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Magni V, Cozzi A, Muscogiuri G, Benedek A, Rossini G, Fanizza M, Di Giulio G, Sardanelli F. Background parenchymal enhancement on contrast-enhanced mammography: associations with breast density and patient's characteristics. LA RADIOLOGIA MEDICA 2024; 129:1303-1312. [PMID: 39060886 DOI: 10.1007/s11547-024-01860-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2024] [Accepted: 07/17/2024] [Indexed: 07/28/2024]
Abstract
PURPOSE To evaluate if background parenchymal enhancement (BPE) on contrast-enhanced mammography (CEM), graded according to the 2022 CEM-dedicated Breast Imaging Reporting and Data System (BI-RADS) lexicon, is associated with breast density, menopausal status, and age. METHODS This bicentric retrospective analysis included CEM examinations performed for the work-up of suspicious mammographic findings. Three readers independently and blindly evaluated BPE on recombined CEM images and breast density on low-energy CEM images. Inter-reader reliability was estimated using Fleiss κ. Multivariable binary logistic regression was performed, dichotomising breast density and BPE as low (a/b BI-RADS categories, minimal/mild BPE) and high (c/d BI-RADS categories, moderate/marked BPE). RESULTS A total of 200 women (median age 56.8 years, interquartile range 50.5-65.6, 140/200 in menopause) were included. Breast density was classified as a in 27/200 patients (13.5%), as b in 110/200 (55.0%), as c in 52/200 (26.0%), and as d in 11/200 (5.5%), with moderate inter-reader reliability (κ = 0.536; 95% confidence interval [CI] 0.482-0.590). BPE was minimal in 95/200 patients (47.5%), mild in 64/200 (32.0%), moderate in 25/200 (12.5%), marked in 16/200 (8.0%), with substantial inter-reader reliability (κ = 0.634; 95% CI 0.581-0.686). At multivariable logistic regression, premenopausal status and breast density were significant positive predictors of high BPE, with adjusted odds ratios of 6.120 (95% CI 1.847-20.281, p = 0.003) and 2.416 (95% CI 1.095-5.332, p = 0.029) respectively. CONCLUSION BPE on CEM is associated with well-established breast cancer risk factors, being higher in women with higher breast density and premenopausal status.
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Affiliation(s)
- Veronica Magni
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Luigi Mangiagalli 31, 20133, Milan, Italy.
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono 7, 20122, Milan, Italy.
| | - Andrea Cozzi
- Unit of Radiology, IRCCS Policlinico San Donato, Via Rodolfo Morandi 30, 20097, San Donato Milanese, Italy
- Imaging Institute of Southern Switzerland (IIMSI), Ente Ospedaliero Cantonale, Via Tesserete 46, 6900, Lugano, Switzerland
| | - Giulia Muscogiuri
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono 7, 20122, Milan, Italy
| | - Adrienn Benedek
- Unit of Radiology, IRCCS Policlinico San Donato, Via Rodolfo Morandi 30, 20097, San Donato Milanese, Italy
| | - Gabriele Rossini
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono 7, 20122, Milan, Italy
| | - Marianna Fanizza
- Department of Breast Radiology, Fondazione IRCCS Policlinico San Matteo, Viale Camillo Golgi 19, 27100, Pavia, Italy
| | - Giuseppe Di Giulio
- Department of Breast Radiology, Fondazione IRCCS Policlinico San Matteo, Viale Camillo Golgi 19, 27100, Pavia, Italy
| | - Francesco Sardanelli
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Luigi Mangiagalli 31, 20133, Milan, Italy
- Unit of Radiology, IRCCS Policlinico San Donato, Via Rodolfo Morandi 30, 20097, San Donato Milanese, Italy
- Lega Italiana per la Lotta contro i Tumori (LILT) Milano Monza Brianza, Piazzale Paolo Gorini 22, 20133, Milan, Italy
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Schiaffino S, Cozzi A, Clauser P, Giannotti E, Marino MA, van Nijnatten TJA, Baltzer PAT, Lobbes MBI, Mann RM, Pinker K, Fuchsjäger MH, Pijnappel RM. Current use and future perspectives of contrast-enhanced mammography (CEM): a survey by the European Society of Breast Imaging (EUSOBI). Eur Radiol 2024; 34:5439-5450. [PMID: 38227202 DOI: 10.1007/s00330-023-10574-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Revised: 12/08/2023] [Accepted: 12/16/2023] [Indexed: 01/17/2024]
Abstract
