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Chikarmane SA, Smith S. Background Parenchymal Enhancement: A Comprehensive Update. Radiol Clin North Am 2024; 62:607-617. [PMID: 38777537 DOI: 10.1016/j.rcl.2023.12.013] [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: 05/25/2024]
Abstract
Breast MR imaging is a complementary screening tool for patients at high risk for breast cancer and has been used in the diagnostic setting. Normal enhancement of breast tissue on MR imaging is called breast parenchymal enhancement (BPE), which occurs after administration of an intravenous contrast agent. BPE varies widely due to menopausal status, use of exogenous hormones, and breast cancer treatment. Degree of BPE has also been shown to influence breast cancer risk and may predict treatment outcomes. The authors provide a comprehensive update on BPE with review of the recent literature.
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Affiliation(s)
- Sona A Chikarmane
- Breast Imaging Division, Department of Radiology, Brigham and Women's Hospital, 75 Francis Street, Boston, MA 02115, USA.
| | - Sharon Smith
- Department of Radiology, Brigham and Women's Hospital, 75 Francis Street, Boston, MA 02115, USA
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2
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Shamir SB, Sasson AL, Margolies LR, Mendelson DS. New Frontiers in Breast Cancer Imaging: The Rise of AI. Bioengineering (Basel) 2024; 11:451. [PMID: 38790318 PMCID: PMC11117903 DOI: 10.3390/bioengineering11050451] [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: 03/21/2024] [Revised: 04/18/2024] [Accepted: 04/26/2024] [Indexed: 05/26/2024] Open
Abstract
Artificial intelligence (AI) has been implemented in multiple fields of medicine to assist in the diagnosis and treatment of patients. AI implementation in radiology, more specifically for breast imaging, has advanced considerably. Breast cancer is one of the most important causes of cancer mortality among women, and there has been increased attention towards creating more efficacious methods for breast cancer detection utilizing AI to improve radiologist accuracy and efficiency to meet the increasing demand of our patients. AI can be applied to imaging studies to improve image quality, increase interpretation accuracy, and improve time efficiency and cost efficiency. AI applied to mammography, ultrasound, and MRI allows for improved cancer detection and diagnosis while decreasing intra- and interobserver variability. The synergistic effect between a radiologist and AI has the potential to improve patient care in underserved populations with the intention of providing quality and equitable care for all. Additionally, AI has allowed for improved risk stratification. Further, AI application can have treatment implications as well by identifying upstage risk of ductal carcinoma in situ (DCIS) to invasive carcinoma and by better predicting individualized patient response to neoadjuvant chemotherapy. AI has potential for advancement in pre-operative 3-dimensional models of the breast as well as improved viability of reconstructive grafts.
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Affiliation(s)
- Stephanie B. Shamir
- Department of Diagnostic, Molecular and Interventional Radiology, The Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY 10029, USA
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Douglas L, Fuhrman J, Hu Q, Edwards A, Sheth D, Abe H, Giger M. Computerized assessment of background parenchymal enhancement on breast dynamic contrast-enhanced-MRI including electronic lesion removal. J Med Imaging (Bellingham) 2024; 11:034501. [PMID: 38737493 PMCID: PMC11086664 DOI: 10.1117/1.jmi.11.3.034501] [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: 04/20/2023] [Revised: 03/29/2024] [Accepted: 04/09/2024] [Indexed: 05/14/2024] Open
Abstract
Purpose 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. Approach A U-Net was trained for breast segmentation from DCE-MRI maximum intensity projection (MIP) images. Fuzzy c -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. Results Statistically significant trends were found between radiologist BPE ratings and calculated BPE scores for all breast regions (Kendall correlation, p < 0.001 ). Scores from all breast regions performed significantly better than guessing (p < 0.025 from the z -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. Conclusions 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.
