Dataset Information

Histopathologic characteristics of background parenchymal enhancement (BPE) on breast MRI.

ABSTRACT: PURPOSE:Breast fibroglandular tissue (FGT), as visualized on a mammogram (mammographic density, MD), is one of the strongest known risk factors for breast cancer. FGT is also visible on breast MRI, and increased background parenchymal enhancement (BPE) in the FGT has been identified as potentially a major breast cancer risk factor. The aim of this exploratory study was to examine the biologic basis of BPE. METHODS:We examined the unaffected contra-lateral breast of 80 breast cancer patients undergoing a prophylactic mastectomy before any treatment other than surgery of their breast cancer. BPE was classified on the BI-RADS scale (minimal/mild/moderate/marked). Slides were stained for microvessel density (MVD), CD34 (another measure of endothelial density), glandular tissue within the FGT and VEGF. Spearman correlations were used to evaluate the associations between BPE and these pathologic variables. RESULTS:In pre-menopausal patients, BPE was highly correlated with MVD, CD34 and glandular concentration within the FGT, and the pathologic variables were themselves highly correlated. The expression of VEGF was effectively confined to terminal duct lobular unit (TDLU) epithelium. The same relationships of the four pathologic variables with BPE were seen in post-menopausal patients, but the relationships were much weaker and not statistically significant. CONCLUSION:The strong correlation of BPE and MVD together with the high correlation of MVD with glandular concentration seen in pre-menopausal patients indicates that increased breast cancer risk associated with BPE in pre-menopausal women is likely to result from its association with increased concentration of glandular tissue in the FGT. The effective confinement of VEGF expression to the TDLUs shows that the signal for MVD growth arises directly from the glandular tissue. Further studies are needed to understand the basis of BPE in post-menopausal women.

SUBMITTER: Sung JS

PROVIDER: S-EPMC6697118 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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Histopathologic characteristics of background parenchymal enhancement (BPE) on breast MRI.

Sung Janice S JS Corben Adriana D AD Brooks Jennifer D JD Edelweiss Marcia M Keating Delia M DM Lin Christine C Morris Elizabeth A EA Patel Prusha P Robson Mark M Woods Meghan M Bernstein Jonine L JL Pike Malcolm C MC

Breast cancer research and treatment 20180823 2

<h4>Purpose</h4>Breast fibroglandular tissue (FGT), as visualized on a mammogram (mammographic density, MD), is one of the strongest known risk factors for breast cancer. FGT is also visible on breast MRI, and increased background parenchymal enhancement (BPE) in the FGT has been identified as potentially a major breast cancer risk factor. The aim of this exploratory study was to examine the biologic basis of BPE.<h4>Methods</h4>We examined the unaffected contra-lateral breast of 80 breast cance ...[more]

PMID: 30140962

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Project description:BackgroundBackground parenchymal enhancement (BPE) on breast magnetic resonance imaging (MRI) may be associated with breast cancer risk, but previous studies of the association are equivocal and limited by incomplete blinding of BPE assessment. In this study, we evaluated the association between BPE and breast cancer based on fully blinded assessments of BPE in the unaffected breast.MethodsThe Imaging and Epidemiology (IMAGINE) study is a multicenter breast cancer case-control study of women receiving diagnostic, screening, or follow-up breast MRI, recruited from three comprehensive cancer centers in the USA. Cases had a first diagnosis of unilateral breast cancer and controls had no history of or current breast cancer. A single board-certified breast radiologist with 12 years' experience, blinded to case-control status and clinical information, assessed the unaffected breast for BPE without view of the affected breast of cases (or the corresponding breast laterality of controls). The association between BPE and breast cancer was estimated by multivariable logistic regression separately for premenopausal and postmenopausal women.ResultsThe analytic dataset included 835 cases and 963 controls. Adjusting for fibroglandular tissue (breast density), age, race/ethnicity, BMI, parity, family history of breast cancer, BRCA1/BRCA2 mutations, and other confounders, moderate/marked BPE (vs minimal/mild BPE) was associated with breast cancer among premenopausal women [odds ratio (OR) 1.49, 95% CI 1.05-2.11; p = 0.02]. Among postmenopausal women, mild/moderate/marked vs minimal BPE had a similar, but statistically non-significant, association with breast cancer (OR 1.45, 95% CI 0.92-2.27; p = 0.1).ConclusionsBPE is associated with breast cancer in premenopausal women, and possibly postmenopausal women, after adjustment for breast density and confounders. Our results suggest that BPE should be evaluated alongside breast density for inclusion in models predicting breast cancer risk.