OBJECTIVES To perform a survey among members of the European Society of Breast Imaging (EUSOBI) regarding the use of contrast-enhanced mammography (CEM). METHODS A panel of nine board-certified radiologists developed a 29-item online questionnaire, distributed to all EUSOBI members (inside and outside Europe) from January 25 to March 10, 2023. CEM implementation, examination protocols, reporting strategies, and current and future CEM indications were investigated. Replies were exploratively analyzed with descriptive and non-parametric statistics. RESULTS Among 434 respondents (74.9% from Europe), 50% (217/434) declared to use CEM, 155/217 (71.4%) seeing less than 200 CEMs per year. CEM use was associated with academic settings and high breast imaging workload (p < 0.001). The lack of CEM adoption was most commonly due to the perceived absence of a clinical need (65.0%) and the lack of resources to acquire CEM-capable systems (37.3%). CEM protocols varied widely, but most respondents (61.3%) had already adopted the 2022 ACR CEM BI-RADS® lexicon. CEM use in patients with contraindications to MRI was the most common current indication (80.6%), followed by preoperative staging (68.7%). Patients with MRI contraindications also represented the most commonly foreseen CEM indication (88.0%), followed by the work-up of inconclusive findings at non-contrast examinations (61.5%) and supplemental imaging in dense breasts (53.0%). Respondents declaring CEM use and higher CEM experience gave significantly more current (p = 0.004) and future indications (p < 0.001). CONCLUSIONS Despite a trend towards academic high-workload settings and its prevalent use in patients with MRI contraindications, CEM use and progressive experience were associated with increased confidence in the technique. CLINICAL RELEVANCE STATEMENT In this first survey on contrast-enhanced mammography (CEM) use and perspectives among the European Society of Breast Imaging (EUSOBI) members, the perceived absence of a clinical need chiefly drove the 50% CEM adoption rate. CEM adoption and progressive experience were associated with more extended current and future indications. KEY POINTS • Among the 434 members of the European Society of Breast Imaging who completed this survey, 50% declared to use contrast-enhanced mammography in clinical practice. • Due to the perceived absence of a clinical need, contrast-enhanced mammography (CEM) is still prevalently used as a replacement for MRI in patients with MRI contraindications. • The number of current and future CEM indications marked by respondents was associated with their degree of CEM experience.
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Affiliation(s)
- Simone Schiaffino
- Imaging Institute of Southern Switzerland (IIMSI), Ente Ospedaliero Cantonale (EOC), Via Tesserete 46, 6900, Lugano, Switzerland.
| | - Andrea Cozzi
- Imaging Institute of Southern Switzerland (IIMSI), Ente Ospedaliero Cantonale (EOC), Via Tesserete 46, 6900, Lugano, Switzerland
| | - Paola Clauser
- Department of Biomedical Imaging and Image-guided Therapy, Division of General and Pediatric Radiology, Medical University of Vienna, Vienna, Austria
| | - Elisabetta Giannotti
- Cambridge Breast Unit, Addenbrooke's Cambridge University Hospital NHS Foundation Trust, Cambridge, UK
| | - Maria Adele Marino
- Department of Biomedical Sciences and Morphologic and Functional Imaging, Università degli Studi di Messina, Messina, Italy
| | - Thiemo J A van Nijnatten
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, The Netherlands
- GROW School for Oncology and Reproduction, Maastricht, The Netherlands
| | - Pascal A T Baltzer
- Department of Biomedical Imaging and Image-guided Therapy, Division of General and Pediatric Radiology, Medical University of Vienna, Vienna, Austria
| | - Marc B I Lobbes
- Department of Medical Imaging, Zuyderland Medical Center, Sittard-Geleen, The Netherlands
| | - Ritse M Mann
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Katja Pinker
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Michael H Fuchsjäger
- Division of General Radiology, Department of Radiology, Medical University Graz, Graz, Austria
| | - Ruud M Pijnappel
- Department of Imaging, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands
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Ripaud E, Jailin C, Quintana GI, Milioni de Carvalho P, Sanchez de la Rosa R, Vancamberg L. Deep-learning model for background parenchymal enhancement classification in contrast-enhanced mammography. Phys Med Biol 2024; 69:115013. [PMID: 38657641 DOI: 10.1088/1361-6560/ad42ff] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Accepted: 04/24/2024] [Indexed: 04/26/2024]
Abstract
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 inbreast 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.