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Affiliation(s)
- Lindsay Douglas
- University of Chicago, Department of Radiology Committee on Medical Physics, Chicago, Illinois, United States
| | - Jordan Fuhrman
- University of Chicago, Department of Radiology Committee on Medical Physics, Chicago, Illinois, United States
| | - Qiyuan Hu
- University of Chicago, Department of Radiology Committee on Medical Physics, Chicago, Illinois, United States
| | - Alexandra Edwards
- University of Chicago, Department of Radiology Committee on Medical Physics, Chicago, Illinois, United States
| | - Deepa Sheth
- University of Chicago, Department of Radiology Committee on Medical Physics, Chicago, Illinois, United States
| | - Hiroyuki Abe
- University of Chicago, Department of Radiology Committee on Medical Physics, Chicago, Illinois, United States
| | - Maryellen Giger
- University of Chicago, Department of Radiology Committee on Medical Physics, Chicago, Illinois, United States
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4
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Yan R, Murakami W, Mortazavi S, Yu T, Chu FI, Lee-Felker S, Sung K. Quantitative assessment of background parenchymal enhancement is associated with lifetime breast cancer risk in screening MRI. Eur Radiol 2024:10.1007/s00330-024-10758-9. [PMID: 38683385 DOI: 10.1007/s00330-024-10758-9] [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: 10/02/2023] [Revised: 03/07/2024] [Accepted: 03/16/2024] [Indexed: 05/01/2024]
Abstract
OBJECTIVES To compare the quantitative background parenchymal enhancement (BPE) in women with different lifetime risks and BRCA mutation status of breast cancer using screening MRI. MATERIALS AND METHODS 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. RESULTS 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). CONCLUSION 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. CLINICAL RELEVANCE STATEMENT 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. KEY POINTS 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.
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Affiliation(s)
- Ran Yan
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
- Department of Bioengineering, Henry Samueli School of Engineering, University of California, Los Angeles, CA, USA.
| | - Wakana Murakami
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Department of Radiology, Showa University Graduate School of Medicine, Tokyo, Japan
| | - Shabnam Mortazavi
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Tiffany Yu
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Fang-I Chu
- Department of Radiation Oncology, University of California, Los Angeles, CA, USA
| | - Stephanie Lee-Felker
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Kyunghyun Sung
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Department of Bioengineering, Henry Samueli School of Engineering, University of California, Los Angeles, CA, USA
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5
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Lo Gullo R, Brunekreef J, Marcus E, Han LK, Eskreis-Winkler S, Thakur SB, Mann R, Groot Lipman K, Teuwen J, Pinker K. AI Applications to Breast MRI: Today and Tomorrow. J Magn Reson Imaging 2024. [PMID: 38581127 DOI: 10.1002/jmri.29358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 03/07/2024] [Accepted: 03/09/2024] [Indexed: 04/08/2024] Open
Abstract
In breast imaging, there is an unrelenting increase in the demand for breast imaging services, partly explained by continuous expanding imaging indications in breast diagnosis and treatment. As the human workforce providing these services is not growing at the same rate, the implementation of artificial intelligence (AI) in breast imaging has gained significant momentum to maximize workflow efficiency and increase productivity while concurrently improving diagnostic accuracy and patient outcomes. Thus far, the implementation of AI in breast imaging is at the most advanced stage with mammography and digital breast tomosynthesis techniques, followed by ultrasound, whereas the implementation of AI in breast magnetic resonance imaging (MRI) is not moving along as rapidly due to the complexity of MRI examinations and fewer available dataset. Nevertheless, there is persisting interest in AI-enhanced breast MRI applications, even as the use of and indications of breast MRI continue to expand. This review presents an overview of the basic concepts of AI imaging analysis and subsequently reviews the use cases for AI-enhanced MRI interpretation, that is, breast MRI triaging and lesion detection, lesion classification, prediction of treatment response, risk assessment, and image quality. Finally, it provides an outlook on the barriers and facilitators for the adoption of AI in breast MRI. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 6.
