Background Parenchymal Enhancement Research Articles

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 >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 < 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< 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< 0.01; r=0.21, p=0.02 adjusted). Quantitative BPE and BPE volume ratio were both positively correlated with VEGF (p< 0.04) and negatively correlated with COX2 (p< 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.

Computerized assessment of background parenchymal enhancement on breast dynamic contrast-enhanced-MRI including electronic lesion removal.

Current clinical assessment qualitatively describes background parenchymal enhancement (BPE) as minimal, mild, moderate, or marked based on the visually perceived volume and intensity of enhancement in normal fibroglandular breast tissue in dynamic contrast-enhanced (DCE)-MRI. Tumor enhancement may be included within the visual assessment of BPE, thus inflating BPE estimation due to angiogenesis within the tumor. Using a dataset of 426 MRIs, we developed an automated method to segment breasts, electronically remove lesions, and calculate scores to estimate BPE levels. A U-Net was trained for breast segmentation from DCE-MRI maximum intensity projection (MIP) images. Fuzzy -means clustering was used to segment lesions; the lesion volume was removed prior to creating projections. U-Net outputs were applied to create projection images of both, affected, and unaffected breasts before and after lesion removal. BPE scores were calculated from various projection images, including MIPs or average intensity projections of first- or second postcontrast subtraction MRIs, to evaluate the effect of varying image parameters on automatic BPE assessment. Receiver operating characteristic analysis was performed to determine the predictive value of computed scores in BPE level classification tasks relative to radiologist ratings. Statistically significant trends were found between radiologist BPE ratings and calculated BPE scores for all breast regions (Kendall correlation, ). Scores from all breast regions performed significantly better than guessing ( from the -test). Results failed to show a statistically significant difference in performance with and without lesion removal. BPE scores of the affected breast in the second postcontrast subtraction MIP after lesion removal performed statistically greater than random guessing across various viewing projections and DCE time points. Results demonstrate the potential for automatic BPE scoring to serve as a quantitative value for objective BPE level classification from breast DCE-MR without the influence of lesion enhancement.

Abstract PO3-28-10: MRI Radiomic phenotypes derived from the ECOG-ACRIN E4112 Trial to assess high-risk ductal carcinoma in situ

