Population-Based Assessment of the Association Between Magnetic Resonance Imaging Background Parenchymal Enhancement and Future Primary Breast Cancer Risk

Magnetic resonance imaging insights from active surveillance of women with ductal carcinoma in situ

New approaches are needed to determine which ductal carcinoma in situ (DCIS) is at high risk for progression to invasive ductal carcinoma (IDC). We retrospectively studied DCIS patients who declined surgery (2002-2019), and received endocrine therapy (ET) and breast MRI. Baseline MRI and changes at 3 months and 6 months were analyzed by recursive partitioning to stratify IDC risk. Sixty-two patients (63 DCIS; 1 bilateral) with a mean follow-up of 8.5 years were included. Fifty-one percent remained on active surveillance (AS) without evidence of IDC, with a mean duration of 7.6 years. A decision tree based on MRI features of lesion distinctness and background parenchymal enhancement (BPE) at baseline and change after 3 months of ET stratified patients into low, intermediate, and high risk for progression to IDC. MRI imaging features in patients treated with ET and undergoing AS, may help determine which DCIS lesions are at low versus high risk for IDC.

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.

Contrast-enhanced mammography for surveillance in women with a personal history of breast cancer

PURPOSE

Women with a personal history of breast cancer have an increased risk of subsequent breast malignancy and may benefit from more sensitive surveillance than conventional mammography (MG). We previously reported outcomes for first surveillance episode using contrast-enhanced mammography (CEM), demonstrating higher sensitivity and comparable specificity to MG. We now report CEM performance for subsequent surveillance.

METHODS

A retrospective study of 1,190 women in an Australian hospital setting undergoing annual surveillance following initial surveillance CEM between June 2016 and December 2022. Outcome measures were recall rate, cancer detection rate, contribution of contrast to recalls, false positive rate, interval cancer rate and characteristics of surveillance detected and interval cancers.

RESULTS

2,592 incident surveillance episodes were analysed, of which 93% involved contrast-based imaging. Of 116 (4.5%) recall episodes, 40/116 (34%) recalls were malignant (27 invasive; 13 ductal carcinoma in situ), totalling 15.4 cancers per 1000 surveillance episodes. 55/116 (47%) recalls were contrast-directed including 17/40 (43%) true positive recalls. Tumour features were similar for contrast-directed recalls and other diagnoses. 8/9 (89%) of contrast-directed invasive recalls were Grade 2-3, and 5/9 (56%) were triple negative breast cancers. There were two symptomatic interval cancers (0.8 per 1000 surveillance episodes, program sensitivity 96%).

CONCLUSION

Routine use of CEM in surveillance of women with PHBC led to an increase in the detection of clinically significant malignant lesions, with a low interval cancer rate compared to previous published series. Compared to mammographic surveillance, contrast-enhanced mammography increases the sensitivity of surveillance programs for women with PHBC.

Background Parenchymal Enhancement: A Comprehensive Update

Breast MR imaging is a complementary screening tool for patients at high risk for breast cancer and has been used in the diagnostic setting. Normal enhancement of breast tissue on MR imaging is called breast parenchymal enhancement (BPE), which occurs after administration of an intravenous contrast agent. BPE varies widely due to menopausal status, use of exogenous hormones, and breast cancer treatment. Degree of BPE has also been shown to influence breast cancer risk and may predict treatment outcomes. The authors provide a comprehensive update on BPE with review of the recent literature.

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.

Automated rating of background parenchymal enhancement in MRI of extremely dense breasts without compromising the association with breast cancer in the DENSE trial

OBJECTIVES

Background parenchymal enhancement (BPE) on dynamic contrast-enhanced MRI (DCE-MRI) as rated by radiologists is subject to inter- and intrareader variability. We aim to automate BPE category from DCE-MRI.

METHODS

This study represents a secondary analysis of the Dense Tissue and Early Breast Neoplasm Screening trial. 4553 women with extremely dense breasts who received supplemental breast MRI screening in eight hospitals were included. Minimal, mild, moderate and marked BPE rated by radiologists were used as reference. Fifteen quantitative MRI features of the fibroglandular tissue were extracted to predict BPE using Random Forest, Naïve Bayes, and KNN classifiers. Majority voting was used to combine the predictions. Internal-external validation was used for training and validation. The inverse-variance weighted mean accuracy was used to express mean performance across the eight hospitals. Cox regression was used to verify non inferiority of the association between automated rating and breast cancer occurrence compared to the association for manual rating.

RESULTS

The accuracy of majority voting ranged between 0.56 and 0.84 across the eight hospitals. The weighted mean prediction accuracy for the four BPE categories was 0.76. The hazard ratio (HR) of BPE for breast cancer occurrence was comparable between automated rating and manual rating (HR = 2.12 versus HR = 1.97, P = 0.65 for mild/moderate/marked BPE relative to minimal BPE).

CONCLUSION

It is feasible to rate BPE automatically in DCE-MRI of women with extremely dense breasts without compromising the underlying association between BPE and breast cancer occurrence. The accuracy for minimal BPE is superior to that for other BPE categories.

Clinical Significance of Background Parenchymal Enhancement in Breast Cancer Risk Stratification

BACKGROUND

Background parenchymal enhancement (BPE) is an established breast cancer risk factor. However, the relationship between BPE levels and breast cancer risk stratification remains unclear.

PURPOSE

To evaluate the clinical relationship between BPE levels and breast cancer risk with covariate adjustments for age, ethnicity, and hormonal status.

STUDY TYPE

Retrospective.

POPULATION

954 screening breast MRI datasets representing 721 women divided into four cohorts: women with pathogenic germline breast cancer (BRCA) mutations (Group 1, N = 211), women with non-BRCA germline mutations (Group 2, N = 60), women without high-risk germline mutations but with a lifetime breast cancer risk of ≥20% using the Tyrer-Cuzick model (Group 3, N = 362), and women with <20% lifetime risk (Group 4, N = 88).

FIELD STRENGTH/SEQUENCE

3 T/axial non-fat-saturated T1, short tau inversion recovery, fat-saturated pre-contrast, and post-contrast T1-weighted images.

ASSESSMENT

Data on age, body mass index, ethnicity, menopausal status, genetic predisposition, and hormonal therapy use were collected. BPE levels were evaluated by two breast fellowship-trained radiologists independently in accordance with BI-RADS, with a third breast fellowship-trained radiologist resolving any discordance.

STATISTICAL TESTS

Propensity score matching (PSM) was utilized to adjust covariates, including age, ethnicity, menopausal status, hormonal treatments, and prior bilateral oophorectomy. The Mann-Whitney U test, chi-squared test, and univariate and multiple logistic regression analysis were performed, with an odds ratio (OR) and corresponding 95% confidence interval. Weighted Kappa statistic was used to assess inter-reader variation. A P value <0.05 indicated a significant result.

RESULTS

In the assessment of BPE, there was substantial agreement between the two interpreting radiologists (κ = 0.74). Patient demographics were not significantly different between patient groups after PSM. The BPE of Group 1 was significantly lower than that of Group 4 and Group 3 among premenopausal women. In estimating the BPE level, the OR of gene mutations was 0.35.

DATA CONCLUSION

Adjusting for potential confounders, the BPE level of premenopausal women with BRCA mutations was significantly lower than that of non-high-risk women.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY: Stage 3.

Abbreviated Breast MRI: State of the Art

Abbreviated MRI is an umbrella term, defined as a focused MRI examination tailored to answer a single specific clinical question. For abbreviated breast MRI, this question is: "Is there evidence of breast cancer?" Abbreviated MRI of the breast makes maximum use of the fact that the kinetics of breast cancers and of benign tissue differ most in the very early postcontrast phase; therefore, abbreviated breast MRI focuses on this period. The different published approaches to abbreviated MRI include the following three subtypes: (a) short protocols, consisting of a precontrast and either a single postcontrast acquisition (first postcontrast subtracted [FAST]) or a time-resolved series of postcontrast acquisitions with lower spatial resolution (ultrafast [UF]), obtained during the early postcontrast phase immediately after contrast agent injection; (b) abridged protocols, consisting of FAST or UF acquisitions plus selected additional pulse sequences; and (c) noncontrast protocols, where diffusion-weighted imaging replaces the contrast information. Abbreviated MRI was proposed to increase tolerability of and access to breast MRI as a screening tool. But its widening application now includes follow-up after breast cancer and even diagnostic assessment. This review defines the three subtypes of abbreviated MRI, highlighting the differences between the protocols and their clinical implications and summarizing the respective evidence on diagnostic accuracy and clinical utility.

