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    Contrast-Enhanced Spectral Mammography-Based Radiomics Nomogram for the Prediction of Neoadjuvant Chemotherapy-Insensitive Breast Cancers. Wang Zhongyi,Lin Fan,Ma Heng,Shi Yinghong,Dong Jianjun,Yang Ping,Zhang Kun,Guo Na,Zhang Ran,Cui Jingjing,Duan Shaofeng,Mao Ning,Xie Haizhu Frontiers in oncology Purpose:We developed and validated a contrast-enhanced spectral mammography (CESM)-based radiomics nomogram to predict neoadjuvant chemotherapy (NAC)-insensitive breast cancers prior to treatment. Methods:We enrolled 117 patients with breast cancer who underwent CESM examination and NAC treatment from July 2017 to April 2019. The patients were grouped randomly into a training set (n = 97) and a validation set (n = 20) in a ratio of 8:2. 792 radiomics features were extracted from CESM images including low-energy and recombined images for each patient. Optimal radiomics features were selected by using analysis of variance (ANOVA) and least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation, to develop a radiomics score in the training set. A radiomics nomogram incorporating the radiomics score and independent clinical risk factors was then developed using multivariate logistic regression analysis. With regard to discrimination and clinical usefulness, radiomics nomogram was evaluated using the area under the receiver operator characteristic (ROC) curve (AUC) and decision curve analysis (DCA). Results:The radiomics nomogram that incorporates 11 radiomics features and 3 independent clinical risk factors, including Ki-67 index, background parenchymal enhancement (BPE) and human epidermal growth factor receptor-2 (HER-2) status, showed an encouraging discrimination power with AUCs of 0.877 [95% confidence interval (CI) 0.816 to 0.924] and 0.81 (95% CI 0.575 to 0.948) in the training and validation sets, respectively. DCA revealed the increased clinical usefulness of this nomogram. Conclusion:The proposed radiomics nomogram that integrates CESM-derived radiomics features and clinical parameters showed potential feasibility for predicting NAC-insensitive breast cancers. 10.3389/fonc.2021.605230
    Fully Automatic Assessment of Background Parenchymal Enhancement on Breast MRI Using Machine-Learning Models. Nam Yoonho,Park Ga Eun,Kang Junghwa,Kim Sung Hun Journal of magnetic resonance imaging : JMRI BACKGROUND:Automated measurement and classification models with objectivity and reproducibility are required for accurate evaluation of the breast cancer risk of fibroglandular tissue (FGT) and background parenchymal enhancement (BPE). PURPOSE:To develop and evaluate a machine-learning algorithm for breast FGT segmentation and BPE classification. STUDY TYPE:Retrospective. POPULATION:A total of 794 patients with breast cancer, 594 patients assigned to the development set, and 200 patients to the test set. FIELD STRENGTH/SEQUENCE:3T and 1.5T; T -weighted, fat-saturated T -weighted (T W) with dynamic contrast enhancement (DCE). ASSESSMENT:Manual segmentation was performed for the whole breast and FGT regions in the contralateral breast. The BPE region was determined by thresholding using the subtraction of the pre- and postcontrast T W images and the segmented FGT mask. Two radiologists independently assessed the categories of FGT and BPE. A deep-learning-based algorithm was designed to segment and measure the volume of whole breast and FGT and classify the grade of BPE. STATISTICAL TESTS:Dice similarity coefficients (DSC) and Spearman correlation analysis were used to compare the volumes from the manual and deep-learning-based segmentations. Kappa statistics were used for agreement analysis. Comparison of area under the receiver operating characteristic (ROC) curves (AUC) and F1 scores were calculated to evaluate the performance of BPE classification. RESULTS:The mean (±SD) DSC for manual and deep-learning segmentations was 0.85 ± 0.11. The correlation coefficient for FGT volume from manual- and deep-learning-based segmentations was 0.93. Overall accuracy of manual segmentation and deep-learning segmentation in BPE classification task was 66% and 67%, respectively. For binary categorization of BPE grade (minimal/mild vs. moderate/marked), overall accuracy increased to 91.5% in manual segmentation and 90.5% in deep-learning segmentation; the AUC was 0.93 in both methods. DATA CONCLUSION:This deep-learning-based algorithm can provide reliable segmentation and classification results for BPE. LEVEL OF EVIDENCE:3 TECHNICAL EFFICACY STAGE: 2. 10.1002/jmri.27429