Abstract INTRODUCTION Ductal carcinoma in situ (DCIS) is a non-lethal pre-invasive breast cancer that can co-exist with invasive disease. Dynamic contrast-enhanced (DCE) MRI is sensitive for the detection of high-grade DCIS and invasive cancer while Oncotype DCIS Score is a 12-gene assay that can assess recurrence risk. In practice, it is difficult to distinguish low- from high-risk DCIS, which leads to overtreatment for up to half of women diagnosed with DCIS. We hypothesize that radiomic phenotypes of DCIS derived from DCE-MRI data may serve as prognostic markers to improve risk stratification by capturing disease heterogeneity. Here we evaluate the ability of these phenotypes to predict DCIS Score and upstaging of DCIS to invasive disease on wide local excision in a multicenter trial. METHODS Data: DCE-MRI data from the ECOG-ACRIN E4112 trial were retrospectively analyzed. Primary analysis focused on participants with data on disease upstaging (N=295), with secondary analysis in a subset (N=174) of participants with DCIS Scores (dichotomized as >55 and 55) and pure DCIS. Clinical information included patient demographics, lesion morphology on MRI, background parenchymal enhancement, DCIS grade, central necrosis, and hormone receptor status. Data analysis: Radiologist-drawn lesion segmentations and publicly available software, CaPTk, were used to compute 64 radiomic features from first post-contrast images for each participant. Radiomic phenotypes were identified using hierarchical clustering on the extracted features. A Chi-square test was used to evaluate the association between radiomic phenotypes and each outcome. The likelihood ratio test was used to compare two logistic regression models: 1)clinical model using only clinical information as predictors and 2) clinical+phenotypes model using clinical information and phenotype assignment as predictors. Each model was used for the prediction of upstaging to invasive disease and DCIS score. Model performance was evaluated as the 10-fold cross-validated area under the receiver operator characteristic curve (AUC). A p< 0.05 was considered significant. RESULTS A total of 45 (15%) cases upstaged to invasive disease. Two radiomic phenotypes were identified: Phenotype 1 indicated greater lesion signal heterogeneity, while Phenotype 2 indicated lower heterogeneity. Radiomic phenotype was strongly associated with disease upstaging (p=0.0034) – with a higher rate of upstaging for Phenotype 1 – but not with DCIS score (p=0.1174, Table 1). For predicting disease upstaging, the clinical+phenotypes model yielded a higher AUC=0.72 and a significantly better fit to the data (p=0.0022) compared to the clinical model parameterized by clinical information alone (AUC=0.69). For predicting DCIS Score, the clinical+phenotypes model (AUC=0.77) showed similar performance compared to the clinical model (AUC=0.76) and no significant improvement in fit to the data (p=0.2920). CONCLUSION Radiomic phenotypes capturing disease heterogeneity show promise as prognostic predictors for predicting disease upstaging in DCIS compared to clinical information alone and may enable more efficient disease management. We observed that phenotypes did not have independent predictive outcome for DCIS score, suggesting that MRI and DCIS Score offer independent information and could be combined in future models to better predict disease recurrence or progression. Clinical applications of radiomic phenotypes may improve risk stratification and potentially result in decreased overtreatment of women diagnosed with DCIS. Table 1: Association between radiomic phenotypes and DCIS outcomes using a Chi square test. Citation Format: Kalina Slavkova, Ruya Kang, Vivian Belenky, Anum Kazerouni, Debosmita Biswas, Hannah Horng, Rhea Chitalia, Michael Hirano, Jennifer Xiao, Ralph Corsetti, Sarah Javid, Derrick Spell, Antonio Wolff, Joseph Sparano, Seema Khan, Christopher Comstock, Justin Romanoff, Jon Steingrimsson, Constantine Gatsonis, Constance Lehman, Savannah Partridge, Despina Kontos, Habib Rahbar. MRI Radiomic phenotypes derived from the ECOG-ACRIN E4112 Trial to assess high-risk ductal carcinoma in situ [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 PO3-28-10.

Abstract PO4-07-09: CONTRAST-ENHANCED MAMMOGRAPHY IN LOCAL STAGING OF SCREEN-DETECTED BREAST CANCER: ADDITIONAL LESIONS AND CHANGES TO CLINICAL MANAGEMENT