Clinical and imaging modality factors impacting radiological interpretation of breast screening in young women with neurofibromatosis type 1

Young women with Neurofibromatosis type 1 (NF1) have a high risk of developing breast cancer and poorer survival following breast cancer diagnosis. International guidelines recommend commencing breast screening between 30 and 35 years; however, the optimal screening modality is unestablished, and previous reports suggest that breast imaging may be complicated by the presence of intramammary and cutaneous neurofibromas (cNFs). The aim of this study was to explore potential barriers to implementation of breast screening for young women with NF1.Twenty-seven women (30-47 years) with NF1 completed breast screening with breast MRI, mammogram and breast ultrasound. Nineteen probably benign/suspicious lesions were detected across 14 women. Despite the presence of breast cNFs, initial biopsy rate for participants with NF1 (37%), were comparable to a BRCA pathogenic variant (PV) cohort (25%) (P = 0.311). No cancers or intramammary neurofibromas were identified. Most participants (89%) returned for second round screening.The presence of cNF did not affect clinician confidence in 3D mammogram interpretation, although increasing breast density, frequently seen in young women, impeded confidence for 2D and 3D mammogram. Moderate or marked background parenchymal enhancement on MRI was higher in the NF1 cohort (70.4%) than BRCA PV carriers (47.3%), which is an independent risk factor for breast cancer.Breast MRI was the preferred mode of screening over mammogram, as the majority (85%) with NF1 demonstrated breast density (BI-RADS 3C/4D), which hinders mammogram interpretation. For those with high breast density and high cNF breast coverage, 3D rather than 2D mammogram is preferred, if MRI is unavailable.

Quantitative evaluation of Kaiser score in diagnosing breast dynamic contrast-enhanced magnetic resonance imaging for patients with high-grade background parenchymal enhancement

Background

High-grade background parenchymal enhancement (BPE), including moderate and marked, poses a considerable challenge for the diagnosis of breast disease due to its tendency to increase the rate of false positives and false negatives. The purpose of our study was to explore whether the Kaiser score can be used for more accurate assessment of benign and malignant lesions in high-grade BPE compared with the Breast Imaging Reporting and Data System (BI-RADS).

Methods

A retrospective review was conducted on consecutive breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) scans from 2 medical centers. Included were patients who underwent DCE-MRI demonstrating high-grade BPE and who had a pathology-confirmed diagnosis. Excluded were patients who had received neoadjuvant chemotherapy or who had undergone biopsy prior to MRI examination. Two physicians with more than 7 years of experience specializing in breast imaging diagnosis jointly reviewed breast magnetic resonance (MR) images. The Kaiser score was used to determine the sensitivity, specificity, and positive predictive value (PPV), and negative predictive value (NPV) of the BI-RADS from different BPE groups and different enhancement types. The performance of the Kaiser score and BI-RADS were compared according to diagnostic accuracy.

Results

A total of 126 cases of high-grade BPE from 2 medical centers were included in this study. The Kaiser score had a higher specificity and PPV than did the BI-RADS (87.5% vs. 46.3%) as well as a higher PPV (94.3% vs. 79.8%). The value of diagnostic accuracy and 95% confidence interval (CI) for the Kaiser score (accuracy 0.928; 95% CI: 0.883-0.973) was larger than that for BI-RADS (accuracy 0.810; 95% CI: 0.741-0.879). Moreover, the Kaiser score had a significantly higher value of diagnostic accuracy for both mass and non-mass enhancement, especially mass lesions (Kaiser score: accuracy 0.947, 95% CI: 0.902-0.992; BI-RADS: accuracy 0.821, 95% CI: 0.782-0.860), with a P value of 0.006.

Conclusions

The Kaiser score is a useful diagnostic tool for the evaluation of high-grade BPE lesions, with a higher specificity, PPV, and diagnostic accuracy as compared to the BI-RADS.

Overcoming the limitations of screening mammography in Japan and Korea: a paradigm shift to personalized breast cancer screening based on ultrasonography

Screening mammography programs have been implemented in numerous Western countries with the aim of reducing breast cancer mortality. However, despite over 20 years of population-based screening mammography, the mortality rates in Japan and Korea continue to rise. This may be due to the fact that screening mammography is not as effective for Japanese and Korean women, who often have dense breasts. This density decreases the sensitivity of mammography due to a masking effect. Therefore, the early detection of small invasive cancers requires more than just mammography, particularly for women in their 40s. This review discusses the limitations and challenges of screening mammography, as well as the keys to successful population-based breast cancer screening in Japan and Korea. This includes a focus on breast ultrasonography techniques, which are based on histopathologic anatomical knowledge, and personalized screening strategies that are based on risk assessments measured by glandular tissue components.

Association of Breast Cancer Odds with Background Parenchymal Enhancement Quantified Using a Fully Automated Method at MRI: The IMAGINE Study

Background Background parenchymal enhancement (BPE) at breast MRI has been associated with increased breast cancer risk in several independent studies. However, variability of subjective BPE assessments have precluded its use in clinical practice. Purpose To examine the association between fully objective measures of BPE at MRI and odds of breast cancer. Materials and Methods This prospective case-control study included patients who underwent a bilateral breast MRI examination and were receiving care at one of three centers in the United States from November 2010 to July 2017. Breast volume, fibroglandular tissue (FGT) volume, and BPE were quantified using fully automated software. Fat volume was defined as breast volume minus FGT volume. BPE extent was defined as the proportion of FGT voxels with enhancement of 20% or more. Spearman rank correlation between quantitative BPE extent and Breast Imaging Reporting and Data System (BI-RADS) BPE categories assigned by an experienced board-certified breast radiologist was estimated. With use of multivariable logistic regression, breast cancer case-control status was regressed on tertiles (low, moderate, and high) of BPE, FGT volume, and fat volume, with adjustment for covariates. Results In total, 536 case participants with breast cancer (median age, 48 years [IQR, 43-55 years]) and 940 cancer-free controls (median age, 46 years [IQR, 38-55 years]) were included. BPE extent was positively associated with BI-RADS BPE (rs = 0.54; P < .001). Compared with low BPE extent (range, 2.9%-34.2%), high BPE extent (range, 50.7%-97.3%) was associated with increased odds of breast cancer (odds ratio [OR], 1.74 [95% CI: 1.23, 2.46]; P for trend = .002) in a multivariable model also including FGT volume (OR, 1.39 [95% CI: 0.97, 1.98]) and fat volume (OR, 1.46 [95% CI: 1.04, 2.06]). The association of high BPE extent with increased odds of breast cancer was similar for premenopausal and postmenopausal women (ORs, 1.75 and 1.83, respectively; interaction P = .73). Conclusion Objectively measured BPE at breast MRI is associated with increased breast cancer odds for both premenopausal and postmenopausal women. Clinical trial registration no. NCT02301767 © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Bokacheva in this issue.

An MRI-Based Radiomics Nomogram to Distinguish Ductal Carcinoma In Situ with Microinvasion From Ductal Carcinoma In Situ of Breast Cancer

RATIONALE AND OBJECTIVES

Accurate preoperative differentiation between ductal carcinoma in situ with microinvasion (DCISM) and ductal carcinoma in situ (DCIS) could facilitate treatment optimization and individualized risk assessment. The present study aims to build and validate a radiomics nomogram based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) that could distinguish DCISM from pure DCIS breast cancer.

MATERIALS AND METHODS

MR images of 140 patients obtained between March 2019 and November 2022 at our institution were included. Patients were randomly divided into a training (n = 97) and a test set (n = 43). Patients in both sets were further split into DCIS and DCISM subgroups. The independent clinical risk factors were selected by multivariate logistic regression to establish the clinical model. The optimal radiomics features were chosen by the least absolute shrinkage and selection operator, and a radiomics signature was built. The nomogram model was constructed by integrating the radiomics signature and independent risk factors. The discrimination efficacy of our nomogram was assessed by using calibration and decision curves.

RESULTS

Six features were selected to construct the radiomics signature for distinguishing DCISM from DCIS. The radiomics signature and nomogram model exhibited better calibration and validation performance in the training (AUC 0.815, 0.911, 95% confidence interval [CI], 0.703-0.926, 0.848-0.974) and test (AUC 0.830, 0.882, 95% CI, 0.672-0.989, 0.764-0.999) sets than in the clinical factor model (AUC 0.672, 0.717, 95% CI, 0.544-0.801, 0.527-0.907). The decision curve also demonstrated that the nomogram model exhibited good clinical utility.

CONCLUSION

The proposed noninvasive MRI-based radiomics nomogram model showed good performance in distinguishing DCISM from DCIS.

Qualitative assessments of density and background parenchymal enhancement on contrast-enhanced spectral mammography associated with breast cancer risk in high-risk women

OBJECTIVE

To investigate the correlation between the risk of breast cancer for high-risk females and the density and background parenchymal enhancement (BPE) on contrast-enhanced spectral mammography (CESM).

METHODS

Females at high-risk, without breast cancer history and received CESM from July 2016 to December 2017 were retrospectively enrolled. The longest follow-up time was 4.5 years, and patients who developed breast cancer with maximized follow-up time were classified as cancer cohort, while females who did not develop breast cancer were categorized as control cohort. These two cohorts were one-to-one matched in age, family and/or genetic history of breast cancer, menopausal status and BRCA status. The density and BPE at CESM imaging were assessed. Conditional logistic regression was applied to evaluate the relationship between imaging features and breast cancer risk.