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Goh Y, Quek ST, Pillay P, Chou CP. Evaluation of architectural distortion with contrast-enhanced mammography. Clin Radiol 2024; 79:163-169. [PMID: 38114374 DOI: 10.1016/j.crad.2023.11.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 11/20/2023] [Accepted: 11/21/2023] [Indexed: 12/21/2023]
Abstract
Architectural distortion (AD) is the third most common abnormality detected on mammograms. In the absence of an accurate non-invasive tool to evaluate ADs, clinical management often requires surgical excision for histological diagnosis. This problem is expected to worsen with the growing use of digital breast tomosynthesis (DBT) and the resultant increasing detection of ADs. There is therefore a great clinical need for a diagnostic imaging tool to complement non-enhanced mammography for the evaluation of AD. Contrast-enhanced mammography (CEM) is an emerging breast imaging method that uses contrast media and the principle of dual-energy subtraction to evaluate vascularity of suspicious breast lesions. CEM, a cost-effective alternative to breast magnetic resonance imaging (MRI), can be used to evaluate AD by juxtaposing CEM images with non-enhanced mammograms for comparison. In this review, the authors aim to provide readers with an overview of the interpretation of AD on CEM using imaging examples. Relevant imaging features of CEM and their respective significance will be matched with information from a literature review. Finally, the authors would like to highlight the added value of CEM in relevant clinical applications in the assessment of AD.
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Affiliation(s)
- Y Goh
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd 119074, Singapore
| | - S T Quek
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd 119074, Singapore
| | - P Pillay
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd 119074, Singapore
| | - C-P Chou
- Kaohsiung Veterans General Hospital, Radiology Department, No. 386, Dazhong 1st Rd, Zuoying Dist., Kaohsiung City 81362, Taiwan, ROC.
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Xu C, Jiang M, Lin F, Zhang K, Xie H, Lv W, Ji H, Mao N. Qualitative assessments of density and background parenchymal enhancement on contrast-enhanced spectral mammography associated with breast cancer risk in high-risk women. Br J Radiol 2023; 96:20220051. [PMID: 37227804 PMCID: PMC10392639 DOI: 10.1259/bjr.20220051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Revised: 04/26/2023] [Accepted: 05/04/2023] [Indexed: 05/27/2023] Open
Abstract
OBJECTIVE To investigate the correlation between the risk of breast cancer for high-risk females and the density and background parenchymal enhancement (BPE) on contrast-enhanced spectral mammography (CESM). METHODS Females at high-risk, without breast cancer history and received CESM from July 2016 to December 2017 were retrospectively enrolled. The longest follow-up time was 4.5 years, and patients who developed breast cancer with maximized follow-up time were classified as cancer cohort, while females who did not develop breast cancer were categorized as control cohort. These two cohorts were one-to-one matched in age, family and/or genetic history of breast cancer, menopausal status and BRCA status. The density and BPE at CESM imaging were assessed. Conditional logistic regression was applied to evaluate the relationship between imaging features and breast cancer risk. RESULTS During the follow-up interval, 90 women at high-risk without history of breast cancer were newly diagnosed. Compared with minimal BPE, increasing BPE levels were associated with the risk of breast cancer among high-risk females in a time interval of 4.5 years (mild: odds ratio [OR]=3.2, p = 0.001; moderate: OR = 4.0, p = 0.002; marked: OR = 11.2, p < 0.001). In addition, females with mild, moderate or marked BPE were four times more likely to be diagnosed with breast cancer than females with minimal BPE in a time interval of 4.5 years (OR = 4.0, p < 0.001). CONCLUSION Qualitative CESM BPE assessment may be useful in the prediction of breast cancer risk among high-risk females. ADVANCES IN KNOWLEDGE • Qualitative CESM BPE assessment may be useful in the prediction of breast cancer risk among high-risk women during the follow-up period of 4.5 years. • The significance of breast density as an independent risk factor is not fully established for high-risk women during the follow-up period of 4.5 years.