Abstract Background BreastScreen Australia, a mammographic population screening program, provides assessment of suspected breast cancer to the point of diagnosis using a combination of conventional imaging and percutaneous biopsy. Comprehensive local staging, not performed at BreastScreen, determines the extent of disease and may identify additional clinically significant breast abnormalities. Local staging options include completion of bilateral mammographic work-up, ultrasound, and/or contrast-based imaging (CBI) (magnetic resonance imaging (MRI) or contrast-enhanced mammography (CEM)), with biopsy as required. We introduced CBI for local staging after diagnosis of screen-detected cancer at our academic hospital breast service in Melbourne, Australia. We report diagnostic findings for otherwise occult disease and their impact on treatment decisions in women who underwent CEM for local staging. Material and methods Women with screen-detected breast cancer who underwent CEM for local staging between November 2018 to April 2022 were identified retrospectively. The reporting breast-specialised radiologist compared BreastScreen and CEM images to identify additional enhancing abnormalities. Additional abnormalities were further investigated with preoperative percutaneous biopsy, surgical biopsy, or problem-solving CBI. Additional CEM-detected abnormalities were documented, with invasive cancer or DCIS recorded as true positive (TP) and any other findings as false positive (FP). Impact on surgical decisions was assessed. Results 204 patients underwent CEM. 62/204 (30%) patients had 76 additional abnormalities, of which 36 (47%) were TP and 40 (53%) FP. CEM identified otherwise occult malignant lesions in 30/204 (15%) patients. TPs comprised 75% invasive cancers and 25% DCIS. 83% (30/36) of malignant abnormalities were ipsilateral to the index lesion, while 17% (6/36) were contralateral. The majority of additional invasive cancers were Grade 2 (20/27, 74.1%), followed by Grade 1 (4/27, 14.8%) then Grade 3 (3/27, 11.1%). All additional invasive cancers had the same phenotype as the patient’s index cancer (ER+/HER2-), except for one abnormality which was ER+/HER2+ where the index cancer was ER+/HER2-. The FP abnormalities consisted of normal breast tissue (20/40, 50%), benign lesions (16/40, 40%) and atypical proliferative lesions (4/40, 10%). Occult malignancies were more common for patients with higher background parenchymal enhancement (20% for moderate/marked vs 4% for minimal/mild, p=0.0023), with no statistically significant differences found by breast density (BIRADS A or B = 12%, vs BIRADS C or D = 20%, p=0.23) or by age (40-49=38%, 50-59=13%, 60-69=13%, ³70=19%, p=0.88). Additional abnormalities found on CEM resulted in surgical management change in 45/204 (22%) patients, including wider resection (24/45), conversion to mastectomy (8/45), contralateral breast surgery (6/45), additional ipsilateral excision (5/45), and bracketing (2/45). Conclusions The use of CEM for local staging of screen-detected breast cancers identified otherwise occult malignancy in 15% of patients. Age and mammographic density did not identify groups at minimal risk of additional findings. Pathology of additional malignancy suggests it is clinically significant. CEM may improve local staging and direct appropriate management of screen-detected breast cancers. Citation Format: Caroline MacCallum, Kenneth Elder, Carolyn Nickson, Kelly Ruecker, Allan Park, Bruce Mann. CONTRAST-ENHANCED MAMMOGRAPHY IN LOCAL STAGING OF SCREEN-DETECTED BREAST CANCER: ADDITIONAL LESIONS AND CHANGES TO CLINICAL MANAGEMENT [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 PO4-07-09.

Quantitative assessment of background parenchymal enhancement is associated with lifetime breast cancer risk in screening MRI.

To compare the quantitative background parenchymal enhancement (BPE) in women with different lifetime risks and BRCA mutation status of breast cancer using screening MRI. This study included screening MRI of 535 women divided into three groups based on lifetime risk: nonhigh-risk women, high-risk women without BRCA mutation, and BRCA1/2 mutation carriers. Six quantitative BPE measurements, including percent enhancement (PE) and signal enhancement ratio (SER), were calculated on DCE-MRI after segmentation of the whole breast and fibroglandular tissue (FGT). The associations between lifetime risk factors and BPE were analyzed via linear regression analysis. We adjusted for risk factors influencing BPE using propensity score matching (PSM) and compared the BPE between different groups. A two-sided Mann-Whitney U-test was used to compare the BPE with a threshold of 0.1 for multiple testing issue-adjusted p values. Age, BMI, menopausal status, and FGT level were significantly correlated with quantitative BPE based on the univariate and multivariable linear regression analyses. After adjusting for age, BMI, menopausal status, hormonal treatment history, and FGT level using PSM, significant differences were observed between high-risk non-BRCA and BRCA groups in PEFGT (11.5 vs. 8.0%, adjusted p = 0.018) and SERFGT (7.2 vs. 9.3%, adjusted p = 0.066). Quantitative BPE varies in women with different lifetime breast cancer risks and BRCA mutation status. These differences may be due to the influence of multiple lifetime risk factors. Quantitative BPE differences remained between groups with and without BRCA mutations after adjusting for known risk factors associated with BPE. BRCA germline mutations may be associated with quantitative background parenchymal enhancement, excluding the effects of known confounding factors. This finding can provide potential insights into the cancer pathophysiological mechanisms behind lifetime risk models. Expanding understanding of breast cancer pathophysiology allows for improved risk stratification and optimized screening protocols. Quantitative BPE is significantly associated with lifetime risk factors and differs between BRCA mutation carriers and noncarriers. This research offers a possible understanding of the physiological mechanisms underlying quantitative BPE and BRCA germline mutations.

The use of ferumoxytol for high-resolution vascular imaging and troubleshooting for abdominal allografts.