RESULTS

During the follow-up interval, 90 women at high-risk without history of breast cancer were newly diagnosed. Compared with minimal BPE, increasing BPE levels were associated with the risk of breast cancer among high-risk females in a time interval of 4.5 years (mild: odds ratio [OR]=3.2, p = 0.001; moderate: OR = 4.0, p = 0.002; marked: OR = 11.2, p < 0.001). In addition, females with mild, moderate or marked BPE were four times more likely to be diagnosed with breast cancer than females with minimal BPE in a time interval of 4.5 years (OR = 4.0, p < 0.001).

CONCLUSION

Qualitative CESM BPE assessment may be useful in the prediction of breast cancer risk among high-risk females.

ADVANCES IN KNOWLEDGE

• Qualitative CESM BPE assessment may be useful in the prediction of breast cancer risk among high-risk women during the follow-up period of 4.5 years. • The significance of breast density as an independent risk factor is not fully established for high-risk women during the follow-up period of 4.5 years.

Assessing Quantitative Parenchymal Features at Baseline Dynamic Contrast-enhanced MRI and Cancer Occurrence in Women with Extremely Dense Breasts

Background Automated identification of quantitative breast parenchymal enhancement features on dynamic contrast-enhanced (DCE) MRI scans could provide added value in assessment of breast cancer risk in women with extremely dense breasts. Purpose To automatically identify quantitative properties of the breast parenchyma on baseline DCE MRI scans and assess their association with breast cancer occurrence in women with extremely dense breasts. Materials and Methods This study represents a secondary analysis of the Dense Tissue and Early Breast Neoplasm Screening trial. MRI was performed in eight hospitals between December 2011 and January 2016. After segmentation of fibroglandular tissue, quantitative features (including volumetric density, volumetric morphology, and enhancement characteristics) of the parenchyma were extracted from baseline MRI scans. Principal component analysis was used to identify parenchymal measures with the greatest variance. Multivariable Cox proportional hazards regression was applied to assess the association between breast cancer occurrence and quantitative parenchymal features, followed by stratification of significant features into tertiles. Results A total of 4553 women (mean age, 55.7 years ± 6 [SD]) with extremely dense breasts were included; of these women, 122 (3%) were diagnosed with breast cancer. Five principal components representing 96% of the variance were identified, and the component explaining the greatest independent variance (42%) consisted of MRI features relating to volume of enhancing parenchyma. Multivariable analysis showed that volume of enhancing parenchyma was associated with breast cancer occurrence (hazard ratio [HR], 1.09; 95% CI: 1.01, 1.18; P = .02). Additionally, women in the high tertile of volume of enhancing parenchyma showed a breast cancer occurrence twice that of women in the low tertile (HR, 2.09; 95% CI: 1.25, 3.61; P = .005). Conclusion In women with extremely dense breasts, a high volume of enhancing parenchyma on baseline DCE MRI scans was associated with increased occurrence of breast cancer as compared with a low volume of enhancing parenchyma. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Grimm in this issue.

Correlation of background parenchymal enhancement on breast MRI with breast cancer

AIM

To evaluate the prognostic value of background parenchymal enhancement (BPE) in breast magnetic resonance imaging (MRI) in women referred to radiological department as a high risk for breast cancer.

MATERIALS AND METHODS

A retrospective, cross-sectional study included 327 consecutive patients (mean age: 60 years, age range: 30-90 years) who underwent breast MRI and tissue biopsy between 2007 and 2016. All MRI images (T1, T2, and subtraction images) were evaluated visually. The relationship of BPE with patient age, fibroglandular tissue (FGT), Breast Imaging Reporting and Data System (BIRADS) categories, presence of breast cancer, and expression of human epidermal growth factor receptor 2 (HER2), progesterone receptor (PR), oestrogen receptor (ER), and Ki67 were analysed. Furthermore, all variables were correlated with pre- and postmenopausal status.

RESULTS

BPE of bilateral breast showed a weak correlation with FGT (right BPE: r=-0.14, p=0.004; left BPE: r=0.16, p=0.003), a weak negative correlation with patient age (right BPE: r=-0.14, p=0.007; left BPE: r=-0.15, p=0.006), and significant correlation with HER2 (right BPE, p=0.02), left BPE with HER2 was not significant. Among the correlations between BPE and BIRADS, only between right BPE and right BIRADS was significant (p=0.031). No clear evidence of an association between breast MRI BPE and breast cancer in premenopausal and postmenopausal status was observed, and no difference was found between the right and left breasts.

CONCLUSIONS

The results of the present study showed no significant correlations between BPE and breast cancer. In addition, there was no significant difference between the right and left breast. Hence, BPE of MRI may not be a reliable biomarker of breast cancer development.

High-temporal resolution DCE-MRI improves assessment of intra- and peri-breast lesions categorized as BI-RADS 4

BACKGROUND

BI-RADS 4 breast lesions are suspicious for malignancy with a range from 2 to 95%, indicating that numerous benign lesions are unnecessarily biopsied. Thus, we aimed to investigate whether high-temporal-resolution dynamic contrast-enhanced MRI (H_DCE-MRI) would be superior to conventional low-temporal-resolution DCE-MRI (L_DCE-MRI) in the diagnosis of BI-RADS 4 breast lesions.

METHODS

This single-center study was approved by the IRB. From April 2015 to June 2017, patients with breast lesions were prospectively included and randomly assigned to undergo either H_DCE-MRI, including 27 phases, or L_DCE-MRI, including 7 phases. Patients with BI-RADS 4 lesions were diagnosed by the senior radiologist in this study. Using a two-compartment extended Tofts model and a three-dimensional volume of interest, several pharmacokinetic parameters reflecting hemodynamics, including K^trans, K_ep, V_e, and V_p, were obtained from the intralesional, perilesional and background parenchymal enhancement areas, which were labeled the Lesion, Peri and BPE areas, respectively. Models were developed based on hemodynamic parameters, and the performance of these models in discriminating between benign and malignant lesions was evaluated by receiver operating characteristic (ROC) curve analysis.

RESULTS

A total of 140 patients were included in the study and underwent H_DCE-MRI (n = 62) or L_DCE-MRI (n = 78) scans; 56 of these 140 patients had BI-RADS 4 lesions. Some pharmacokinetic parameters from H_DCE-MRI (Lesion_K^trans, K_ep, and V_p; Peri_K^trans, K_ep, and V_p) and from L_DCE-MRI (Lesion_K_ep, Peri_V_p, BPE_K^trans and BPE_V_p) were significantly different between benign and malignant breast lesions (P < 0.01). ROC analysis showed that Lesion_K^trans (AUC = 0.866), Lesion_K_ep (AUC = 0.929), Lesion_V_p (AUC = 0.872), Peri_K^trans (AUC = 0.733), Peri_K_ep (AUC = 0.810), and Peri_V_p (AUC = 0.857) in the H_DCE-MRI group had good discrimination performance. Parameters from the BPE area showed no differentiating ability in the H_DCE-MRI group. Lesion_K_ep (AUC = 0.767), Peri_V_p (AUC = 0.726), and BPE_K^trans and BPE_V_p (AUC = 0.687 and 0.707) could differentiate between benign and malignant breast lesions in the L_DCE-MRI group. The models were compared with the senior radiologist's assessment for the identification of BI-RADS 4 breast lesions. The AUC, sensitivity and specificity of Lesion_K_ep (0.963, 100.0%, and 88.9%, respectively) in the H_DCE-MRI group were significantly higher than those of the same parameter in the L_DCE-MRI group (0.663, 69.6% and 75.0%, respectively) for the assessment of BI-RADS 4 breast lesions. The DeLong test was conducted, and there was a significant difference only between Lesion_K_ep in the H_DCE-MRI group and the senior radiologist (P = 0.04).

CONCLUSIONS

Pharmacokinetic parameters (K^trans, K_ep and V_p) from the intralesional and perilesional regions on high-temporal-resolution DCE-MRI, especially the intralesional K_ep parameter, can improve the assessment of benign and malignant BI-RADS 4 breast lesions to avoid unnecessary biopsy.

Beyond Breast Density: Risk Measures for Breast Cancer in Multiple Imaging Modalities

Breast density is an independent risk factor for breast cancer. In digital mammography and digital breast tomosynthesis, breast density is assessed visually using the four-category scale developed by the American College of Radiology Breast Imaging Reporting and Data System (5th edition as of November 2022). Epidemiologically based risk models, such as the Tyrer-Cuzick model (version 8), demonstrate superior modeling performance when mammographic density is incorporated. Beyond just density, a separate mammographic measure of breast cancer risk is parenchymal textural complexity. With advancements in radiomics and deep learning, mammographic textural patterns can be assessed quantitatively and incorporated into risk models. Other supplemental screening modalities, such as breast US and MRI, offer independent risk measures complementary to those derived from mammography. Breast US allows the two components of fibroglandular tissue (stromal and glandular) to be visualized separately in a manner that is not possible with mammography. A higher glandular component at screening breast US is associated with higher risk. With MRI, a higher background parenchymal enhancement of the fibroglandular tissue has also emerged as an imaging marker for risk assessment. Imaging markers observed at mammography, US, and MRI are powerful tools in refining breast cancer risk prediction, beyond mammographic density alone.