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Affiliation(s)
- Cong Xu
- Physical Examination Center, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, China
| | - Meiping Jiang
- Department of Ultrasound, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, China
| | - Fan Lin
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, China
| | - Kun Zhang
- Department of Breast Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, China
| | - Haizhu Xie
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, China
| | - Wei Lv
- Physical Examination Center, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, China
| | - Haixia Ji
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, China
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Background enhancement in contrast-enhanced spectral mammography (CESM): are there qualitative and quantitative differences between imaging systems? Eur Radiol 2023; 33:2945-2953. [PMID: 36474057 PMCID: PMC10017655 DOI: 10.1007/s00330-022-09238-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 08/15/2022] [Accepted: 10/13/2022] [Indexed: 12/12/2022]
Abstract
OBJECTIVE To evaluate the impact of the digital mammography imaging system on overall background enhancement on recombined contrast-enhanced spectral mammography (CESM) images, the overall background enhancement of two different mammography systems was compared. METHODS In a retrospective single-center study, CESM images of n = 129 female patients who underwent CESM between 2016 and 2019 were analyzed independently by two radiologists. Two mammography machines of different manufacturers were compared qualitatively using a Likert-scale from 1 (minimal) to 4 (marked overall background enhancement) and quantitatively by placing a region of interest and measuring the intensity enhancement. Lesion conspicuity was analyzed using a Likert-scale from 1 (lesion not reliably distinguishable) to 5 (excellent lesion conspicuity). A multivariate regression was performed to test for potential biases on the quantitative results. RESULTS Significant differences in qualitative background enhancement measurements between machines A and B were observed for both readers (p = 0.003 and p < 0.001). The quantitative evaluation showed significant differences in background enhancement with an average difference of 75.69 (99%-CI [74.37, 77.02]; p < 0.001). Lesion conspicuity was better for machine A for the first and second reader respectively (p = 0.009 and p < 0.001). The factor machine was the only influencing factor (p < 0.001). The factors contrast agent, breast density, age, and menstrual cycle could be excluded as potential biases. CONCLUSION Mammography machines seem to significantly influence overall background enhancement qualitatively and quantitatively; thus, an impact on diagnostic accuracy appears possible. KEY POINTS • Overall background enhancement on CESM differs between different vendors qualitatively and quantitatively. • Our retrospective single-center study showed consistent results of the qualitative and quantitative data analysis of overall background enhancement. • Lesion conspicuity is higher in cases of lower background enhancement on CESM.
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Mao N, Zhang H, Dai Y, Li Q, Lin F, Gao J, Zheng T, Zhao F, Xie H, Xu C, Ma H. Attention-based deep learning for breast lesions classification on contrast enhanced spectral mammography: a multicentre study. Br J Cancer 2023; 128:793-804. [PMID: 36522478 PMCID: PMC9977865 DOI: 10.1038/s41416-022-02092-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Revised: 11/21/2022] [Accepted: 11/24/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND This study aims to develop an attention-based deep learning model for distinguishing benign from malignant breast lesions on CESM. METHODS Preoperative CESM images of 1239 patients, which were definitely diagnosed on pathology in a multicentre cohort, were divided into training and validation sets, internal and external test sets. The regions of interest of the breast lesions were outlined manually by a senior radiologist. We adopted three conventional convolutional neural networks (CNNs), namely, DenseNet 121, Xception, and ResNet 50, as the backbone architectures and incorporated the convolutional block attention module (CBAM) into them for classification. The performance of the models was analysed in terms of the receiver operating characteristic (ROC) curve, accuracy, the positive predictive value (PPV), the negative predictive value (NPV), the F1 score, the precision recall curve (PRC), and heat maps. The final models were compared with the diagnostic performance of conventional CNNs, radiomics models, and two radiologists with specialised breast imaging experience. RESULTS The best-performing deep learning model, that is, the CBAM-based Xception, achieved an area under the ROC curve (AUC) of 0.970, a sensitivity of 0.848, a specificity of 1.000, and an accuracy of 0.891 on the external test set, which was higher than those of other CNNs, radiomics models, and radiologists. The PRC and the heat maps also indicated the favourable predictive performance of the attention-based CNN model. The diagnostic performance of two radiologists improved with deep learning assistance. CONCLUSIONS Using an attention-based deep learning model based on CESM images can help to distinguishing benign from malignant breast lesions, and the diagnostic performance of radiologists improved with deep learning assistance.
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Affiliation(s)
- Ning Mao
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, 264000, Yantai, Shandong, P. R. China
- Big Data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Qingdao University, 264000, Yantai, Shandong, P. R. China
| | - Haicheng Zhang
- Big Data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Qingdao University, 264000, Yantai, Shandong, P. R. China
| | - Yi Dai
- Department of Radiology, Peking University Shenzhen Hospital, 518000, Shenzhen, P. R. China
| | - Qin Li
- Department of Radiology, Fudan University Cancer Center, 200433, Shanghai, P. R. China
| | - Fan Lin
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, 264000, Yantai, Shandong, P. R. China
| | - Jing Gao
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, 264000, Yantai, Shandong, P. R. China
| | - Tiantian Zheng
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, 264000, Yantai, Shandong, P. R. China
| | - Feng Zhao
- School of Computer Science and Technology, Shandong Technology and Business University, 264005, Yantai, Shandong, P. R. China
| | - Haizhu Xie
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, 264000, Yantai, Shandong, P. R. China
| | - Cong Xu
- Physical Examination Center, Yantai Yuhuangding Hospital, Qingdao University, 264000, Yantai, Shandong, P. R. China.
| | - Heng Ma
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, 264000, Yantai, Shandong, P. R. China.