Ferumoxytol is an ultrasmall superparamagnetic iron oxide which has been used as an off-label intravenous contrast agent for MRI. Unlike gadolinium-based contrast agents, ferumoxytol remains in the intravascular space with a long half-life of 14-21h. During the first several hours, it acts as a blood-pool agent and has minimal parenchymal enhancement. Studies have shown adequate intravascular signal for up to 72h after initial contrast bolus. Ferumoxytol has been shown to be safe, even in patients with renal failure. Ferumoxytol has shown promise in a variety of clinical settings. The exquisite resolution enabled by the long intravascular times and lack of background parenchymal enhancement is of particular interest in the vascular imaging of solid organ allografts. Ferumoxytol magnetic resonance angiography (MRA) may identify clinically actionable findings months before ultrasound, CT angiography, or Gadolinium-enhanced MRA. Ferumoxytol MRA is of particular benefit as a troubleshooting tool in the setting of equivocal ultrasound and CT imaging. In the following review, we highlight the use of ferumoxytol for high-resolution MR vascular imaging for abdominal solid organ allografts, with representative cases.

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

ObjectivesBackground 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. MethodsThis 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. ResultsThe 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). ConclusionIt 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.

Abstract 776: Change in background contrast enhancement category on abbreviated breast MRI with administration of 6 months of bazedoxifene and conjugated estrogen vs wait list control

Abstract Background MRI background contrast enhancement (BPE) has found to be a marker of breast cancer risk providing information beyond that provided by assessment of breast density. BPE is qualitatively classified into categories that correspond to BPE estimates as minimal, <25% BPE; mild, 25-50% BPE; moderate, 50-75% BPE; and marked, >75% BPE. We divided minimal into minimal and almost none (≤5%). We conducted a feasibility study of change in BPE after 6 months of the selective estrogen receptor modulator bazedoxifene (BZA) 20 mg + conjugated estrogen (CE) 0.45 mg marketed commercially as Duavee™ for relief of hot flashes and prevention of osteoporosis in postmenopausal women. Methods STUDY00145121 started 5-27-2020 with randomization to 6 months of Duavee™ vs waitlist control but was stopped after 11 entrants because participants were unable to receive follow-up breast MRIs due to COVID and Duavee™ became unavailable. STUDY0014761 was opened in early 2022 when a source of BZA was found. Doses of BZA and CE were the same as Duavee™ but given as two pills instead of one. 16 women have been randomized to 6 months of BZA/CE vs a wait list control between 2-14-2022 and 9-19-2023. Inclusion criteria are postmenopausal women ages 45-65 at increased risk for breast cancer, BMI < 36 kg/m2 and having current vasomotor symptoms. Participants had a blood draw, 3D mammogram, and abbreviated breast MRI at baseline and the studies were repeated at 6 months. Volumetric density was assessed by Volpara™ fully automated software. Research evaluation of abbreviated MRI background parenchymal contrast enhancement (BPE) was assessed by 5 categories as almost none, minimal, mild, moderate, and marked. BPE was evaluated by a single radiologist blinded to treatment assignment and compared to the 4-category clinical assessment. Results A total of 27 women have been entered in these studies. Six were not evaluable from STUDY00145121 because of the inability to perform 6-month MRI. Two women on STUDY0014761 are not endpoint evaluable due to early drop out and 3 have not yet completed study. A total of 16 women have completed the 6-month portion of the study and abbreviated MRI, with median age 55 (range 47-62) and median BMI 26 kg/m2 (20-33 kg/m2). At baseline, BPE were 3 almost none, 4 minimal, 8 mild, 1 moderate, and 0 marked. There was little correlation between absolute baseline mammographic fibroglandular volume and baseline BPE. A shift in BPE category using the 5-category assessment was noted in 6/11 women (all but one decreased) randomized to BZA +CE and 2/5 women (both decreased) on waitlist. The one increase and four categorical decreases would not have been detected without expansion of BPE to a 5-category system. Conclusion Using the 5-category classification, abbreviated MRI BPE may have promise as a response biomarker in prevention trials for postmenopausal high-risk women. Citation Format: Carol J. Fabian, Onilisa Winblad, Amy Kreutzjans, Krystal Pittman, Christy Altman, Kandy R. Powers, Mary McCarthy, Lauren Nye, Bruce F. Kimler. Change in background contrast enhancement category on abbreviated breast MRI with administration of 6 months of bazedoxifene and conjugated estrogen vs wait list control [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 776.

