Background Parenchymal Enhancement Research Articles

Are background breast parenchymal features on preoperative breast MRI associated with disease-free survival in patients with invasive breast cancer?

To evaluate whether breast parenchymal features of the contralateral breast on preoperative MRI are associated with primary breast cancer characteristics and disease-free survival (DFS) in women with invasive breast cancer. Women with newly diagnosed invasive breast cancer who underwent preoperative breast MRI followed by surgery were retrospectively evaluated. Background parenchymal enhancement (BPE) on dynamic contrast-enhanced MRI and background diffusion signal (BDS) on diffusion-weighted MRI of the contralateral breast were qualitatively assessed using a four-category scale: minimal, mild, moderate, or marked. Primary breast cancer characteristics were compared based on the degree of BPE or BDS. Cox proportional hazards models were used to evaluate the association between MRI parenchymal features and DFS after adjusting for clinicopathologic features. A total of 515 women (mean age, 54years) were included. Of whom, 46 (8.9%) patients who developed disease recurrence at a median follow-up of 60months were observed. A high level (moderate/marked) of BPE or BDS was associated with younger age (≤ 45) and premenopausal status (all P < 0.05) compared to a low level (minimal/mild), but it was not associated with primary cancer characteristics such as tumor stage, grade, or subtype. Multivariable Cox proportional hazards analysis demonstrated that larger tumor size (> 2cm) (hazard ratio [HR], 3.877; P < . 001), triple-negative subtype (HR, 2.440; P = .013), and axillary node metastasis (HR, 1.823; P = .049) were associated with worse DFS. No associations were observed between background parenchymal features and disease outcomes. MRI parenchymal features, including BPE and BDS, of the contralateral breast showed no associations with primary breast cancer characteristics or DFS in women with invasive breast cancer.

Clinical and Imaging features associated with malignant focal non-mass enhancement on breast MRI

Introduction: Focal non-mass enhancement (NME) is a common breast MRI finding with limited data to guide management. This study aimed to assess clinical and imaging features of malignant BI-RADS 4 focal NME. MethodsThis IRB-approved, retrospective study included breast MRI exams between August 1, 2013 and September 1, 2022 yielding BI-RADS 4 focal NME lesions that underwent core biopsy or excision with available pathology result or demonstrated decrease or resolution during follow-up MRI or at least two years of MRI stability. ResultsA total of 296 BI-RADS 4 focal NME lesions in 246 patients were included in the study. The overall malignancy rate of BI-RADS 4 focal NME was 36/296 (12.2%). Focal NME in a patient presenting for evaluation of extent of disease or other diagnostic concern was 5.5 and 3.4 times more likely, respectively, to be malignant compared to focal NME seen on a high-risk screening exam. There was also a significant association between malignancy and focal NME that was brighter than background parenchymal enhancement (BPE) on maximum intensity projection (MIP) images. There was no significant association between malignancy and lesion size, internal enhancement pattern, amount of BPE, amount of fibroglandular tissue, or signal intensity on T2-weighted images. ConclusionOur study yielded a malignancy rate of 12.2% for BI-RADS 4 focal NME lesions. Indication for MRI and signal intensity compared to BPE on MIP images were features associated with malignancy that may provide guidance on the management for focal NME. Micro abstractFocal non-mass enhancement (NME) is a common breast MRI finding with limited data to guide management. In this rertospective study of 296 BI-RADS 4 focal NME lesions in 246 patients, 36/296 (12.2%) were malignant. Lesions were more likely to be malignant if they were brighter than background parenchymal enhancement or if the exam was a diagnostic, rather than screening, exam.

Machine Learning Assessment of Background Parenchymal Enhancement in Breast Cancer and Clinical Applications: A Literature Review.

Background Parenchymal Enhancement (BPE) on breast MRI holds promise as an imaging biomarker for breast cancer risk and prognosis. The ability to identify those at greatest risk can inform clinical decisions, promoting early diagnosis and potentially guiding strategies for prevention such as risk-reduction interventions with the use of selective estrogen receptor modulators and aromatase inhibitors. Currently, the standard method of assessing BPE is based on the Breast Imaging-Reporting and Data System (BI-RADS), which involves a radiologist's qualitative categorization of BPE as minimal, mild, moderate, or marked on contrast-enhanced MRI. This approach can be subjective and prone to inter/intra-observer variability, and compromises accuracy and reproducibility. In addition, this approach limits qualitative assessment to 4 categories. More recently developed methods using machine learning/artificial intelligence (ML/AI) techniques have the potential to quantify BPE more accurately and objectively. This paper will review the current machine learning/AI methods to determine BPE, and the clinical applications of BPE as an imaging biomarker for breast cancer risk prediction and prognosis.

