parenchymal enhancement (BPE) could be used to stratify two cohorts of healthy women: BRCA carriers and women at population risk of breast cancer.

METHODS

Pre-menopausal women aged 40-50 years old were scanned at 3 T, employing a standard breast protocol including a DCE-MRI (35 and 30 participants in high- and low-risk groups, respectively). The dynamic range of the DCE protocol was characterised and both breasts were masked and segmented with minimal user input to produce measurements of fibro-glandular tissue volume, MRBD, and voxelwise BPE. Statistical tests were performed to determine inter- and intra-user repeatability, evaluate the symmetry between metrics derived from left and right breasts, and investigate MRBD and BPE differences between the high- and low-risk cohorts.

RESULTS

Intra- and inter-user reproducibility in estimates of fibro-glandular tissue volume, MRBD, and median BPE estimations were good, with coefficients of variation < 15%. Coefficients of variation between left and right breasts were also low (< 25%). There were no significant correlations between fibro-glandular tissue volume, MRBD, and BPE for either risk group. However, the high-risk group had higher BPE kurtosis, although linear regression analysis did not reveal significant associations between BPE kurtosis and breast cancer risk.

CONCLUSIONS

This study found no significant differences or correlations in fibro-glandular tissue volume, MRBD, or BPE metrics between the two groups of women with different levels of breast cancer risk. However, the results support further investigation into the heterogeneity of parenchymal enhancement.

KEY POINTS

• A semi-automated method enabled quantitative measurements of fibro-glandular tissue volume, breast density, and background parenchymal enhancement with minimal user intervention. • Background parenchymal enhancement was quantified over the entire parenchyma, segmented in pre-contrast images, thus avoiding region selection. • No significant differences and correlations in fibro-glandular tissue volume, breast density, and breast background parenchymal enhancement were found between two cohorts of women at high and low levels of breast cancer risk.

MRI can accurately diagnose breast cancer during lactation

OBJECTIVE

To test the diagnostic performance of breast dynamic contrast-enhanced (DCE) MRI during lactation.

MATERIALS AND METHODS

Datasets of 198 lactating patients, including 66 pregnancy-associated breast cancer (PABC) patients and 132 controls, who were scanned by DCE on 1.5-T MRI, were retrospectively evaluated. Six blinded, expert radiologists independently read a single DCE maximal intensity projection (MIP) image for each case and were asked to determine whether malignancy was suspected and the background-parenchymal-enhancement (BPE) grade. Likewise, computer-aided diagnosis CAD MIP images were independently read by the readers. Contrast-to-noise ratio (CNR) analysis was measured and compared among four consecutive acquisitions of DCE subtraction images.

RESULTS

For MIP-DCE images, the readers achieved the following means: sensitivity 93.3%, specificity 80.3%, positive-predictive-value 70.4, negative-predictive-value 96.2, and diagnostic accuracy of 84.6%, with a substantial inter-rater agreement (Kappa = 0.673, p value < 0.001). Most false-positive interpretations were attributed to either the MIP presentation, an underlying benign lesion, or an asymmetric appearance due to prior treatments. CAD's derived diagnostic accuracy was similar (p = 0.41). BPE grades were significantly increased in the healthy controls compared to the PABC cohort (p < 0.001). CNR significantly decreased by 11-13% in each of the four post-contrast images (p < 0.001).

CONCLUSION

Breast DCE MRI maintains its high efficiency among the lactating population, probably due to a vascular-steal phenomenon, which causes a significant reduction of BPE in cancer cases. Upon validation by prospective, multicenter trials, this study could open up the opportunity for breast MRI to be indicated in the screening and diagnosis of lactating patients, with the aim of facilitating an earlier diagnosis of PABC.

KEY POINTS

• A single DCE MIP image was sufficient to reach a mean sensitivity of 93.3% and NPV of 96.2%, to stress the high efficiency of breast MRI during lactation. • Reduction in BPE among PABC patients compared to the lactating controls suggests that several factors, including a possible vascular steal phenomenon, may affect cancer patients. • Reduction in CNR along four consecutive post-contrast acquisitions highlights the differences in breast carcinoma and BPE kinetics and explains the sufficient conspicuity on the first subtracted image.