Predicting pathological complete response to neoadjuvant chemotherapy in breast cancer patients: use of MRI radiomics data from three regions with multiple machine learning algorithms

ObjectiveTo construct a multi-region MRI radiomics model for predicting pathological complete response (pCR) in breast cancer (BCa) patients who received neoadjuvant chemotherapy (NACT) and provide a theoretical basis for the peritumoral microenvironment affecting the efficacy of NACT.MethodsA total of 133 BCa patients who received NACT, including 49 with confirmed pCR, were retrospectively analyzed. The radiomics features of the intratumoral region, peritumoral region, and background parenchymal enhancement (BPE) were extracted, and the most relevant features were obtained after dimensional reduction. Then, combining different areas, multivariate logistic regression analysis was used to select the optimal feature set, and six different machine learning models were used to predict pCR. The optimal model was selected, and its performance was evaluated using receiver operating characteristic (ROC) analysis. SHAP analysis was used to examine the relationship between the features of the model and pCR.ResultsFor signatures constructed using three individual regions, BPE provided the best predictions of pCR, and the diagnostic performance of the intratumoral and peritumoral regions improved after adding the BPE signature. The radiomics signature from the combination of all the three regions with the XGBoost machine learning algorithm provided the best predictions of pCR based on AUC (training set: 0.891, validation set: 0.861), sensitivity (training set: 0.882, validation set: 0.800), and specificity (training set: 0.847, validation set: 0.84). SHAP analysis demonstrated that LZ_log.sigma.2.0.mm.3D_glcm_ClusterShade_T12 made the greatest contribution to the predictions of this model.ConclusionThe addition of the BPE MRI signature improved the prediction of pCR in BCa patients who received NACT. These results suggest that the features of the peritumoral microenvironment are related to the efficacy of NACT.

Investigation of imaging features in contrast-enhanced magnetic resonance imaging of benign and malignant breast lesions.

This study aimed to enhance the diagnostic accuracy of contrast-enhanced breast magnetic resonance imaging (MRI) using gadobutrol for differentiating benign breast lesions from malignant ones. Moreover, this study sought to address the limitations of current imaging techniques and criteria based on the Breast Imaging Reporting and Data System (BI-RADS). In a multicenter retrospective study conducted in Japan, 200 women were included, comprising 100 with benign lesions and 100 with malignant lesions, all classified under BI-RADS categories 3 and 4. The MRI protocol included 3D fast gradient echo T1- weighted images with fat suppression, with gadobutrol as the contrast agent. The analysis involved evaluating patient and lesion characteristics, including age, size, location, fibroglandular tissue, background parenchymal enhancement (BPE), signal intensity, and the findings of mass and non-mass enhancement. In this study, univariate and multivariate logistic regression analyses were performed, along with decision tree analysis, to identify significant predictors for the classification of lesions. Differences in lesion characteristics were identified, which may influence malignancy risk. The multivariate logistic regression model revealed age, lesion location, shape, and signal intensity as significant predictors of malignancy. Decision tree analysis identified additional diagnostic factors, including lesion margin and BPE level. The decision tree models demonstrated high diagnostic accuracy, with the logistic regression model showing an area under the curve of 0.925 for masses and 0.829 for non-mass enhancements. This study underscores the importance of integrating patient age, lesion location, and BPE level into the BI-RADS criteria to improve the differentiation between benign and malignant breast lesions. This approach could minimize unnecessary biopsies and enhance clinical decision-making in breast cancer diagnostics, highlighting the effectiveness of gadobutrol in breast MRI evaluations.