    Possible Breast Cancer Risk Related to Background Parenchymal Enhancement at Breast MRI: A Meta-Analysis Study. Zhang Hui,Guo Lili,Tao Weijing,Zhang Jiandong,Zhu Yan,Abdelrahim Mohamed E A,Bai Genji Nutrition and cancer BACKGROUND:The higher level of background parenchymal enhancement (BPE) at breast magnetic resonance imaging (MRI) has drawn considerable attention in the early detection and prediction of breast cancer. It has been reported that there is a possible relationship between the level of BPE at breast MRI and the presence of breast cancer. This meta-analysis was performed to evaluate this relationship. METHODS:Through a systematic literature search up to December 2019, 12 studies with 9541 females, 3870 of them were breast cancer. They were identified reporting relationships between breast cancer and BPE at breast MRI with its different categories (10 related to minimal or mild BPE, eight related to moderate BPE and nine related to high BPE). Odd ratio(OR) with 95% confidence intervals (CIs) was calculated comparing breast cancer prevalence and BPE at breast MRI using dichotomous method with a random or fixed effect model. RESULTS:Females with high (OR, 2.93; 95% CI, 1.24-6.88) and moderate (OR, 2.89; 95% CI, 1.51-5.52) BPE at breast MRI was related with high odds to breast cancer compared to control females. However, females with minimal or mild BPE at breast MRI (OR, 1.33; 95% CI, 0.56-3.17) did not have such risk on breast cancer. The impact of BPE on breast cancer may have a great influence as a tool for improving early detection and prevention of breast cancer. CONCLUSIONS:Based on this meta-analysis, females with high or moderate BPE at breast MRI may have an independent relationship with the risk of breast cancer. This relationship forces us to recommend follow up with those with high or moderate BPE at breast MRI to avoid any complication. 10.1080/01635581.2020.1795211
    Comparison of Segmentation Methods in Assessing Background Parenchymal Enhancement as a Biomarker for Response to Neoadjuvant Therapy. Nguyen Alex Anh-Tu,Arasu Vignesh A,Strand Fredrik,Li Wen,Onishi Natsuko,Gibbs Jessica,Jones Ella F,Joe Bonnie N,Esserman Laura J,Newitt David C,Hylton Nola M Tomography (Ann Arbor, Mich.) Breast parenchymal enhancement (BPE) has shown association with breast cancer risk and response to neoadjuvant treatment. However, BPE quantification is challenging, and there is no standardized segmentation method for measurement. We investigated the use of a fully automated breast fibroglandular tissue segmentation method to calculate BPE from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for use as a predictor of pathologic complete response (pCR) following neoadjuvant treatment in the I-SPY 2 TRIAL. In this trial, patients had DCE-MRI at baseline (T0), after 3 weeks of treatment (T1), after 12 weeks of treatment and between drug regimens (T2), and after completion of treatment (T3). A retrospective analysis of 2 cohorts was performed: one with 735 patients and another with a final cohort of 340 patients, meeting a high-quality benchmark for segmentation. We evaluated 3 subvolumes of interest segmented from bilateral T1-weighted axial breast DCE-MRI: full stack (all axial slices), half stack (center 50% of slices), and center 5 slices. The differences between methods were assessed, and a univariate logistic regression model was implemented to determine the predictive performance of each segmentation method. The results showed that the half stack method provided the best compromise between sampling error from too little tissue and inclusion of incorrectly segmented tissues from extreme superior and inferior regions. Our results indicate that BPE calculated using the half stack segmentation approach has potential as an early biomarker for response to treatment in the hormone receptor-negative and human epidermal growth factor receptor 2-positive subtype. 10.18383/j.tom.2020.00009
    Background parenchymal enhancement on breast MRI: A comprehensive review. Liao Geraldine J,Henze Bancroft Leah C,Strigel Roberta M,Chitalia Rhea D,Kontos Despina,Moy Linda,Partridge Savannah C,Rahbar Habib Journal of magnetic resonance imaging : JMRI The degree of normal fibroglandular tissue that enhances on breast MRI, known as background parenchymal enhancement (BPE), was initially described as an incidental finding that could affect interpretation performance. While BPE is now established to be a physiologic phenomenon that is affected by both endogenous and exogenous hormone levels, evidence supporting the notion that BPE frequently masks breast cancers is limited. However, compelling data have emerged to suggest BPE is an independent marker of breast cancer risk and breast cancer treatment outcomes. Specifically, multiple studies have shown that elevated BPE levels, measured qualitatively or quantitatively, are associated with a greater risk of developing breast cancer. Evidence also suggests