Performance of breast MRI for high-risk screening during lactation.

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

Factors Influencing Background Parenchymal Enhancement in Contrast-Enhanced Mammography Images.

Background: The aim of this study is to evaluate the correlation between background parenchymal enhancement (BPE) and various patient-related and technical factors in recombined contrast-enhanced spectral mammography (CESM) images. Material and Methods: We assessed CESM images from 62 female patients who underwent CESM between May 2017 and October 2019, focusing on factors influencing BPE. A total of 235 images, all acquired using the same mammography machine, were analyzed. A region of interest (ROI) with a standard size of 0.75 to 1 cm2 was used to evaluate the minimal, maximal, and average pixel intensity enhancement. Additionally, the images were qualitatively assessed on a scale from 1 (minimal BPE) to 4 (marked BPE). We examined correlations with body mass index (BMI), age, hematocrit, hemoglobin levels, cardiovascular conditions, and the amount of pressure applied during the examination. Results: Our study identified a significant correlation between the amount of pressure applied during the examination and the BPE (Spearman's ρ = 0.546). Additionally, a significant but weak correlation was observed between BPE and BMI (Spearman's ρ = 0.421). No significant associations were found between BPE and menopausal status, cardiovascular preconditions, hematocrit, hemoglobin levels, breast density, or age. Conclusions: Patient-related and procedural factors significantly influence BPE in CESM images. Specifically, increased applied pressure and BMI are associated with higher BPE.

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

To evaluate if background parenchymal enhancement (BPE) on contrast-enhanced mammography (CEM), graded according to the2022 CEM-dedicated Breast Imaging Reporting and Data System (BI-RADS) lexicon, is associated with breast density, menopausal status, and age. This bicentric retrospective analysis included CEM examinations performed for thework-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). A total of 200 women (median age 56.8years, 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. BPE on CEM is associated with well-established breast cancer risk factors, being higher in women with higher breast density and premenopausal status.

Preoperative breast MR imaging influences surgical management in patients with invasive lobular carcinoma.

The purpose of the study is to assess the role of preoperative magnetic resonance (MR) imaging on the surgical management of invasive lobular carcinoma (ILC) and to evaluate whether breast density and background parenchymal enhancement (BPE) influence surgical treatment. This retrospective study was conducted on 56 patients who were diagnosed with ILC between 2014 and 2020. All patients had mammogram and ultrasound. Preoperative MRI was available in 34 patients. Age, menopausal status, breast density, BPE, multifocality/multicentricity and surgical treatment were collected. Mean pathological tumour size was 36.4 mm (range 5-140 mm). Dense breasts had larger tumours compared to non-dense breasts (P = 0.072). Of the 34 patients with MRI, 6 opted for mastectomy. Of the remaining 28 cases, MRI findings upgraded surgery to mastectomy in 54% (15/28) because mammogram/ultrasound underestimated tumour extent in 25% (7/28), or multifocal/multicentric disease was identified in 29% (8/28). Tumour size was underestimated by MRI in 7% (2/28). In the non-MRI subgroup, 64% (14/22) of patients underwent breast-conserving surgery, but 29% of them (4/14) required a second-stage mastectomy due to extensive margin involvement. There was no difference in mastectomy rate between patients with MRI (62%) and without MRI (55%) (P = 0.061). Tumour size correlation between MRI and histopathology demonstrated an excellent intraclass correlation coefficient (P < 0.001). Surgical treatment recommendation was not significantly impacted by breast density or BPE. Breast MRI improves surgical management of patients with ILC in providing additional diagnostic information often missed with standard imaging modalities, and without increasing mastectomy rate. Surgical treatment is not impacted by breast density or BPE.

Development and validation of machine learning models for predicting HER2-zero and HER2-low breast cancers.