Pilot study: A simple CAD-based tool to detect breast cancer on MRI of the breast

PurposeThis pilot-study aims to assess, whether quantitatively assessed enhancing breast tissue as a percentage of the entire breast volume can serve as an indicator of breast cancer at breast MRI and whether the contrast-agent employed affects diagnostic efficacy. MaterialsThis retrospective IRB-approved study, included 39 consecutive patients, that underwent two subsequent breast MRI exams for suspicious findings at conventional imaging with 0.1 mmol/kg gadobenic and gadoteric acid. Two independent readers, blinded to the histopathological outcome, assessed unenhanced and early post-contrast images using computer-assisted software (Brevis, Siemens Healthcare). Diagnostic performance was statistically determined for percentage of ipsilateral voxel volume enhancement and for percentage of contralateral enhancing voxel volume subtracted from ipsilateral enhancing voxel volume after crosstabulation with the dichotomized histological outcome (benign/malignant). ResultsIpsilateral enhancing voxel volume versus histopathological outcome resulted in an AUC of 0.707 and 0.687 for gadobenic acid, reader 1 and 2, respectively and in an AUC of 0.778 and 0.773 for gadoteric acid, reader 1 and 2, respectively. Accounting for background parenchymal enhancement by subtracting contralateral enhancing volume from ipsilateral enhancing voxel volume versus histolopathological outcome resulted in an AUC of 0.793 and 0.843 for gadobenic acid, reader 1 and 2, respectively and in an AUC of 0.692 and 0.662 for gadoteric acid, reader 1 and 2, respectively. Pairwise testing yielded no statistically significant difference both between readers and between contrast agents employed (p > 0.05). ConclusionOur proposed CAD algorithm, which quantitatively assesses enhancing breast tissue as a percentage of the entire breast volume, allows indicating the presence of breast cancer.

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

BackgroundNon-invasive identification of breast cancer (BCa) patients with pathological complete response (pCR) after neoadjuvant chemotherapy (NACT) is critical to determine appropriate surgical strategies and guide the resection range of tumor. This study aimed to examine the effectiveness of a nomogram created by combining radiomics signatures from both intratumoral and derived tissues with clinical characteristics for predicting pCR after NACT.MethodsThe clinical data of 133 BCa patients were analyzed retrospectively and divided into training and validation sets. The radiomics features for Intratumoral, peritumoral, and background parenchymal enhancement (BPE) in the training set were dimensionalized. Logistic regression analysis was used to select the optimal feature set, and a radiomics signature was constructed using a decision tree. The signature was combined with clinical features to build joint models and generate nomograms. The area under curve (AUC) value of receiver operating characteristic (ROC) curve was then used to assess the performance of the nomogram and independent predictors.ResultsAmong single region, intratumoral had the best predictive value. The diagnostic performance of the intratumoral improved after adding the BPE features. The AUC values of the radiomics signature were 0.822 and 0.82 in the training and validation sets. Multivariate logistic regression analysis revealed that age, ER, PR, Ki-67, and radiomics signature were independent predictors of pCR in constructing a nomogram. The AUC of the nomogram in the training and validation sets were 0.947 and 0.933. The DeLong test showed that the nomogram had statistically significant differences compared to other independent predictors in both the training and validation sets (P < 0.05).ConclusionBPE has value in predicting the efficacy of neoadjuvant chemotherapy, thereby revealing the potential impact of tumor growth environment on the efficacy of neoadjuvant chemotherapy.

Machine learning prediction of pathological complete response and overall survival of breast cancer patients in an underserved inner-city population