Obesity and metabolic dysfunction correlate with background parenchymal enhancement in premenopausal women

OBJECTIVE

This study tested the hypothesis that obesity and metabolic abnormalities correlate with background parenchymal enhancement (BPE), the volume and intensity of enhancing fibroglandular breast tissue on dynamic contrast-enhanced magnetic resonance imaging.

METHODS

Participants included 59 premenopausal women at high risk of breast cancer. Obesity was defined as BMI ≥ 30 kg/m2 . Metabolic parameters included dual-energy x-ray absorptiometry-quantified body composition, plasma biomarkers of insulin resistance, adipokines, inflammation, lipids, and urinary sex hormones. BPE was assessed using computerized algorithms on dynamic contrast-enhanced magnetic resonance imaging.

RESULTS

BMI was positively correlated with BPE (r = 0.69; p < 0.001); participants with obesity had higher BPE than those without obesity (404.9 ± 189.6 vs. 261.8 ± 143.8 cm2 ; Δ: 143.1 cm2 [95% CI: 49.5-236.7]; p = 0.003). Total body fat mass (r = 0.68; p < 0.001), body fat percentage (r = 0.64; p < 0.001), visceral adipose tissue area (r = 0.65; p < 0.001), subcutaneous adipose tissue area (r = 0.60; p < 0.001), insulin (r = 0.59; p < 0.001), glucose (r = 0.35; p = 0.011), homeostatic model of insulin resistance (r = 0.62; p < 0.001), and leptin (r = 0.60; p < 0.001) were positively correlated with BPE. Adiponectin (r = -0.44; p < 0.001) was negatively correlated with BPE. Plasma biomarkers of inflammation and lipids and urinary sex hormones were not correlated with BPE.

CONCLUSIONS

In premenopausal women at high risk of breast cancer, increased BPE is associated with obesity, insulin resistance, leptin, and adiponectin.

Background Parenchymal Enhancement at Postoperative Surveillance Breast MRI: Association with Future Second Breast Cancer Risk

Background Background parenchymal enhancement (BPE) is a known risk factor for breast cancer. However, studies on the association between BPE and second breast cancer risk are still lacking. Purpose To investigate whether BPE at surveillance breast MRI is associated with subsequent second breast cancer risk in women with a personal history of breast cancer. Materials and Methods A retrospective search of the imaging database of an academic medical center identified consecutive surveillance breast MRI examinations performed between January 2008 and December 2017 in women who underwent surgery for primary breast cancer and had no prior diagnosis of second breast cancer. BPE at surveillance breast MRI was qualitatively assessed using a four-category classification of minimal, mild, moderate, or marked. Future second breast cancer was defined as ipsilateral breast tumor recurrence or contralateral breast cancer diagnosed at least 1 year after each surveillance breast MRI examination. Factors associated with future second breast cancer risk were evaluated using the multivariable Fine-Gray subdistribution hazard model. Results Among the 2668 women (mean age at baseline surveillance breast MRI, 49 years ± 8 [SD]), 109 developed a second breast cancer (49 ipsilateral, 58 contralateral, and two ipsilateral and contralateral) at a median follow-up of 5.8 years. Mild, moderate, or marked BPE at surveillance breast MRI (hazard ratio [HR], 2.1 [95% CI: 1.4, 3.1]; P < .001), young age (<45 years) at initial breast cancer diagnosis (HR, 3.4 [95% CI: 1.7, 6.4]; P < .001), positive results from a BRCA1/2 genetic test (HR, 6.5 [95% CI: 3.5, 12.0]; P < .001), and negative hormone receptor expression in the initial breast cancer (HR, 1.6 [95% CI: 1.1, 2.6]; P = .02) were independently associated with an increased risk of future second breast cancer. Conclusion Background parenchymal enhancement at surveillance breast MRI was associated with future second breast cancer risk in women with a personal history of breast cancer. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Niell in this issue.

Identifying Good Candidates for Active Surveillance of Ductal Carcinoma In Situ: Insights from a Large Neoadjuvant Endocrine Therapy Cohort

Ductal carcinoma in situ (DCIS) is a biologically heterogenous entity with uncertain risk for invasive ductal carcinoma (IDC) development. Standard treatment is surgical resection often followed by radiation. New approaches are needed to reduce overtreatment. This was an observational study that enrolled patients with DCIS who chose not to pursue surgical resection from 2002 to 2019 at a single academic medical center. All patients underwent breast MRI exams at 3- to 6-month intervals. Patients with hormone receptor-positive disease received endocrine therapy. Surgical resection was strongly recommended if clinical or radiographic evidence of disease progression developed. A recursive partitioning (R-PART) algorithm incorporating breast MRI features and endocrine responsiveness was used retrospectively to stratify risk of IDC. A total of 71 patients were enrolled, 2 with bilateral DCIS (73 lesions). A total of 34 (46.6%) were premenopausal, 68 (93.2%) were hormone-receptor positive, and 60 (82.1%) were intermediate- or high-grade lesions. Mean follow-up time was 8.5 years. Over half (52.1%) remained on active surveillance without evidence of IDC with mean duration of 7.4 years. Twenty patients developed IDC, of which 6 were HER2 positive. DCIS and subsequent IDC had highly concordant tumor biology. Risk of IDC was characterized by MRI features after 6 months of endocrine therapy exposure; low-, intermediate-, and high-risk groups were identified with respective IDC rates of 8.7%, 20.0%, and 68.2%. Thus, active surveillance consisting of neoadjuvant endocrine therapy and serial breast MRI may be an effective tool to risk-stratify patients with DCIS and optimally select medical or surgical management.

Significance

A retrospective analysis of 71 patients with DCIS who did not undergo upfront surgery demonstrated that breast MRI features after short-term exposure to endocrine therapy identify those at high (68.2%), intermediate (20.0%), and low risk (8.7%) of IDC. With 7.4 years mean follow-up, 52.1% of patients remain on active surveillance. A period of active surveillance offers the opportunity to risk-stratify DCIS lesions and guide decisions for operative management.

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.

MRI background parenchymal enhancement, breast density and breast cancer risk factors: A cross-sectional study in pre- and post-menopausal women

Breast tissue enhances on contrast MRI and is called background parenchymal enhancement (BPE). Having high BPE has been associated with an increased risk of breast cancer. We examined the relationship between BPE and the amount of fibroglandular tissue on MRI (MRI-FGT) and breast cancer risk factors. This was a cross-sectional study of 415 women without breast cancer undergoing contrast-enhanced breast MRI at Memorial Sloan Kettering Cancer Center. All women completed a questionnaire assessing exposures at the time of MRI. Prevalence ratios (PR) and 95% confidence intervals (CI) describing the relationship between breast cancer risk factors and BPE and MRI-FGT were generated using modified Poisson regression. In multivariable-adjusted models a positive association between body mass index (BMI) and BPE was observed, with a 5-unit increase in BMI associated with a 14% and 44% increase in prevalence of high BPE in pre- and post-menopausal women, respectively. Conversely, a strong inverse relationship between BMI and MRI-FGT was observed in both pre- (PR = 0.66, 95% CI 0.57, 0.76) and post-menopausal (PR = 0.66, 95% CI 0.56, 0.78) women. Use of preventive medication (e.g., tamoxifen) was associated with having low BPE, while no association was observed for MRI-FGT. BPE is an imaging marker available from standard contrast-enhanced MRI, that is influenced by endogenous and exogenous hormonal exposures in both pre- and post-menopausal women.

Comparison of background parenchymal enhancement (BPE) on contrast-enhanced cone-beam breast CT (CE-CBBCT) and breast MRI

OBJECTIVES

To compare the background parenchymal enhancement (BPE) levels on contrast-enhanced cone-beam breast CT (CE-CBBCT) and MRI, evaluate inter-reader reliability, and analyze the relationship between clinical factors and BPE level on CE-CBBCT.

METHODS

In this retrospective study, patients who underwent both CE-CBBCT and MRI were analyzed. BPE levels on CE-CBBCT and MRI were assessed by five specialists independently in random fashion, with a wash-out period of 4 weeks. Weighted kappa was used to analyze the agreement between CE-CBBCT and MRI, and intraclass correlation coefficient (ICC) was used to evaluate the inter-reader reliability for each modality. The association between BPE level on CE-CBBCT and clinical factors was evaluated by univariate and multivariate logistic regression.