that BPE could be a predictor of neoadjuvant breast cancer treatment response and overall breast cancer treatment outcomes. These discoveries come at a time when breast cancer screening and treatment have moved toward an increased emphasis on targeted and individualized approaches, of which the identification of imaging features that can predict cancer diagnosis and treatment response is an increasingly recognized component. Historically, researchers have primarily studied quantitative tumor imaging features in pursuit of clinically useful biomarkers. However, the need to segment less well-defined areas of normal tissue for quantitative BPE measurements presents its own unique challenges. Furthermore, there is no consensus on the optimal timing on dynamic contrast-enhanced MRI for BPE quantitation. This article comprehensively reviews BPE with a particular focus on its potential to increase precision approaches to breast cancer risk assessment, diagnosis, and treatment. It also describes areas of needed future research, such as the applicability of BPE to women at average risk, the biological underpinnings of BPE, and the standardization of BPE characterization. Level of Evidence: 3 Technical Efficacy Stage: 5 J. Magn. Reson. Imaging 2020;51:43-61. 10.1002/jmri.26762
    Differences Between Ipsilateral and Contralateral Early Parenchymal Enhancement Kinetics Predict Response of Breast Cancer to Neoadjuvant Therapy. Ren Zhen,Pineda Federico D,Howard Frederick M,Hill Elle,Szasz Teodora,Safi Rabia,Medved Milica,Nanda Rita,Yankeelov Thomas E,Abe Hiroyuki,Karczmar Gregory S Academic radiology RATIONALE AND OBJECTIVES:To determine whether kinetics measured with ultrafast dynamic contrast-enhanced magnetic resonance imaging in tumor and normal parenchyma pre- and post-neoadjuvant therapy (NAT) can predict the response of breast cancer to NAT. MATERIALS AND METHODS:Twenty-four patients with histologically confirmed invasive breast cancer were enrolled. They were scanned with ultrafast dynamic contrast-enhanced magnetic resonance imaging (3-7 seconds/frame) pre- and post-NAT. Four kinetic parameters were calculated in the segmented tumors, and ipsi- and contra-lateral normal parenchyma: (1) tumor (tSE30) or background parenchymal relative enhancement at 30 seconds (BPE30), (2) maximum relative enhancement slope (MaxSlope), (3) bolus arrival time (BAT), and (4) area under relative signal enhancement curve for the initial 30 seconds (AUC30). The tumor kinetics and the differences between ipsi- and contra-lateral parenchymal kinetics were compared for patients achieving pathologic complete response (pCR) vs those who had residual disease after NAT. The chi-squared test and two-sided t-test were used for baseline demographics. The Wilcoxon rank sum test and one-way analysis of variance were used for differential responses to therapy. RESULTS:Patients with similar pre-NAT mean BPE30, median BAT and mean AUC30 in the ipsi- and contralateral normal parenchyma were more likely to achieve pCR following NAT (p < 0.02). Patients classified as having residual cancer burden (RCB) II after NAT showed higher post-NAT tSE30 and tumor AUC30 and higher post-NAT MaxSlope in ipsilateral normal parenchyma compared to those classified as RCB I or pCR (p < 0.05). CONCLUSION:Bilateral asymmetry in normal parenchyma could predict treatment outcome prior to NAT. Post-NAT tumor kinetics could evaluate the aggressiveness of residual tumor. 10.1016/j.acra.2022.02.008
    Multiparametric Integrated F-FDG PET/MRI-Based Radiomics for Breast Cancer Phenotyping and Tumor Decoding. Umutlu Lale,Kirchner Julian,Bruckmann Nils Martin,Morawitz Janna,Antoch Gerald,Ingenwerth Marc,Bittner Ann-Kathrin,Hoffmann Oliver,Haubold Johannes,Grueneisen Johannes,Quick Harald H,Rischpler Christoph,Herrmann Ken,Gibbs Peter,Pinker-Domenig Katja Cancers BACKGROUND:This study investigated the performance of simultaneous F-FDG PET/MRI of the breast as a platform for comprehensive radiomics analysis for breast cancer subtype analysis, hormone receptor status, proliferation rate and lymphonodular and distant metastatic spread. METHODS:One hundred and twenty-four patients underwent simultaneous F-FDG PET/MRI. Breast tumors were segmented and radiomic features were extracted utilizing CERR software following the IBSI guidelines. LASSO regression was employed to select the most important radiomics features prior to model development. Five-fold cross validation was then utilized alongside support vector machines, resulting in predictive models for various combinations of imaging data series. RESULTS:The highest AUC and accuracy for differentiation between luminal A and B was achieved by all MR sequences (AUC 0.98; accuracy 97.3). The best results in AUC for prediction of hormone receptor status and proliferation rate were found based on all MR and PET data (ER AUC 0.87, PR AUC 0.88, Ki-67 AUC 0.997). PET provided the best determination of grading (AUC 0.71), while all MR and PET analyses yielded the best results for lymphonodular and distant metastatic spread (0.81 and 0.99, respectively). CONCLUSION:F-FDG PET/MRI enables comprehensive high-quality radiomics analysis for breast cancer phenotyping and tumor decoding, utilizing the perks of simultaneously acquired morphologic, functional and metabolic data. 10.3390/cancers13122928