BackgroundGeneralizability of predictive models for pathological complete response (pCR) and overall survival (OS) in breast cancer patients requires diverse datasets. This study employed four machine learning models to predict pCR and OS up to 7.5 years using data from a diverse and underserved inner-city population.MethodsDemographics, staging, tumor subtypes, income, insurance status, and data from radiology reports were obtained from 475 breast cancer patients on neoadjuvant chemotherapy in an inner-city health system (01/01/2012 to 12/31/2021). Logistic regression, Neural Network, Random Forest, and Gradient Boosted Regression models were used to predict outcomes (pCR and OS) with fivefold cross validation.ResultspCR was not associated with age, race, ethnicity, tumor staging, Nottingham grade, income, and insurance status (p > 0.05). ER−/HER2+ showed the highest pCR rate, followed by triple negative, ER+/HER2+, and ER+/HER2− (all p < 0.05), tumor size (p < 0.003) and background parenchymal enhancement (BPE) (p < 0.01). Machine learning models ranked ER+/HER2−, ER−/HER2+, tumor size, and BPE as top predictors of pCR (AUC = 0.74–0.76). OS was associated with race, pCR status, tumor subtype, and insurance status (p < 0.05), but not ethnicity and incomes (p > 0.05). Machine learning models ranked tumor stage, pCR, nodal stage, and triple-negative subtype as top predictors of OS (AUC = 0.83–0.85). When grouping race and ethnicity by tumor subtypes, neither OS nor pCR were different due to race and ethnicity for each tumor subtype (p > 0.05).ConclusionTumor subtypes and imaging characteristics were top predictors of pCR in our inner-city population. Insurance status, race, tumor subtypes and pCR were associated with OS. Machine learning models accurately predicted pCR and OS.

Assessment of Background Parenchymal Enhancement at Dynamic Contrast-enhanced MRI in Predicting Breast Cancer Recurrence Risk.

Background Background parenchymal enhancement (BPE) at dynamic contrast-enhanced (DCE) MRI of cancer-free breasts increases the risk of developing breast cancer; implications of quantitative BPE in ipsilateral breasts with breast cancer are largely unexplored. Purpose To determine whether quantitative BPE measurements in one or both breasts could be used to predict recurrence risk in women with breast cancer, using the Oncotype DX recurrence score as the reference standard. Materials and Methods This HIPAA-compliant retrospective single-institution study included women diagnosed with breast cancer between January 2007 and January 2012 (development set) and between January 2012 and January 2017 (internal test set). Quantitative BPE was automatically computed using an in-house-developed computer algorithm in both breasts. Univariable logistic regression was used to examine the association of BPE with Oncotype DX recurrence score binarized into high-risk (recurrence score >25) and low- or intermediate-risk (recurrence score ≤25) categories. Models including BPE measures were assessed for their ability to distinguish patients with high risk versus those with low or intermediate risk and the actual recurrence outcome. Results The development set included 127 women (mean age, 58 years ± 10.2 [SD]; 33 with high risk and 94 with low or intermediate risk) with an actual local or distant recurrence rate of 15.7% (20 of 127) at a minimum 10 years of follow-up. The test set included 60 women (mean age, 57.8 years ± 11.6; 16 with high risk and 44 with low or intermediate risk). BPE measurements quantified in both breasts were associated with increased odds of a high-risk Oncotype DX recurrence score (odds ratio range, 1.27-1.66 [95% CI: 1.02, 2.56]; P < .001 to P = .04). Measures of BPE combined with tumor radiomics helped distinguish patients with a high-risk Oncotype DX recurrence score from those with a low- or intermediate-risk score, with an area under the receiver operating characteristic curve of 0.94 in the development set and 0.79 in the test set. For the combined models, the negative predictive values were 0.97 and 0.93 in predicting actual distant recurrence and local recurrence, respectively. Conclusion Ipsilateral and contralateral DCE MRI measures of BPE quantified in patients with breast cancer can help distinguish patients with high recurrence risk from those with low or intermediate recurrence risk, similar to Oncotype DX recurrence score. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Zhou and Rahbar in this issue.