RESULTS

A total of 221 patients from January 2017 to April 2021 were enrolled. CE-CBBCT showed substantial agreement (weighted kappa = 0.690) with MRI for BPE evaluation, with good degree of inter-reader reliability on both CE-CBBCT (ICC = 0.712) and MRI (ICC = 0.757). Based on majority reports, BPE levels on CE-CBBCT were lower than MRI (p < 0.001). BPE level on CE-CBBCT was significantly associated with menstrual status (odds ratio, OR = 0.125), breast density (OR = 2.308), and previously treated breast cancer (OR = 0.052) (all p < 0.05). BPE level for premenopausal patients was associated with menstrual cycle, with lower BPE level for the 2^nd week of menstrual cycle (OR = 0.246).

CONCLUSIONS

CE-CBBCT showed substantial agreement and comparable inter-reader reliability with MRI for BPE evaluation, indicating that the corresponding BI-RADS lexicons could be used to describe BPE level on CE-CBBCT. The 2^nd week of menstrual cycle timing is suggested as the optimal examination period for CE-CBBCT.

KEY POINTS

• CE-CBBCT showed substantial agreement and comparable inter-reader reliability with MRI for BPE evaluation. • Menstrual status, breast density, and previously treated breast cancer were associated with the BPE level on CE-CBBCT images. • The 2^ndweek of the menstrual cycle is suggested as the optimal examination period for CE-CBBCT.

Ipsilateral Recurrence of DCIS in Relation to Radiomics Features on Contrast Enhanced Breast MRI

The purpose of this retrospective study was to investigate the association between ipsilateral recurrence of ductal carcinoma in situ (DCIS) and radiomics features from DCIS and contralateral normal breast on contrast enhanced breast MR imaging. A total of 163 patients with DCIS who underwent preoperative MR imaging between January 2010 and December 2014 were included (training cohort; n = 117, validation cohort; n = 46). Radiomics features were extracted from whole tumor volume of DCIS on early dynamic T1-subtraction images and from the contralateral normal breast on precontrast T1 and early dynamic T1-subtraction images. After feature selection, a Rad-score was established by LASSO Cox regression model. Performance of Rad-score was evaluated by the receiver operating characteristic (ROC) curve and Kaplan Meier curve with log rank test. The Rad-score was significantly associated with ipsilateral recurrence free survival (RFS). The low-risk group with a low Rad-score showed higher ipsilateral RFS than the high-risk group with a high Rad-score in both training and validation cohorts (p < 0.01). The Rad-score based on radiomics features from DCIS and contralateral normal breast on breast MR imaging showed the potential for prediction of ipsilateral RFS of DCIS.

[Non-genetic indications for risk reducing mastectomies: Guidelines of the National College of French Gynecologists and Obstetricians (CNGOF)]

OBJECTIVE

To determine the value of performing a risk-reducting mastectomy (RRM) in the absence of a deleterious variant of a breast cancer susceptibility gene, in 4 clinical situations at risk of breast cancer.

DESIGN

The CNGOF Commission of Senology, composed of 26 experts, developed these recommendations. A policy of declaration and monitoring of links of interest was applied throughout the process of making the recommendations. Similarly, the development of these recommendations did not benefit from any funding from a company marketing a health product. The Commission of Senology adhered to the AGREE II (Advancing guideline development, reporting and evaluation in healthcare) criteria and followed the Grading of Recommendations Assessment, Development and Evaluation (GRADE) method to assess the quality of the evidence on which the recommendations were based. The potential drawbacks of making recommendations in the presence of poor quality or insufficient evidence were highlighted.

METHODS

The Commission of Senology considered 8 questions on 4 topics, focusing on histological, familial (no identified genetic abnormality), radiological (of unrecognized cancer), and radiation (history of Hodgkin's disease) risk. For each situation, it was determined whether performing RRM compared with surveillance would decrease the risk of developing breast cancer and/or increase survival.

RESULTS

The Commission of Senology synthesis and application of the GRADE method resulted in 11 recommendations, 6 with a high level of evidence (GRADE 1±) and 5 with a low level of evidence (GRADE 2±).

CONCLUSION

There was significant agreement among the Commission of Senology members on recommendations to improve practice for performing or not performing RRM in the clinical setting.

Background parenchymal enhancement in contrast-enhanced MR imaging suggests systemic effects of intrauterine contraceptive devices

OBJECTIVES

Levonorgestrel-releasing intrauterine contraceptive devices (LNG-IUDs) are designed to exhibit only local hormonal effects. There is an ongoing debate on whether LNG-IUDs can have side effects similar to systemic hormonal medication. Benign background parenchymal enhancement (BPE) in dynamic contrast-enhanced (DCE) MRI has been established as a sensitive marker of hormonal stimulation of the breast. We investigated the association between LNG-IUD use and BPE in breast MRI to further explore possible systemic effects of LNG-IUDs.

METHODS

Our hospital database was searched to identify premenopausal women without personal history of breast cancer, oophorectomy, and hormone replacement or antihormone therapy, who had undergone standardized DCE breast MRI at least twice, once with and without an LNG-IUD in place. To avoid confounding aging-related effects on BPE, half of included women had their first MRI without, the other half with, LNG-IUD in place. Degree of BPE was analyzed according to the ACR categories. Wilcoxon-matched-pairs signed-rank test was used to compare the distribution of ACR categories with vs. without LNG-IUD.

RESULTS

Forty-eight women (mean age, 46 years) were included. In 24/48 women (50% [95% CI: 35.9-64.1%]), ACR categories did not change with vs. without LNG-IUDs. In 23/48 women (48% [33.9-62.1%]), the ACR category was higher with vs. without LNG-IUDs; in 1/48 (2% [0-6%]), the ACR category was lower with vs. without LNG-IUDs. The change of ACR category depending on the presence or absence of an LNG-IUD proved highly significant (p < 0.001).

CONCLUSION

The use of an LNG-IUD can be associated with increased BPE in breast MRI, providing further evidence that LNG-IUDs do have systemic effects.

KEY POINTS

• The use of levonorgestrel-releasing intrauterine contraceptive devices is associated with increased background parenchymal enhancement in breast MRI. • This suggests that hormonal effects of these devices are not only confined to the uterine cavity, but may be systemic. • Potential systemic effects of levonorgestrel-releasing intrauterine contraceptive devices should therefore be considered.

Current and Future Directions of Breast MRI

Magnetic resonance imaging (MRI) is the most sensitive exam for detecting breast cancer. The American College of Radiology recommends women with 20% or greater lifetime risk of developing breast cancer be screened annually with MRI. However, other high-risk populations would also benefit. Hartmann et al. reported women with atypical hyperplasia have nearly a 30% incidence of breast cancer at 25-year follow-up. Women with dense breast tissue have up to a 4-fold increased risk of breast cancer when compared to average-risk women; their cancers are more likely to be mammographically occult. Because multiple cohorts of women are at high risk for developing breast cancer, there has been a movement to develop an abbreviated MRI (abMRI) protocol to expand the availability of MRI screening. Studies on abMRI effectiveness have been promising, with Weinstein et al. demonstrating a cancer detection rate of 27.4/1000 in women with dense breasts after a negative digital breast tomosynthesis. Breast MRI is also used to evaluate the extent of disease as part of preoperative assessment in women with newly diagnosed breast cancer, and to assess a patient's response to neoadjuvant chemotherapy. This paper aims to explore the current uses of MRI and propose future indications and directions.

Contralateral parenchymal enhancement on MRI is associated with tumor proteasome pathway gene expression and overall survival of early ER+/HER2-breast cancer patients

Purpose

To assess whether contralateral parenchymal enhancement (CPE) on MRI is associated with gene expression pathways in ER+/HER2-breast cancer, and if so, whether such pathways are related to survival.

Methods

Preoperative breast MRIs were analyzed of early ER+/HER2-breast cancer patients eligible for breast-conserving surgery included in a prospective observational cohort study (MARGINS). The contralateral parenchyma was segmented and CPE was calculated as the average of the top-10% delayed enhancement. Total tumor RNA sequencing was performed and gene set enrichment analysis was used to reveal gene expression pathways associated with CPE (N = 226) and related to overall survival (OS) and invasive disease-free survival (IDFS) in multivariable survival analysis. The latter was also done for the METABRIC cohort (N = 1355).

Results

CPE was most strongly correlated with proteasome pathways (normalized enrichment statistic = 2.04, false discovery rate = .11). Patients with high CPE showed lower tumor proteasome gene expression. Proteasome gene expression had a hazard ratio (HR) of 1.40 (95% CI = 0.89, 2.16; P = .143) for OS in the MARGINS cohort and 1.53 (95% CI = 1.08, 2.14; P = .017) for IDFS, in METABRIC proteasome gene expression had an HR of 1.09 (95% CI = 1.01, 1.18; P = .020) for OS and 1.10 (95% CI = 1.02, 1.18; P = .012) for IDFS.