    Response Predictivity to Neoadjuvant Therapies in Breast Cancer: A Qualitative Analysis of Background Parenchymal Enhancement in DCE-MRI. La Forgia Daniele,Vestito Angela,Lasciarrea Maurilia,Comes Maria Colomba,Diotaiuti Sergio,Giotta Francesco,Latorre Agnese,Lorusso Vito,Massafra Raffaella,Palmiotti Gennaro,Rinaldi Lucia,Signorile Rahel,Gatta Gianluca,Fanizzi Annarita Journal of personalized medicine BACKGROUND:For assessing the predictability of oncology neoadjuvant therapy results, the background parenchymal enhancement (BPE) parameter in breast magnetic resonance imaging (MRI) has acquired increased interest. This work aims to qualitatively evaluate the BPE parameter as a potential predictive marker for neoadjuvant therapy. METHOD:Three radiologists examined, in triple-blind modality, the MRIs of 80 patients performed before the start of chemotherapy, after three months from the start of treatment, and after surgery. They identified the portion of fibroglandular tissue (FGT) and BPE of the contralateral breast to the tumor in the basal control pre-treatment (baseline). RESULTS:We observed a reduction of BPE classes in serial MRI checks performed during neoadjuvant therapy, as compared to baseline pre-treatment conditions, in 61.3% of patients in the intermediate step, and in 86.7% of patients in the final step. BPE reduction was significantly associated with sequential anthracyclines/taxane administration in the first cycle of neoadjuvant therapy compared to anti-HER2 containing therapies. The therapy response was also significantly related to tumor size. There were no associations with menopausal status, fibroglandular tissue (FGT) amount, age, BPE baseline, BPE in intermediate, and in the final MRI step. CONCLUSIONS:The measured variability of this parameter during therapy could predict therapy effectiveness in early stages, improving decision-making in the perspective of personalized medicine. Our preliminary results suggest that BPE may represent a predictive factor in response to neoadjuvant therapy in breast cancer, warranting future investigations in conjunction with radiomics. 10.3390/jpm11040256
    Apparent diffusion coefficient mapping using diffusion-weighted MRI: impact of background parenchymal enhancement, amount of fibroglandular tissue and menopausal status on breast cancer diagnosis. Horvat Joao V,Durando Manuela,Milans Soledad,Patil Sujata,Massler Jessica,Gibbons Girard,Giri Dilip,Pinker Katja,Morris Elizabeth A,Thakur Sunitha B European radiology OBJECTIVES:To investigate the impact of background parenchymal enhancement (BPE), amount of fibroglandular tissue (FGT) and menopausal status on apparent diffusion coefficient (ADC) values in differentiation between malignant and benign lesions. METHODS:In this HIPAA-compliant study, mean ADC values of 218 malignant and 130 benign lesions from 288 patients were retrospectively evaluated. The differences in mean ADC values between benign and malignant lesions were calculated within groups stratified by BPE level (high/low), amount of FGT (dense/non-dense) and menopausal status (premenopausal/postmenopausal). Sensitivities and specificities for distinguishing malignant from benign lesions within different groups were compared for statistical significance. RESULTS:The mean ADC value for malignant lesions was significantly lower compared to that for benign lesions (1.07±0.21 x 10 mm/s vs. 1.53±0.26 x 10 mm/s) (p<0.0001). Using the optimal cut-off point of 1.30 x 10 mm/s, an area under the curve of 0.918 was obtained, with sensitivity and specificity both of 87 %. There was no statistically significant difference in sensitivities and specificities of ADC values between different groups stratified by BPE level, amount of FGT or menopausal status. CONCLUSIONS:Differentiation between benign and malignant lesions on ADC values is not significantly affected by BPE level, amount of FGT or menopausal status. KEY POINTS:• ADC allows differentiation between benign and malignant lesions. • ADC is useful for breast cancer diagnosis despite different patient characteristics. • BPE, FGT or menopause do not significantly affect sensitivity and specificity. 10.1007/s00330-017-5202-4