Ultrafast Dynamic Contrast‐Enhanced MRI of the Breast: From Theory to Practice

The development of ultrafast dynamic contrast‐enhanced (UF‐DCE) MRI has occurred in tandem with fast MRI scan techniques, particularly view‐sharing and compressed sensing. Understanding the strengths of each technique and optimizing the relevant parameters are essential to their implementation. UF‐DCE MRI has now shifted from research protocols to becoming a part of clinical scan protocols for breast cancer. UF‐DCE MRI is expected to compensate for the low specificity of abbreviated MRI by adding kinetic information from the upslope of the time‐intensity curve. Because kinetic information from UF‐DCE MRI is obtained from the shape and timing of the initial upslope, various new kinetic parameters have been proposed. These parameters may be associated with receptor status or prognostic markers for breast cancer. In addition to the diagnosis of malignant lesions, more emphasis has been placed on predicting and evaluating treatment response because hyper‐vascularity is linked to the aggressiveness of breast cancers. In clinical practice, it is important to note that breast lesion images obtained from UF‐DCE MRI are slightly different from those obtained by conventional DCE MRI in terms of morphology. A major benefit of using UF‐DCE MRI is avoidance of the marked or moderate background parenchymal enhancement (BPE) that can obscure the target enhancing lesions. BPE is less prominent in the earlier phases of UF‐DCE MRI, which offers better lesion‐to‐noise contrast. The excellent contrast of early‐enhancing vessels provides a key to understanding the detailed pathological structure of tumor‐associated vessels. UF‐DCE MRI is normally accompanied by a large volume of image data for which automated/artificial intelligence‐based processing is expected to be useful. In this review, both the theoretical and practical aspects of UF‐DCE MRI are summarized.Evidence Level5Technical EfficacyStage 2

A background parenchymal enhancement quantification framework of breast magnetic resonance imaging.

Background parenchymal enhancement (BPE) is defined as the enhanced proportion of normal fibroglandular tissue on enhanced magnetic resonance imaging. BPE shows promise as a quantitative imaging biomarker (QIB). However, the lack of consensus among radiologists in their semi-quantitative grading of BPE limits its clinical utility. The main objective of this study was to develop a BPE quantification model according to clinical expertise, with the BPE integral being used as a QIB to incorporate both the volume and intensity of the enhancement metrics. The model was applied to 2,786 cases to compare our quantitative results with radiologists' semi-quantitative BPE grading to evaluate the effectiveness of using the BPE integral as a QIB for analyzing BPE. Comparisons between multiple groups of nonnormally distributed BPE integrals were performed using the Kruskal-Wallis test. Our study found a considerable degree of concordance between our BPE quantitative integral and radiologists' semi-quantitative assessments. Specifically, our research results revealed significant variability in BPE integral attained through the BPE quantification framework among all semi-quantitative BPE grading groups labeled by experienced radiologists, including mild-moderate (P<0.001), mild-marked (P<0.001), and moderate-marked (P<0.001). Furthermore, there was an apparent correlation between BPE integral and BPE grades, with marked BPE displaying the highest BPE integral, followed by moderate BPE, with mild BPE exhibiting the lowest BPE integral value. The study developed and implemented a BPE quantification framework, which incorporated both the volume and intensity of enhancement and which could serve as a QIB for BPE.

Breast Fibroglandular Tissue Segmentation for Automated BPE Quantification With Iterative Cycle-Consistent Semi-Supervised Learning.

Background Parenchymal Enhancement (BPE) quantification in Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) plays a pivotal role in clinical breast cancer diagnosis and prognosis. However, the emerging deep learning-based breast fibroglandular tissue segmentation, a crucial step in automated BPE quantification, often suffers from limited training samples with accurate annotations. To address this challenge, we propose a novel iterative cycle-consistent semi-supervised framework to leverage segmentation performance by using a large amount of paired pre-/post-contrast images without annotations. Specifically, we design the reconstruction network, cascaded with the segmentation network, to learn a mapping from the pre-contrast images and segmentation predictions to the post-contrast images. Thus, we can implicitly use the reconstruction task to explore the inter-relationship between these two-phase images, which in return guides the segmentation task. Moreover, the reconstructed post-contrast images across multiple auto-context modeling-based iterations can be viewed as new augmentations, facilitating cycle-consistent constraints across each segmentation output. Extensive experiments on two datasets with various data distributions show great segmentation and BPE quantification accuracy compared with other state-of-the-art semi-supervised methods. Importantly, our method achieves 11.80 times of quantification accuracy improvement along with 10 times faster, compared with clinical physicians, demonstrating its potential for automated BPE quantification. The code is available at https://github.com/ZhangJD-ong/Iterative-Cycle-consistent-Semi-supervised-Learning-for-fibroglandular-tissue-segmentation.