Contrast-Enhanced Mammography (CEM) Capability to Distinguish Molecular Breast Cancer Subtypes

With breast cancer ranking first among the most common malignant neoplasms in the world, new techniques of early detection are in even more demand than before. Our awareness of tumors' biology is expanding and may be used to treat patients more efficiently. A link between radiology and pathology was searched for in our study, as well as the answer to the question of whether a tumor type can be seen on contrast-enhanced mammography and if such knowledge may serve as part of precision medicine.

Applying artificial intelligence technology to assist with breast cancer diagnosis and prognosis prediction

Breast cancer remains the most diagnosed cancer in women. Advances in medical imaging modalities and technologies have greatly aided in the early detection of breast cancer and the decline of patient mortality rates. However, reading and interpreting breast images remains difficult due to the high heterogeneity of breast tumors and fibro-glandular tissue, which results in lower cancer detection sensitivity and specificity and large inter-reader variability. In order to help overcome these clinical challenges, researchers have made great efforts to develop computer-aided detection and/or diagnosis (CAD) schemes of breast images to provide radiologists with decision-making support tools. Recent rapid advances in high throughput data analysis methods and artificial intelligence (AI) technologies, particularly radiomics and deep learning techniques, have led to an exponential increase in the development of new AI-based models of breast images that cover a broad range of application topics. In this review paper, we focus on reviewing recent advances in better understanding the association between radiomics features and tumor microenvironment and the progress in developing new AI-based quantitative image feature analysis models in three realms of breast cancer: predicting breast cancer risk, the likelihood of tumor malignancy, and tumor response to treatment. The outlook and three major challenges of applying new AI-based models of breast images to clinical practice are also discussed. Through this review we conclude that although developing new AI-based models of breast images has achieved significant progress and promising results, several obstacles to applying these new AI-based models to clinical practice remain. Therefore, more research effort is needed in future studies.

Breast MRI during pregnancy and lactation: clinical challenges and technical advances

The breast experiences substantial changes in morphology and function during pregnancy and lactation which affects its imaging properties and may reduce the visibility of a concurrent pathological process. The high incidence of benign gestational-related entities may further add complexity to the clinical and radiological evaluation of the breast during the period. Consequently, pregnancy-associated breast cancer (PABC) is often a delayed diagnosis and carries a poor prognosis. This state-of-the-art pictorial review illustrates how despite currently being underutilized, technical advances and new clinical evidence support the use of unenhanced breast MRI during pregnancy and both unenhanced and dynamic-contrast enhanced (DCE) during lactation, to serve as effective supplementary modalities in the diagnostic work-up of PABC.

Post-Processing Bias Field Inhomogeneity Correction for Assessing Background Parenchymal Enhancement on Breast MRI as a Quantitative Marker of Treatment Response

Background parenchymal enhancement (BPE) of breast fibroglandular tissue (FGT) in dynamic contrast-enhanced breast magnetic resonance imaging (MRI) has shown an association with response to neoadjuvant chemotherapy (NAC) in patients with breast cancer. Fully automated segmentation of FGT for BPE calculation is a challenge when image artifacts are present. Low spatial frequency intensity nonuniformity due to coil sensitivity variations is known as bias or inhomogeneity and can affect FGT segmentation and subsequent BPE measurement. In this study, we utilized the N4ITK algorithm for bias correction over a restricted bilateral breast volume and compared the contralateral FGT segmentations based on uncorrected and bias-corrected images in three MRI examinations at pre-treatment, early treatment and inter-regimen timepoints during NAC. A retrospective analysis of 2 cohorts was performed: one with 735 patients enrolled in the multi-center I-SPY 2 TRIAL and the sub-cohort of 340 patients meeting a high-quality benchmark for segmentation. Bias correction substantially increased the FGT segmentation quality for 6.3–8.0% of examinations, while it substantially decreased the quality for no examination. Our results showed improvement in segmentation quality and a small but statistically significant increase in the resulting BPE measurement after bias correction at all timepoints in both cohorts. Continuing studies are examining the effects on pCR prediction.