To develop and validate machine learning models for human epidermal growth factor receptor 2 (HER2)-zero and HER2-low using MRI features pre-neoadjuvant therapy (NAT). Five hundred and sixteen breast cancer patients post-NAT surgery were randomly divided into training (n = 362) and internal validation sets (n = 154) for model building and evaluation. MRI features (tumour diameter, enhancement type, background parenchymal enhancement, enhancement pattern, percentage of enhancement, signal enhancement ratio, breast oedema, and apparent diffusion coefficient) were reviewed. Logistic regression (LR), support vector machine (SVM), k-nearest neighbour (KNN), and extreme gradient boosting (XGBoost) models utilized MRI characteristics for HER2 status assessment in training and validation datasets. The best-performing model generated a HER2 score, which was subsequently correlated with pathological complete response (pCR) and disease-free survival (DFS). The XGBoost model outperformed LR, SVM, and KNN, achieving an area under the receiver operating characteristic curve (AUC) of 0.783 (95% CI, 0.733-0.833) and 0.787 (95% CI, 0.709-0.865) in the validation dataset. Its HER2 score for predicting pCR had an AUC of 0.708 in the training datasets and 0.695 in the validation dataset. Additionally, the low HER2 score was significantly associated with shorter DFS in the validation dataset (hazard ratio: 2.748, 95% CI, 1.016-7.432, P = .037). The XGBoost model could help distinguish HER2-zero and HER2-low breast cancers and has the potential to predict pCR and prognosis in breast cancer patients undergoing NAT. HER2-low-expressing breast cancer can benefit from the HER2-targeted therapy. Prediction of HER2-low expression is crucial for appropriate management. MRI features offer a solution to this clinical issue.

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.

Early prediction of neoadjuvant chemotherapy efficacy for mass breast cancer based on dynamic contrast‐enhanced magnetic resonance imaging radiomics

AbstractRadiomics uses automated algorithms to extract high‐order features from images, which can contribute to clinical decisions such as therapeutic efficacy evaluation. We assessed the value of a dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI)‐based radiomics model for predicting pathological complete response (pCR) after a second cycle of neoadjuvant chemotherapy (NAC) in patients with mass breast cancer. We retrospectively analyzed data from 149 patients with mass breast cancer who underwent NAC between January 2017 and December 2022. Using DCE‐MRI, before NAC and after a second cycle of NAC, the least absolute shrinkage and selection operator and logistic regression (LR) algorithms were applied for feature selection and radiomics modeling. We found significant differences in two clinical imaging features (molecular subtypes, background parenchymal enhancement changes) and two radiomics features. Clinical and radiomics features were employed to build clinical, radiomics, and combined models to predict pCR. The LR model that combined clinical and radiomics features had an area under the curve of 0.811, higher than that for the imaging or radiomics model. Our findings suggest that a combined model based on imaging and radiomics features can improve early prediction of NAC efficacy for patients with mass breast cancer.

Kaiser score diagnosis of breast MRI lesions: Factors associated with false-negative and false-positive results

PurposeWe sought factors associated with false-negative and false-positive results in the diagnosis of breast lesions using the Kaiser score (KS) on breast magnetic resonance imaging (MRI). MethodsWe retrospectively analyzed 1058 patients with 1058 breast lesions who underwent preoperative breast MRI with successful histopathologic results. Two radiologists assessed each lesion according to KS criteria, and clinicopathologic features and MRI findings were analyzed. Multivariate regression analysis was conducted to identify factors associated with false-negative and false-positive KS results. ResultsOf the 1058 lesions, 859 were malignant and 199 were benign. Particularly high misdiagnosis rates were observed for intraductal papilloma, inflammatory lesion, and mucinous carcinoma. For breast cancer, KS yielded 821 (95.6 %) true-positive and 38 (4.4 %) false-negative results. Multivariate analysis showed that smaller lesion size (≤1 cm) (OR, 3.698; 95 %CI, 1.430–9.567; p = 0.007), absence of ipsilateral breast hypervascularity (OR, 3.029; 95 %CI, 1.370–6.693; p = 0.006), and presence of hyperintensity on T2WI (OR, 2.405; 95 %CI, 1.121–5.162; p = 0.024) were significantly associated with false-negative breast cancer results. For benign lesions, KS yielded 141 (70.9 %) true-negative and 58 (29.1 %) false-positive results. Multivariate regression analysis revealed that non-mass enhancement lesions (OR, 4.660; 95 %CI, 2.018–10.762; p＜0.001), moderate/high background parenchymal enhancement (OR, 2.402; 95 %CI, 1.180–4.892; p = 0.016), and the presence of hyperintensity on T2WI (OR, 2.986; 95 %CI, 1.386–6.433; p = 0.005) were significantly associated with false-positive KS results. ConclusionSeveral clinicopathologic and MRI features influence the accuracy of KS diagnosis. Understanding these factors may facilitate appropriate use of KS and guide alternative diagnostic approaches, ultimately improving diagnostic accuracy in the evaluation of breast lesions.