Conclusion

CPE was negatively correlated with tumor proteasome gene expression in early ER+/HER2-breast cancer patients. Low tumor proteasome gene expression was associated with improved survival in the METABRIC data.

•

Contralateral parenchymal enhancement on MRI was associated with tumor proteasome gene expression in ER+/HER2-breast cancer.

•

A high contralateral parenchymal enhancement was associated with a low proteasome gene expression in the breast cancer.

•

Low proteasome tumor gene expression was associated with improved survival in an independent patient cohort.

Clinical Artificial Intelligence Applications: Breast Imaging

This article gives a brief overview of the development of artificial intelligence in clinical breast imaging. For multiple decades, artificial intelligence (AI) methods have been developed and translated for breast imaging tasks such as detection, diagnosis, and assessing response to therapy. As imaging modalities arise to support breast cancer screening programs and diagnostic examinations, including full-field digital mammography, breast tomosynthesis, ultrasound, and MRI, AI techniques parallel the efforts with more complex algorithms, faster computers, and larger data sets. AI methods include human-engineered radiomics algorithms and deep learning methods. Examples of these AI-supported clinical tasks are given along with commentary on the future.

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.

Contrast-Enhanced Spectral Mammography Assessment of Patients Treated with Neoadjuvant Chemotherapy for Breast Cancer

Background: Evaluating the tumor response to neoadjuvant chemotherapy is key to planning further therapy of breast cancer. Our study aimed to evaluate the effectiveness of low-energy and subtraction contrast-enhanced spectral mammography (CESM) images in the detection of complete response (CR) for neoadjuvant chemotherapy (NAC) in breast cancer. Methods: A total of 63 female patients were qualified for our retrospective analysis. Low-energy and subtraction CESM images just before the beginning of NAC and as a follow-up examination 2 weeks before the end of chemotherapy were compared with one another and assessed for compliance with the postoperative histopathological examination (HP). The response to preoperative chemotherapy was evaluated based on the RECIST 1.1 criteria (Response Evaluation Criteria in Solid Tumors). Results: Low-energy images tend to overestimate residual lesions (6.28 mm) and subtraction images tend to underestimate them (2.75 mm). The sensitivity of low-energy images in forecasting CR amounted to 33.33%, while the specificity was 92.86%. In the case of subtraction CESM, the sensitivity amounted to 85.71% and the specificity to 71.42%. Conclusions: CESM is characterized by high sensitivity in the assessment of CR after NAC. The use of only morphological assessment is insufficient. CESM correlates well with the size of residual lesions on histopathological examination but tends to underestimate the dimensions.

Screening Breast MRI and the Science of Premenopausal Background Parenchymal Enhancement

The significance of background parenchymal enhancement (BPE) on screening and diagnostic breast MRI continues to be elucidated. Background parenchymal enhancement was initially deemed probably benign and followed or thought of as an artifact degrading the accuracy of breast cancer detection on breast MRI examinations. Subsequent research has focused on understanding the role of BPE regarding screening breast MRI. Today, there is growing evidence that a myriad of factors affect BPE, which in turn may influence patient outcomes. Additionally, BPE could represent an important risk factor for the future development of breast cancer. This article aims to describe the most up-to-date research on BPE as it relates to screening breast MRI in premenopausal women.

Glandular Tissue Component and Breast Cancer Risk in Mammographically Dense Breasts at Screening Breast US

Background Breast density at mammography is an established risk factor for breast cancer, but it cannot be used to distinguish between glandular and fibrous tissue. Purpose To evaluate the association between the glandular tissue component (GTC) at screening breast US and the risk of future breast cancer in women with dense breasts and the association between the GTC and lobular involution. Materials and Methods Screening breast US examinations performed in women with no prior history of breast cancer and with dense breasts with negative findings from mammography from January 2012 to December 2015 were retrospectively identified. The GTC was reported as being minimal, mild, moderate, or marked at the time of the US examination. In women who had benign breast biopsy results, the degree of lobular involution in normal background tissue was categorized as not present, mild, moderate, or complete. The GTC-related breast cancer risk in women with a cancer diagnosis or follow-up after 6 months was estimated by using Cox proportional hazards regression. Cumulative logistic regression was used to evaluate the association between the GTC and lobular involution. Results Among 8483 women (mean age, 49 years ± 8 [standard deviation]), 137 developed breast cancer over a median follow-up time of 5.3 years. Compared with a minimal or mild GTC, a moderate or marked GTC was associated with an increased cancer risk (hazard ratio, 1.5; 95% CI: 1.05, 2.1; P = .03) after adjusting for age and breast density. The GTC had an inverse association with lobular involution; women with no, mild, or moderate involution had greater odds (odds ratios of 4.9 [95% CI: 1.5, 16.6], 2.6 [95% CI: 0.95, 7.2], and 1.8 [95% CI: 0.7, 4.6], respectively) of a moderate or marked GTC than those with complete involution (P = .004). Conclusion The glandular tissue component was independently associated with the future breast cancer risk in women with dense breasts and reflects the lobular involution. It should be considered for risk stratification during screening breast US. © RSNA, 2021 Online supplemental material is available for this article.

Early prediction of neoadjuvant chemotherapy response by exploiting a transfer learning approach on breast DCE-MRIs

The dynamic contrast-enhanced MR imaging plays a crucial role in evaluating the effectiveness of neoadjuvant chemotherapy (NAC) even since its early stage through the prediction of the final pathological complete response (pCR). In this study, we proposed a transfer learning approach to predict if a patient achieved pCR (pCR) or did not (non-pCR) by exploiting, separately or in combination, pre-treatment and early-treatment exams from I-SPY1 TRIAL public database. First, low-level features, i.e., related to local structure of the image, were automatically extracted by a pre-trained convolutional neural network (CNN) overcoming manual feature extraction. Next, an optimal set of most stable features was detected and then used to design an SVM classifier. A first subset of patients, called fine-tuning dataset (30 pCR; 78 non-pCR), was used to perform the optimal choice of features. A second subset not involved in the feature selection process was employed as an independent test (7 pCR; 19 non-pCR) to validate the model. By combining the optimal features extracted from both pre-treatment and early-treatment exams with some clinical features, i.e., ER, PgR, HER2 and molecular subtype, an accuracy of 91.4% and 92.3%, and an AUC value of 0.93 and 0.90, were returned on the fine-tuning dataset and the independent test, respectively. Overall, the low-level CNN features have an important role in the early evaluation of the NAC efficacy by predicting pCR. The proposed model represents a first effort towards the development of a clinical support tool for an early prediction of pCR to NAC.

3,3-Diindolylmethane (DIM): a nutritional intervention and its impact on breast density in healthy BRCA carriers. A prospective clinical trial

Women who carry the BRCA mutation are at high lifetime risk of breast cancer, but there is no consensus regarding an effective and safe chemoprevention strategy. A large body of evidence suggests that 3,3-diindolylmethane (DIM), a dimer of indole-3-carbinol found in cruciferous vegetables, can potentially prevent carcinogenesis and tumor development. The primary aim of this prospective single-arm study was to investigate the effect of DIM supplementation on breast density, a recognized predictive factor of breast cancer risk. Participants were 23 healthy female BRCA carriers (median age 47 years; 78% postmenopausal) who were treated with oral DIM 100 mg × 1/day for 1 year. The amount of fibroglandular tissue (FGT) and background parenchymal enhancement (BPE) on magnetic resonance imaging (MRI) performed before and after the intervention was scored by two independent expert radiologists using the Breast Imaging and Reporting Data System. The results showed a decrease in the average score for FGT amount from 2.8 ± 0.8 at the onset to 2.65 ± 0.84 after 1 year (P = 0.031), with no significant change in BPE (P = 0.429). A group of DIM-untreated age- and menopausal-status-matched women from the BRCA clinic did not show a significant change in FGT amount (P = 0.33) or BPE (P = 0.814) in a parallel year. Mean estradiol level decreased from 159 to 102 pmol/l (P = 0.01), and mean testosterone level decreased from 0.42 to 0.31 pmol/l (P = 0.007). Side effects were grade 1. In conclusion, 1 year’s supplementation with DIM 100 mg × 1/day in BRCA carriers was associated with a significant decline in FGT amount on MRI. Larger randomized studies are warranted to corroborate these findings.