Project description:BackgroundPreliminary work has demonstrated that background parenchymal enhancement (BPE) assessed by radiologists is predictive of future breast cancer in women undergoing high-risk screening MRI. Algorithmically assessed measures of BPE offer a more precise and reproducible means of measuring BPE than human readers and thus might improve the predictive performance of future cancer development.PurposeTo determine if algorithmically extracted imaging features of BPE on screening breast MRI in high-risk women are associated with subsequent development of cancer.Study typeCase-control study.PopulationIn all, 133 women at high risk for developing breast cancer; 46 of these patients developed breast cancer subsequently over a follow-up period of 2 years.Field strength/sequence5 T or 3.0 T T1 -weighted precontrast fat-saturated and nonfat-saturated sequences and postcontrast nonfat-saturated sequences.AssessmentAutomatic features of BPE were extracted with a computer algorithm. Subjective BPE scores from five breast radiologists (blinded to clinical outcomes) were also available.Statistical testsLeave-one-out crossvalidation for a multivariate logistic regression model developed using the automatic features and receiver operating characteristic (ROC) analysis were performed to calculate the area under the curve (AUC). Comparison of automatic features and subjective features was performed using a generalized regression model and the P-value was obtained. Odds ratios for automatic and subjective features were compared.ResultsThe multivariate model discriminated patients who developed cancer from the patients who did not, with an AUC of 0.70 (95% confidence interval: 0.60-0.79, P < 0.001). The imaging features remained independently predictive of subsequent development of cancer (P < 0.003) when compared with the subjective BPE assessment of the readers.Data conclusionAutomatically extracted BPE measurements may potentially be used to further stratify risk in patients undergoing high-risk screening MRI.Level of evidence3 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2019;50:456-464.

Project description:ObjectivesTo determine the added discriminative value of detailed quantitative characterization of background parenchymal enhancement in addition to the tumor itself on dynamic contrast-enhanced (DCE) MRI at 3.0 Tesla in identifying "triple-negative" breast cancers.Materials and methodsIn this Institutional Review Board-approved retrospective study, DCE-MRI of 84 women presenting 88 invasive carcinomas were evaluated by a radiologist and analyzed using quantitative computer-aided techniques. Each tumor and its surrounding parenchyma were segmented semi-automatically in 3-D. A total of 85 imaging features were extracted from the two regions, including morphologic, densitometric, and statistical texture measures of enhancement. A small subset of optimal features was selected using an efficient sequential forward floating search algorithm. To distinguish triple-negative cancers from other subtypes, we built predictive models based on support vector machines. Their classification performance was assessed with the area under receiver operating characteristic curve (AUC) using cross-validation.ResultsImaging features based on the tumor region achieved an AUC of 0.782 in differentiating triple-negative cancers from others, in line with the current state of the art. When background parenchymal enhancement features were included, the AUC increased significantly to 0.878 (p<0.01). Similar improvements were seen in nearly all subtype classification tasks undertaken. Notably, amongst the most discriminating features for predicting triple-negative cancers were textures of background parenchymal enhancement.ConclusionsConsidering the tumor as well as its surrounding parenchyma on DCE-MRI for radiomic image phenotyping provides useful information for identifying triple-negative breast cancers. Heterogeneity of background parenchymal enhancement, characterized by quantitative texture features on DCE-MRI, adds value to such differentiation models as they are strongly associated with the triple-negative subtype. Prospective validation studies are warranted to confirm these findings and determine potential implications.

Dataset Information

Histopathologic characteristics of background parenchymal enhancement (BPE) on breast MRI.

Publications

Histopathologic characteristics of background parenchymal enhancement (BPE) on breast MRI.

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