Ultrafast sequence-based prediction model and nomogram to differentiate additional suspicious lesions on preoperative breast MRI.

To investigate whether ultrafast sequence improves the diagnostic performance of conventional dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in differentiating additional suspicious lesions (ASLs) on preoperative breast MRI. A retrospective database search identified 668 consecutive patients who underwent preoperative breast DCE-MRI with ultrafast sequence between June 2020 and July 2021. Among these, 107 ASLs from 98 patients with breast cancer (36 multifocal, 42 multicentric, and 29 contralateral) were identified. Clinical, pathological, conventional MRI findings, and ultrafast sequence-derived parameters were collected. A prediction model that adds ultrafast sequence-derived parameters to clinical, pathological, and conventional MRI findings was developed and validated internally. Decision curve analysis and net reclassification index statistics were performed. A nomogram was constructed. The ultrafast model adding time to peak enhancement, time to enhancement, and maximum slope showed a significantly increased area under the receiver operating characteristic curve compared with the conventional model which includes age, human epidermal growth factor receptor 2 expression of index cancer, size of index cancer, lesion type of index cancer, location of ASL, and size of ASL (0.92 vs. 0.82; p = 0.002). The decision curve analysis showed that the ultrafast model had a higher overall net benefit than the conventional model. The net reclassification index of ultrafast model was 23.3% (p = 0.001). A combination of ultrafast sequence-derived parameters with clinical, pathological, and conventional MRI findings can aid in the differentiation of ASL on preoperative breast MRI. Our prediction model and nomogram that was based on ultrafast sequence-derived parameters could help radiologists differentiate ASLs on preoperative breast MRI. Ultrafast MRI can diminish background parenchymal enhancement and possibly improve diagnostic accuracy for additional suspicious lesions (ASLs). Locationof ASL, larger size of ASL, and highermaximum slope were associated with malignant ASL. The ultrafast model and nomogram can help preoperatively differentiate additional malignancies.

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.

Magnetic resonance imaging-based prognostic model for subsequent distant metastasis in patients with ipsilateral breast tumor recurrence following breast-conserving surgery.

Ipsilateral breast tumor recurrence (IBTR) following breast-conserving surgery (BCS) has been considered a risk factor for distant metastasis (DM). Limited data are available regarding the subsequent outcomes after IBTR. Therefore, this study aimed to determine the clinical course after IBTR and develop a magnetic resonance imaging (MRI)-based predictive model for subsequent DM. We retrospectively extracted quantitative features from MRI to construct a radiomics cohort, with all eligible patients undergoing preoperative MRI at time of primary tumor and IBTR between 2010 and 2018. Multivariate Cox analysis was performed to identify factors associated with DM. Three models were constructed using different sets of clinicopathological, qualitative, and quantitative MRI features and compared. Additionally, Kaplan-Meier analysis was performed to assess the prognostic value of the optimal model. Among the 183 patients who experienced IBTR, 47 who underwent MRI for both primary and recurrent tumors were enrolled. Multivariate analysis demonstrated that the independent prognostic factors were human epidermal growth factor receptor 2 (HER2) status [hazard ratio (HR) =5.40] and background parenchymal enhancement (BPE) (HR =7.94) (all P values <0.01). Furthermore, four quantitative MRI features of recurrent tumors were selected through the least absolute shrinkage and selection operator (LASSO) method. The combined model exhibited superior performance [concordance index (C-index) 0.77] compared to the clinicoradiological model (C-index 0.71; P=0.006) and radiomics model (C-index 0.70; and P=0.01). Furthermore, the combined model successfully categorized patients into low- and high-risk subgroups with distinct prognoses (P<0.001). The clinicopathological and MRI features of IBTR were associated with secondary events following surgery. Additionally, the MRI-based combined model exhibited the highest predictive efficacy. These findings could be helpful in risk stratification and tailoring follow-up strategies in patients with IBTR.

Racial differences in background parenchymal enhancement, a novel marker of breast cancer.