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Li D, Makeev A, Glick SJ. 4D digital anthropomorphic breast phantom for iodinated contrast-enhanced imaging. J Med Imaging (Bellingham) 2023; 10:S22403. [PMID: 36910740 PMCID: PMC10005817 DOI: 10.1117/1.jmi.10.s2.s22403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Accepted: 02/13/2023] [Indexed: 03/12/2023] Open
Abstract
Purpose Differentiating between benign and malignant masses is one of the biggest challenges in breast imaging. The challenge is ingrained in the similarity of the attenuation coefficients between different types of lesion tissues and fibroglandular tissues. Contrast-enhanced imaging techniques can take advantage of the differing metabolism in different tissues, therefore, potentially allowing better differentiation of malignant and benign lesions. To facilitate the development and optimization of such technologies, we propose a fully digital 4D phantom that features time-varying enhancement patterns for different tissue types. Approach The 4D model is based on a static, anthropomorphic 3D digital breast phantom. Masses inserted into the 3D phantom are based on a previously published model. Physiological parameters that capture the key characteristics of masses, e.g., wash-in and wash-out rates indicating metabolic level, are employed in the model to simulate fundamental features for categorizing mass types. The two-compartmental model, a well-known model in the field of pharmacokinetics, is used to depict the diffusion process of the contrast agent. Two methods are proposed to allow for the simulations of lesions with necrotic cores of varying shapes and sizes. Results The fourth dimension of the phantom models different time-varying enhancement patterns for different materials including fibroglandular tissue and lesion tissue. Metabolic characteristics of mass models can be adjusted to provide different enhancement patterns. The parameters of the 4D phantom can also be adjusted to fit different scenarios. The usage of the phantom is demonstrated by simulating mammograms at different time frames. Conclusion A 4D digital anthropomorphic breast phantom that models different time-varying contrast enhancement patterns is presented. This phantom could be an integral tool for use in in silico trials to assess image quality of iodinated contrast-enhanced mammography, digital breast tomosynthesis, and breast computed tomography systems.
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Affiliation(s)
- Dan Li
- Food and Drug Administration, Silver Spring, Maryland, United States
| | - Andrey Makeev
- Food and Drug Administration, Silver Spring, Maryland, United States
| | - Stephen J. Glick
- Food and Drug Administration, Silver Spring, Maryland, United States
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Yuen S, Monzawa S, Gose A, Yanai S, Yata Y, Matsumoto H, Ichinose Y, Tashiro T, Yamagami K. Impact of background parenchymal enhancement levels on the diagnosis of contrast-enhanced digital mammography in evaluations of breast cancer: comparison with contrast-enhanced breast MRI. Breast Cancer 2022; 29:677-687. [DOI: 10.1007/s12282-022-01345-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 02/13/2022] [Indexed: 11/28/2022]
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Lester SP, Vegunta S. Influence of Menopausal Hormone Therapy on the Breast: Counseling Your Patients Before You Prescribe. J Womens Health (Larchmt) 2021; 31:167-170. [PMID: 34788572 DOI: 10.1089/jwh.2021.0322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Menopausal hormone therapy (HT) aims to improve a woman's quality of life by treating bothersome menopausal symptoms associated with low estrogen levels. Although HT is prescribed to millions of women worldwide, its breast-related adverse effects have always been a concern. Some of the common adverse effects of HT are breast fullness, increased breast density, and increased breast cancer (BC) risk. Health care professionals need to be aware of the influence of HT on breast tissue to provide appropriate counseling as part of informed decision making. Our review summarizes the influence of HT on breast symptoms, breast density, mammograms, and BC risk.