Quantitative Measures of Background Parenchymal Enhancement Predict Breast Cancer Risk

BACKGROUND. Higher categories of background parenchymal enhancement (BPE) increase breast cancer risk. However, current clinical BPE categorization is subjective. OBJECTIVE. Using a semiautomated segmentation algorithm, we calculated quantitative BPE measures and investigated the utility of individual features and feature pairs in significantly predicting subsequent breast cancer risk compared with radiologist-assigned BPE category. METHODS. In this retrospective case-control study, we identified 95 women at high risk of breast cancer but without a personal history of breast cancer who underwent breast MRI. Of these women, 19 subsequently developed breast cancer and were included as cases. Each case was age matched to four control patients (76 control patients total). Sociodemographic characteristics were compared between the cases and matched control patients using the Mann-Whitney U test. From each dynamic contrast-enhanced MRI examination, quantitative fibroglandular tissue and BPE measures were computed by averaging enhancing voxels above enhancement ratio thresholds (0-100%), totaling the enhancing volume above thresholds (BPE volume in cm³), and estimating the percentage of enhancing tissue above thresholds relative to total breast volume (BPE%) on each gadolinium-enhanced phase. For the 91 imaging features generated, we compared predictive performance using conditional logistic regression with 80:20 hold-out cross validation and ROC curve analysis. ROC AUC was the figure of merit. Sensitivity, specificity, PPV, and NPV were also computed. All feature pairs were exhaustively searched to identify those with the highest AUC and Youden index. A DeLong test was used to compare predictive performance (AUCs). RESULTS. Women subsequently diagnosed with breast cancer were more likely to have mild, moderate, or marked BPE (odds ratio, 3.0; 95% CI, 0.9-10.0; p = .07). According to ROC curve analysis, a BPE category threshold greater than minimal resulted in a maximized AUC (0.62) in distinguishing cases from control patients. Compared with BPE category, the first gadolinium-enhanced (phase 1) BPE% at the 30% and 40% enhancement ratio thresholds yielded significantly higher AUC values of 0.85 (p = .0007) and 0.84 (p = .0004), respectively. Feature combinations showed similar AUC values with improved sensitivity. CONCLUSION. Preliminary data indicate that quantitative BPE measures may outperform radiologist-assigned category in breast cancer risk prediction. CLINICAL IMPACT. Future risk prediction models that incorporate quantitative measures warrant additional investigation.

Comparison of Segmentation Methods in Assessing Background Parenchymal Enhancement as a Biomarker for Response to Neoadjuvant Therapy

Breast parenchymal enhancement (BPE) has shown association with breast cancer risk and response to neoadjuvant treatment. However, BPE quantification is challenging, and there is no standardized segmentation method for measurement. We investigated the use of a fully automated breast fibroglandular tissue segmentation method to calculate BPE from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for use as a predictor of pathologic complete response (pCR) following neoadjuvant treatment in the I-SPY 2 TRIAL. In this trial, patients had DCE-MRI at baseline (T0), after 3 weeks of treatment (T1), after 12 weeks of treatment and between drug regimens (T2), and after completion of treatment (T3). A retrospective analysis of 2 cohorts was performed: one with 735 patients and another with a final cohort of 340 patients, meeting a high-quality benchmark for segmentation. We evaluated 3 subvolumes of interest segmented from bilateral T1-weighted axial breast DCE-MRI: full stack (all axial slices), half stack (center 50% of slices), and center 5 slices. The differences between methods were assessed, and a univariate logistic regression model was implemented to determine the predictive performance of each segmentation method. The results showed that the half stack method provided the best compromise between sampling error from too little tissue and inclusion of incorrectly segmented tissues from extreme superior and inferior regions. Our results indicate that BPE calculated using the half stack segmentation approach has potential as an early biomarker for response to treatment in the hormone receptor–negative and human epidermal growth factor receptor 2–positive subtype.

Mini review: The FDA-approved prescription drugs that target the MAPK signaling pathway in women with breast cancer

Breast cancer (BC) is the most common cancer and the prevalent type of malignancy among women. Multiple risk factors, including genetic changes, biological age, dense breast tissue, and obesity are associated with BC. The mitogen-activated protein kinases (MAPK) signaling pathway has a pivotal role in regulating biological functions such as cell proliferation, differentiation, apoptosis, and survival. It has become evident that the MAPK pathway is associated with tumorigenesis and may promote breast cancer development. The MAPK/RAS/RAF cascade is closely associated with breast cancer. RAS signaling can enhance BC cell growth and progression. B-Raf is an important kinase and a potent RAF isoform involved in breast tumor initiation and differentiation. Depending on the reasons for cancer, there are different strategies for treatment of women with BC. Till now, several FDA-approved treatments have been investigated that inhibit the MAPK pathway and reduce metastatic progression in breast cancer. The most common breast cancer drugs that regulate or inhibit the MAPK pathway may include Farnesyltransferase inhibitors (FTIs), Sorafenib, Vemurafenib, PLX8394, Dabrafenib, Ulixertinib, Simvastatin, Alisertib, and Teriflunomide. In this review, we will discuss the roles of the MAPK/RAS/RAF/MEK/ERK pathway in BC and summarize the FDA-approved prescription drugs that target the MAPK signaling pathway in women with BC.

Interval Cancers after Negative Supplemental Screening Breast MRI Results in Women with a Personal History of Breast Cancer

Background There are few interval cancer studies of incident screening MRI for women with a personal history of breast cancer (PHBC). Purpose To evaluate the performance measures of screening breast MRI in women with a PHBC across multiple rounds and to identify subgroups who might be more at risk for interval cancer. Materials and Methods Between January 2008 and March 2019, consecutive screening breast MRI studies for women who had undergone breast-conserving surgery because of breast cancer were retrospectively identified. Inclusion criteria were negative or benign findings at mammography with US, availability of at least 1 year of follow-up data, and examinations having been performed within 12 months after the initial cancer surgery. Performance measures were calculated for each round. Multivariable logistic regression analysis was performed to determine factors associated with the risk of interval cancer. Results Among the 6603 MRI examinations for 2809 women (median age, 47 years; interquartile range, 42-53 years), the cancer detection rate was 8.3 per 1000 screening examinations (55 of 6603 examinations) and the interval cancer rate was 1.5 per 1000 screening examinations (10 of 6603 examinations). The sensitivity and specificity were 85% (55 of 65 examinations; 95% CI: 76, 93) and 88.3% (5775 of 6538 examinations; 95% CI: 87.6, 89.1), respectively. At multivariable analysis, interval cancers were associated with a first-degree family history of breast cancer (odds ratio [OR], 5.4; 95% CI: 1.3, 22.5; P = .02), estrogen receptor- and progesterone receptor-negative primary cancers (OR, 3.6; 95% CI: 1.1, 12.2; P = .04), and moderate or marked background parenchymal enhancement (OR, 10.8; 95% CI: 3.3, 35.7; P < .001). Conclusion Performance of screening breast MRI in women with a personal history of breast cancer was sustained across multiple rounds, and a first-degree family history of breast cancer, estrogen receptor- and progesterone receptor-negative primary cancers, and moderate or marked background parenchymal enhancement at MRI were independently associated with the risk of developing interval cancers. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Slanetz in this issue.

Response Predictivity to Neoadjuvant Therapies in Breast Cancer: A Qualitative Analysis of Background Parenchymal Enhancement in DCE-MRI

Background: For assessing the predictability of oncology neoadjuvant therapy results, the background parenchymal enhancement (BPE) parameter in breast magnetic resonance imaging (MRI) has acquired increased interest. This work aims to qualitatively evaluate the BPE parameter as a potential predictive marker for neoadjuvant therapy. Method: Three radiologists examined, in triple-blind modality, the MRIs of 80 patients performed before the start of chemotherapy, after three months from the start of treatment, and after surgery. They identified the portion of fibroglandular tissue (FGT) and BPE of the contralateral breast to the tumor in the basal control pre-treatment (baseline). Results: We observed a reduction of BPE classes in serial MRI checks performed during neoadjuvant therapy, as compared to baseline pre-treatment conditions, in 61.3% of patients in the intermediate step, and in 86.7% of patients in the final step. BPE reduction was significantly associated with sequential anthracyclines/taxane administration in the first cycle of neoadjuvant therapy compared to anti-HER2 containing therapies. The therapy response was also significantly related to tumor size. There were no associations with menopausal status, fibroglandular tissue (FGT) amount, age, BPE baseline, BPE in intermediate, and in the final MRI step. Conclusions: The measured variability of this parameter during therapy could predict therapy effectiveness in early stages, improving decision-making in the perspective of personalized medicine. Our preliminary results suggest that BPE may represent a predictive factor in response to neoadjuvant therapy in breast cancer, warranting future investigations in conjunction with radiomics.

Breast cancer and background parenchymal enhancement at breast magnetic resonance imaging: a meta-analysis

Background

The background parenchymal enhancement at breast magnetic resonance imaging use to predict breast cancer attracts many searchers to draw a possible relationship. However, the results of their relationships were conflicting. This meta-analysis was performed to assess breast cancer frequency associations with background parenchymal enhancement.

Methods

A systematic literature search up to January 2020 was performed to detect studies recording associations between breast cancer frequency and background parenchymal enhancement. We found thirteen studies including 13,788 women at the start with 4046 breast cancer. We calculated the odds ratio (OR) and the 95% confidence intervals (CIs) between breast cancer frequency and background parenchymal enhancement by the dichotomous technique with a random or fixed-effect model.