10528 Background: Breast density is a risk factor for breast cancer that can guide eligibility for supplemental screening including MRI, but Black women are less likely to qualify for supplemental screening due to having less dense breasts on average despite higher rates of breast cancer mortality. Background parenchymal enhancement (BPE), the enhancement of fibroglandular breast tissue on contrast-enhanced MRI, is a novel biomarker that has been shown to be a superior breast cancer risk marker than breast density. Women with high BPE levels have up to a four-fold increased risk of breast cancer, even after adjusting for density, but no studies have examined BPE exclusively among Black women. Methods: We identified Black and white women ages 40-74 with no prior history of breast cancer who had a mammogram and subsequent MRI as part of a screening or diagnostic work-up from 2016-2022 at our institution. BPE was qualitatively defined according to BI-RADS (Breast Imaging Reporting and Data System), with four ordinal levels of increasing enhancement: minimal, mild, moderate, and marked. We conducted a cross-sectional study to perform a logistic regression with race as the exposure and moderate or marked BPE as the outcome, using minimal and mild BPE as the reference group, adjusted for age and BI-RADS density. Results: We included 1027 Black women and 4834 white women in our analysis. Fewer Black women had extremely dense breasts compared to white women (6% vs 13%, p= 0.04), however, there was a larger proportion of high BPE (moderate or marked) in Black women compared to white women (32% vs 28%, p = 0.03) (Table). There was an inverse association between age and BPE levels (p = 0.03), and a positive association between density and BPE levels regardless of race (p&lt; 0.01) (not shown). Black women were 39% more likely to have high BPE levels (95% CI: 1.39 [1.19, 1.63]), adjusted for age and density. Conclusions: Black women have lower density but higher BPE than white women, suggesting that BPE may improve identification of breast cancer risk for Black women. Participants [Table: see text]

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.

Deep-learning model for background parenchymal enhancement classification in contrast-enhanced mammography

Background. Breast background parenchymal enhancement (BPE) is correlated with the risk of breast cancer. BPE level is currently assessed by radiologists in contrast-enhanced mammography (CEM) using 4 classes: minimal, mild, moderate and marked, as described in breast imaging reporting and data system (BI-RADS). However, BPE classification remains subject to intra- and inter-reader variability. Fully automated methods to assess BPE level have already been developed in breast contrast-enhanced MRI (CE-MRI) and have been shown to provide accurate and repeatable BPE level classification. However, to our knowledge, no BPE level classification tool is available in the literature for CEM. Materials and methods. A BPE level classification tool based on deep learning has been trained and optimized on 7012 CEM image pairs (low-energy and recombined images) and evaluated on a dataset of 1013 image pairs. The impact of image resolution, backbone architecture and loss function were analyzed, as well as the influence of lesion presence and type on BPE assessment. The evaluation of the model performance was conducted using different metrics including 4-class balanced accuracy and mean absolute error. The results of the optimized model for a binary classification: minimal/mild versus moderate/marked, were also investigated. Results. The optimized model achieved a 4-class balanced accuracy of 71.5% (95% CI: 71.2–71.9) with 98.8% of classification errors between adjacent classes. For binary classification, the accuracy reached 93.0%. A slight decrease in model accuracy is observed in the presence of lesions, but it is not statistically significant, suggesting that our model is robust to the presence of lesions in the image for a classification task. Visual assessment also confirms that the model is more affected by non-mass enhancements than by mass-like enhancements. Conclusion. The proposed BPE classification tool for CEM achieves similar results than what is published in the literature for CE-MRI.

A clinicopathological-imaging nomogram for the prediction of pathological complete response in breast cancer cases administered neoadjuvant therapy