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Affiliation(s)
- Sara P Lester
- Division of General Internal Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Suneela Vegunta
- Division of Women's Health Internal Medicine, Mayo Clinic, Scottsdale, Arizona, USA
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Berg WA, Bandos AI, Zuley ML, Waheed UX. Training Radiologists to Interpret Contrast-enhanced Mammography: Toward a Standardized Lexicon. JOURNAL OF BREAST IMAGING 2021; 3:176-189. [PMID: 38424825 DOI: 10.1093/jbi/wbaa115] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Accepted: 12/05/2020] [Indexed: 03/02/2024]
Abstract
OBJECTIVE Using terms adapted from the BI-RADS Mammography and MRI lexicons, we trained radiologists to interpret contrast-enhanced mammography (CEM) and assessed reliability of their description and assessment. METHODS A 60-minute presentation on CEM and terminology was reviewed independently by 21 breast imaging radiologist observers. For 21 CEM exams with 31 marked findings, observers recorded background parenchymal enhancement (BPE) (minimal, mild, moderate, marked), lesion type (oval/round or irregular mass, or non-mass enhancement), intensity of enhancement (none, weak, medium, strong), enhancement quality (none, homogeneous, heterogeneous, rim), and BI-RADS assessment category (2, 3, 4A, 4B, 4C, 5). "Expert" consensus of 3 other radiologists experienced in CEM was developed. Kappa statistic was used to assess agreement between radiologists and expert consensus, and between radiologists themselves, on imaging feature categories and final assessments. Reproducibility of specific feature descriptors was assessed as fraction of consensus-concordant responses. RESULTS Radiologists demonstrated moderate agreement for BPE, (mean kappa, 0.43; range, 0.05-0.69), and lowest reproducibility for "minimal." Agreement was substantial for lesion type (mean kappa, 0.70; range, 0.47-0.93), moderate for intensity of enhancement (mean kappa, 0.57; range, 0.44-0.76), and moderate for enhancement quality (mean kappa, 0.59; range, 0.20-0.78). Agreement on final assessment was fair (mean kappa, 0.26; range, 0.09-0.44), with BI-RADS category 3 the least reproducible. Decision to biopsy (BI-RADS 2-3 vs 4-5) showed moderate agreement with consensus (mean kappa, 0.54; range, -0.06-0.87). CONCLUSION With minimal training, agreement for description of CEM findings by breast imaging radiologists was comparable to other BI-RADS lexicons.
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Affiliation(s)
- Wendie A Berg
- University of Pittsburgh School of Medicine, Magee-Womens Hospital of UPMC, Department of Radiology, Pittsburgh, PA
| | - Andriy I Bandos
- University of Pittsburgh Graduate School of Public Health, Department of Biostatistics, Pittsburgh, PA
| | - Margarita L Zuley
- University of Pittsburgh School of Medicine, Magee-Womens Hospital of UPMC, Department of Radiology, Pittsburgh, PA
| | - Uzma X Waheed
- University of Pittsburgh School of Medicine, Magee-Womens Hospital of UPMC, Department of Radiology, Pittsburgh, PA
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The effectiveness of contrast-enhanced spectral mammography and magnetic resonance imaging in dense breasts. Pol J Radiol 2021; 86:e159-e164. [PMID: 33828627 PMCID: PMC8018270 DOI: 10.5114/pjr.2021.104834] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Accepted: 08/26/2020] [Indexed: 11/17/2022] Open
Abstract
Purpose Breast cancer is the most common cause of death from neoplastic disease in women. Among all breast anatomy types, glandular type is the most problematic concerning evaluation. While digital mammography still remains the basic diagnostic tool, one must be aware of its limitations in dense breasts. Although magnetic resonance imaging (MRI) has greatly improved sensitivity, its specificity is low. Moreover, there are contraindications for MRI for some patients, so a substitute has been searched for. This study was performed to check if contrast-enhanced spectral mammography (CESM) can be a viable option for patients with dense breasts. Material and methods The study involved 121 patients with abnormalities detected on base-line diagnostic imaging (ultrasound or mammography). The patients had subsequent examinations, both CESM and MRI performed within a maximum 2-month time interval. The sensitivity and specificity of both methods in the whole group as well as in specific breast structure types were measured and compared. Results Contrast enhancement was visible in all 121 cases on MRI, while on CESM lack of enhancement was noted in 13 cases. All of those 13 lesions turned out to be benign. There were 40 (33%) benign and 81 (69%) malignant tumours. The analysed group included 53 (44%) glandular type breast patients, 39 (32%) mixed type, and 29 (23%) fatty type. Although MRI proved to be slightly more effective in dense breasts, both methods showed similar results in the whole study group. Conclusion CESM can be used with confidence in patients with glandular breast type when MRI is not available or there are reported contraindications to MRI.
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