Results

Women with minimal or mild background parenchymal enhancement at breast magnetic resonance imaging did not have any risk of breast cancer compared to control women (OR, 1.20; 95% CI 0.54–2.67). However, high background parenchymal enhancement at breast magnetic resonance imaging (OR, 2.66; 95% CI 1.36–5.19) and moderate (OR, 2.51; 95% CI 1.49–4.21) was associated with a significantly higher rate of breast cancer frequency compared to control women.

Conclusions

Our meta-analysis showed that the women with high and moderate background parenchymal enhancement at breast magnetic resonance imaging have higher risks, up to 2.66 fold, of breast cancer. We suggest that women with high or moderate background parenchymal enhancement at breast magnetic resonance imaging to be scheduled for more frequent follow-up and screening for breast cancer to avoid any complications.

Association of breast cancer with MRI background parenchymal enhancement: the IMAGINE case-control study

Background

Background parenchymal enhancement (BPE) on breast magnetic resonance imaging (MRI) may be associated with breast cancer risk, but previous studies of the association are equivocal and limited by incomplete blinding of BPE assessment. In this study, we evaluated the association between BPE and breast cancer based on fully blinded assessments of BPE in the unaffected breast.

Methods

The Imaging and Epidemiology (IMAGINE) study is a multicenter breast cancer case-control study of women receiving diagnostic, screening, or follow-up breast MRI, recruited from three comprehensive cancer centers in the USA. Cases had a first diagnosis of unilateral breast cancer and controls had no history of or current breast cancer. A single board-certified breast radiologist with 12 years’ experience, blinded to case-control status and clinical information, assessed the unaffected breast for BPE without view of the affected breast of cases (or the corresponding breast laterality of controls). The association between BPE and breast cancer was estimated by multivariable logistic regression separately for premenopausal and postmenopausal women.

Results

The analytic dataset included 835 cases and 963 controls. Adjusting for fibroglandular tissue (breast density), age, race/ethnicity, BMI, parity, family history of breast cancer, BRCA1/BRCA2 mutations, and other confounders, moderate/marked BPE (vs minimal/mild BPE) was associated with breast cancer among premenopausal women [odds ratio (OR) 1.49, 95% CI 1.05–2.11; p = 0.02]. Among postmenopausal women, mild/moderate/marked vs minimal BPE had a similar, but statistically non-significant, association with breast cancer (OR 1.45, 95% CI 0.92–2.27; p = 0.1).

Conclusions

BPE is associated with breast cancer in premenopausal women, and possibly postmenopausal women, after adjustment for breast density and confounders. Our results suggest that BPE should be evaluated alongside breast density for inclusion in models predicting breast cancer risk.

The association between breast cancer risk factors and background parenchymal enhancement at dynamic contrast-enhanced breast MRI

Background

Background parenchymal enhancement (BPE) of normal tissue at breast magnetic resonance imaging is suggested to be an independent risk factor for breast cancer. Its association with established risk factors for breast cancer is not fully investigated.

Purpose

To study the association between BPE and risk factors for breast cancer in a healthy, non-high-risk screening population.

Material and Methods

We measured BPE and mammographic density and used data from self-reported questionnaires in 214 healthy women aged 43–74 years. We estimated odds ratios for the univariable association between BPE and risk factors. We then fitted an adjusted model using logistic regression to evaluate associations between BPE (high vs. low) and risk factors, including mammographic breast density.

Results

The majority of women had low BPE (84%). In a multivariable model, we found statistically significant associations between BPE and age (P = 0.002) and BMI (P = 0.03). We did find a significant association between systemic progesterone medication and BPE, but due to small numbers, the results should be interpreted with caution. The adjusted odds ratio for high BPE was 3.1 among women with density D (compared to B) and 2.1 for density C (compared to B). However, the association between high BPE and density was not statistically significant. We did not find statistically significant associations with any other risk factors.

Conclusion

Our study confirmed the known association of BPE with age and BMI. Although our results show a higher likelihood for high BPE with increasing levels of mammographic density, the association was not statistically significant.

Predicting breast cancer response to neoadjuvant treatment using multi-feature MRI: results from the I-SPY 2 TRIAL

Dynamic contrast-enhanced (DCE) MRI provides both morphological and functional information regarding breast tumor response to neoadjuvant chemotherapy (NAC). The purpose of this retrospective study is to test if prediction models combining multiple MRI features outperform models with single features. Four features were quantitatively calculated in each MRI exam: functional tumor volume, longest diameter, sphericity, and contralateral background parenchymal enhancement. Logistic regression analysis was used to study the relationship between MRI variables and pathologic complete response (pCR). Predictive performance was estimated using the area under the receiver operating characteristic curve (AUC). The full cohort was stratified by hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2) status (positive or negative). A total of 384 patients (median age: 49 y/o) were included. Results showed analysis with combined features achieved higher AUCs than analysis with any feature alone. AUCs estimated for the combined versus highest AUCs among single features were 0.81 (95% confidence interval [CI]: 0.76, 0.86) versus 0.79 (95% CI: 0.73, 0.85) in the full cohort, 0.83 (95% CI: 0.77, 0.92) versus 0.73 (95% CI: 0.61, 0.84) in HR-positive/HER2-negative, 0.88 (95% CI: 0.79, 0.97) versus 0.78 (95% CI: 0.63, 0.89) in HR-positive/HER2-positive, 0.83 (95% CI not available) versus 0.75 (95% CI: 0.46, 0.81) in HR-negative/HER2-positive, and 0.82 (95% CI: 0.74, 0.91) versus 0.75 (95% CI: 0.64, 0.83) in triple negatives. Multi-feature MRI analysis improved pCR prediction over analysis of any individual feature that we examined. Additionally, the improvements in prediction were more notable when analysis was conducted according to cancer subtype.

Fully Automatic Assessment of Background Parenchymal Enhancement on Breast MRI Using Machine-Learning Models

BACKGROUND

Automated measurement and classification models with objectivity and reproducibility are required for accurate evaluation of the breast cancer risk of fibroglandular tissue (FGT) and background parenchymal enhancement (BPE).

PURPOSE

To develop and evaluate a machine-learning algorithm for breast FGT segmentation and BPE classification.

STUDY TYPE

Retrospective.

POPULATION

A total of 794 patients with breast cancer, 594 patients assigned to the development set, and 200 patients to the test set.

FIELD STRENGTH/SEQUENCE

3T and 1.5T; T₂ -weighted, fat-saturated T₁ -weighted (T₁ W) with dynamic contrast enhancement (DCE).

ASSESSMENT

Manual segmentation was performed for the whole breast and FGT regions in the contralateral breast. The BPE region was determined by thresholding using the subtraction of the pre- and postcontrast T₁ W images and the segmented FGT mask. Two radiologists independently assessed the categories of FGT and BPE. A deep-learning-based algorithm was designed to segment and measure the volume of whole breast and FGT and classify the grade of BPE.

STATISTICAL TESTS

Dice similarity coefficients (DSC) and Spearman correlation analysis were used to compare the volumes from the manual and deep-learning-based segmentations. Kappa statistics were used for agreement analysis. Comparison of area under the receiver operating characteristic (ROC) curves (AUC) and F1 scores were calculated to evaluate the performance of BPE classification.

RESULTS

The mean (±SD) DSC for manual and deep-learning segmentations was 0.85 ± 0.11. The correlation coefficient for FGT volume from manual- and deep-learning-based segmentations was 0.93. Overall accuracy of manual segmentation and deep-learning segmentation in BPE classification task was 66% and 67%, respectively. For binary categorization of BPE grade (minimal/mild vs. moderate/marked), overall accuracy increased to 91.5% in manual segmentation and 90.5% in deep-learning segmentation; the AUC was 0.93 in both methods.

DATA CONCLUSION

This deep-learning-based algorithm can provide reliable segmentation and classification results for BPE.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY STAGE: 2.

Personalized early detection and prevention of breast cancer: ENVISION consensus statement

The European Collaborative on Personalized Early Detection and Prevention of Breast Cancer (ENVISION) brings together several international research consortia working on different aspects of the personalized early detection and prevention of breast cancer. In a consensus conference held in 2019, the members of this network identified research areas requiring development to enable evidence-based personalized interventions that might improve the benefits and reduce the harms of existing breast cancer screening and prevention programmes. The priority areas identified were: 1) breast cancer subtype-specific risk assessment tools applicable to women of all ancestries; 2) intermediate surrogate markers of response to preventive measures; 3) novel non-surgical preventive measures to reduce the incidence of breast cancer of poor prognosis; and 4) hybrid effectiveness-implementation research combined with modelling studies to evaluate the long-term population outcomes of risk-based early detection strategies. The implementation of such programmes would require health-care systems to be open to learning and adapting, the engagement of a diverse range of stakeholders and tailoring to societal norms and values, while also addressing the ethical and legal issues. In this Consensus Statement, we discuss the current state of breast cancer risk prediction, risk-stratified prevention and early detection strategies, and their implementation. Throughout, we highlight priorities for advancing each of these areas.