ObjectiveTo construct a user-friendly nomogram with MRI and clinicopathological parameters for the prediction of pathological complete response (pCR) after neoadjuvant therapy (NAT) in patients with breast cancer (BC). MethodsWe retrospectively enrolled consecutive female patients pathologically confirmed with breast cancer who received NAT followed by surgery between January 2018 and December 2022 as the development cohort. Additionally, we prospectively collected eligible candidates between January 2023 and December 2023 as an external validation group at our institution. Pretreatment MRI features and clinicopathological variables were collected, and the pre- and post-treatment background parenchymal enhancement (BPE) and the changes in BPE on two MRIs were compared between patients who achieved pCR and those who did not. Multivariable logistic regression analysis was used to identify independent variables associated with pCR in the development cohort. These independent variables were combined into a predictive nomogram for which performance was assessed using the area under the receiver operating characteristic curve (AUC), calibration plot, decision curve analysis, and external validation. ResultsIn the development cohort, there were a total of 276 female patients with a mean age of 48.3 ± 8.7 years, while in the validation cohort, there were 87 female patients with a mean age of 49.0 ± 9.5 years. Independent prognostic factors of pCR included small tumor size, HER2(+), high Ki-67 index,high signal enhancement ratio (SER), low minimum value of apparent diffusion coefficient (ADCmin), and significantly decreased BPE after NAT(change of BPE). The nomogram, which incorporates the above parameters, demonstrated excellent predictive performance in both the development and external validation cohorts, with AUC values of 0.900 and 0.850, respectively. Additionally, the nomogram showed excellent calibration capacities, as indicated by Hosmer-Lemeshow test p values of 0.508 and 0.423 in the two cohorts. Furthermore, the nomogram provided greater net benefits compared to the default simple schemes in both cohorts. ConclusionA nomogram constructed using tumor size, HER2 status, Ki-67 index, SER, ADCmin, and changes in pre- and post-NAT BPE demonstrated strong predictive performance, calibration ability, and greater net benefits for predicting pCR in patients with BC after NAT. This suggests that the user-friendly nomogram could be a valuable imaging biomarker for identifying suitable candidates for NAT.

Abstract PO4-08-07: Background parenchymal enhancement (BPE) on breast magnetic resonance imaging as a biomarker of breast cancer risk among BRCA1/2 carriers

Abstract Introduction: BRCA1/2 carriers are recommended to undergo dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) screening of the breasts annually. Fibroglandular tissue (FGT) may enhance with MRI contrast agent termed background parenchymal enhancement (BPE). BPE is believed to be a marker of hormonally responsive breast tissue, as BPE is impacted by aromatase inhibition and by oophorectomy. BPE has been shown to be more strongly associated with breast cancer risk than breast density. We hypothesize that BPE may serve as a useful dynamic marker of breast cancer risk among BRCA1/2 mutation carriers, given their frequent MRI screening. Methods: We identified female BRCA1/2 carriers from the University of Pennsylvania Cancer Risk Evaluation Program with no personal history of breast or ovarian cancer at the time of breast MRI. Patients diagnosed within 6 months were excluded to remove prevalent cancer cases at MRI. Among 421 eligible patients, 42 were diagnosed with breast cancer. BPE was quantified from all available breast MRIs using a validated, fully automated method to segment fibroglandular tissue (FGT) and quantify BPE. For preprocessing, the N4ITK algorithm was used for bias-field artifact correction and FGT was segmented using a soft-margin support vector machine classifier using the T1-weighted nonfat-saturated breast MRI. FGT was segmented and quantified in 3D. For BPE quantification, the T1-weighted nonfat-saturated image and the derived FGT mask were registered to the pre-contrast DCE-MRI. The first post contrast DCE-MRI and FGT mask were used to compute the relative enhancement map compared to the pre-contrast image. We calculated BPE metrics in two ways. First, the median BPE enhancement was calculated as the median enhancement of all voxels within the FGT mask. Second the BPE enhancement ratio was calculated as the proportion of voxels enhancing at ≥20%. We performed Cox proportional hazards regression to estimate the hazard ratios for breast cancer per standard deviation unit increase in each BPE metric, with first MRI as the time origin and censoring upon bilateral mastectomy, ovarian cancer diagnosis, death, or loss to follow-up and adjustment for age at MRI, menopause status, volumetric breast density (VBD), body mass index (BMI), and BRCA1 vs. BRCA2 mutation. BPE, VBD, BMI, and menstrual status were included as time varying covariates, enabling inclusion of multiple BPE measurements per woman. Results: The mean age at first MRI was 39 for cases and 41 for non-cases, and the mean number of MRIs was 4. There were no significant differences in median BPE (14% vs. 14%, p=0.965) or BPE ratio (34% vs. 36%, p=0.769) between cases and non-cases. In regression analyses adjusted for age and menstrual status, median BPE was significantly associated with breast cancer risk (HR=1.12, p=0.025). The HR was similar in the fully adjusted model, though did not reach statistical significance (HR=1.11, p=0.055). Higher BPE ratio was significantly associated with increased breast cancer risk in both minimally (HR=1.21, p=0.005) and fully adjusted models (HR=1.21, p=0.012). Conclusions: Our results suggest that BPE is significantly associated with breast cancer diagnosis among BRCA1/2 carriers undergoing breast MRI screening. Both median BPE and BPE ratio metrics were associated with breast cancer risk, even after adjusting for breast density and when accounting for multiple measurements of BPE over time, suggesting that BPE may provide additional risk information for BRCA1/2 carriers. Table 1: Results of Cox proportional hazards regression with time varying covariates Citation Format: Anne Marie McCarthy, Alex Anh-Tu Nguyen, Walter Mankowski, Eric Cohen, Sarah Ehsan, Rhonda Gillette, Lauren Pantalone, Susan Weinstein, Emily Conant, Susan Domchek, Despina Kontos. Background parenchymal enhancement (BPE) on breast magnetic resonance imaging as a biomarker of breast cancer risk among BRCA1/2 carriers [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-08-07.

Abstract PO5-07-07: The biological basis of breast MRI background parenchymal enhancement in women with high breast cancer risk

Abstract Introduction: The amount and degree of the fibroglandular tissue (FGT) enhancement on breast MRI, known as background parenchymal enhancement (BPE), is an emerging marker of breast cancer risk. Higher qualitative and quantitative BPE levels have been shown to correlate with both future risk of initial breast cancer diagnosis and breast cancer recurrence after treatment. Furthermore, adjuvant and preventative therapies such as radiation and endocrine therapy have been shown to lower BPE in many women, potentially serving as a marker of treatment efficacy. However, the biological basis of BPE remains unclear and elucidation of this mechanism could identify novel targeted prevention strategies. The goal of this study was to assess the association of BPE levels with markers of inflammation, vascularity, proliferation, and estrogen sensitivity in a cohort of high-risk women. Materials and Methods: In this IRB-approved retrospective study, we identified asymptomatic high-risk women who underwent at least one dynamic contrast-enhanced (DCE) breast MRI within one year of prophylactic bilateral mastectomy. Qualitative BPE assessments were obtained from clinical reports. Quantitative BPE measures were obtained using in-house semi-automated software: whole breast FGT was first segmented on pre-contrast images, percent enhancement (PE) was calculated for each voxel as (S1-S0)/S0 × 100%, where S0 and S1 are pre- and early post-contrast signal intensity, respectively, and quantitative BPE was then defined as the mean PE of the FGT. Additionally, the BPE volume ratio was calculated as the fraction of FGT that had a PE &amp;gt;50%. Histopathology markers of inflammation (COX-2), vascularity (VEGF), proliferation (Ki-67), and estrogen receptor status (ER) were prospectively measured on archived mastectomy specimens of each breast. BPE metrics were correlated with histopathological markers using Spearman’s rank correlation, with and without adjustment by menopausal status of the patient. All tests were based on generalized estimating equations models to account for the non-independence of the two breasts per patient. A p-value &amp;lt; 0.05 was considered significant. Results: We identified 56 women (median age: 39; range: 26-63 years) for this study, of which 39% were post-menopausal (N=22). Distribution of qualitative BPE levels were 41% minimal (N=23), 29% mild (N=16), 18% moderate (N=10), and 12% marked (N=7). Qualitative BPE and quantitative BPE markers were moderately positively correlated (r=0.37-0.54, p&amp;lt; 0.01). Table 1 lists the correlations between BPE metrics and histopathology markers. After adjusting for menopausal status, qualitative BPE was positively associated with ER (r=0.27, p=0.02) and VEGF (r=0.24, p&amp;lt; 0.01; r=0.21, p=0.02 adjusted). Quantitative BPE and BPE volume ratio were both positively correlated with VEGF (p&amp;lt; 0.04) and negatively correlated with COX2 (p&amp;lt; 0.05). No significant associations were observed between BPE and Ki-67. Discussion: Our study suggests that increased BPE on MRI positively correlates with increased vascularity and higher estrogen receptor expression but not inflammation or proliferation within normal breast tissue in high-risk women. These findings, along with established associations between BPE and cancer risk, could be useful to select and assess efficacy of targeted preventative treatments. Table. Spearman rank correlations between BPE metrics and histopathological markers. Citation Format: Pegah Khoshpouri, Anum Kazerouni, Sana Parsian, Daniel Hippe, Olivia Walsh, Lisa Koch, Savannah Partridge, Habib Rahbar. The biological basis of breast MRI background parenchymal enhancement in women with high breast cancer risk [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-07-07.