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Affiliation(s)
- Roberto Lo Gullo
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, New York, USA
| | - Joren Brunekreef
- AI for Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Eric Marcus
- AI for Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Lynn K Han
- Weill Cornell Medical College, New York-Presbyterian Hospital, New York City, New York, USA
| | - Sarah Eskreis-Winkler
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, New York, USA
| | - Sunitha B Thakur
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, New York, USA
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York City, New York, USA
| | - Ritse Mann
- AI for Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Kevin Groot Lipman
- AI for Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Jonas Teuwen
- AI for Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Katja Pinker
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, New York, USA
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6
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Hussain S, Ali M, Naseem U, Nezhadmoghadam F, Jatoi MA, Gulliver TA, Tamez-Peña JG. Breast cancer risk prediction using machine learning: a systematic review. Front Oncol 2024; 14:1343627. [PMID: 38571502 PMCID: PMC10987819 DOI: 10.3389/fonc.2024.1343627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Accepted: 02/26/2024] [Indexed: 04/05/2024] Open
Abstract
Background Breast cancer is the leading cause of cancer-related fatalities among women worldwide. Conventional screening and risk prediction models primarily rely on demographic and patient clinical history to devise policies and estimate likelihood. However, recent advancements in artificial intelligence (AI) techniques, particularly deep learning (DL), have shown promise in the development of personalized risk models. These models leverage individual patient information obtained from medical imaging and associated reports. In this systematic review, we thoroughly investigated the existing literature on the application of DL to digital mammography, radiomics, genomics, and clinical information for breast cancer risk assessment. We critically analyzed these studies and discussed their findings, highlighting the promising prospects of DL techniques for breast cancer risk prediction. Additionally, we explored ongoing research initiatives and potential future applications of AI-driven approaches to further improve breast cancer risk prediction, thereby facilitating more effective screening and personalized risk management strategies. Objective and methods This study presents a comprehensive overview of imaging and non-imaging features used in breast cancer risk prediction using traditional and AI models. The features reviewed in this study included imaging, radiomics, genomics, and clinical features. Furthermore, this survey systematically presented DL methods developed for breast cancer risk prediction, aiming to be useful for both beginners and advanced-level researchers. Results A total of 600 articles were identified, 20 of which met the set criteria and were selected. Parallel benchmarking of DL models, along with natural language processing (NLP) applied to imaging and non-imaging features, could allow clinicians and researchers to gain greater awareness as they consider the clinical deployment or development of new models. This review provides a comprehensive guide for understanding the current status of breast cancer risk assessment using AI. Conclusion This study offers investigators a different perspective on the use of AI for breast cancer risk prediction, incorporating numerous imaging and non-imaging features.
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Affiliation(s)
- Sadam Hussain
- School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey, Mexico
- Department of Electrical and Computer Engineering, University of Victoria, Victoria, BC, Canada
| | - Mansoor Ali
- School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey, Mexico
| | - Usman Naseem
- College of Science and Engineering, James Cook University, Cairns, QLD, Australia
| | | | - Munsif Ali Jatoi
- Department of Biomedical Engineering, Salim Habib University, Karachi, Pakistan
| | - T. Aaron Gulliver
- Department of Electrical and Computer Engineering, University of Victoria, Victoria, BC, Canada
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7
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Bokacheva L. Automated Background Parenchymal Enhancement Measurements at MRI to Predict Breast Cancer Risk. Radiology 2023; 308:e231765. [PMID: 37750769 PMCID: PMC10546279 DOI: 10.1148/radiol.231765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Revised: 07/13/2023] [Accepted: 07/14/2023] [Indexed: 09/27/2023]
Affiliation(s)
- Louisa Bokacheva
- From the Center for Cognitive Neurology, Department of Neurology, NYU Grossman School of Medicine, 145 E 32nd St, 5th Floor, New York, NY 10016
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8
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Wang H, van der Velden BHM, Verburg E, Bakker MF, Pijnappel RM, Veldhuis WB, van Gils CH, Gilhuijs KGA. Assessing Quantitative Parenchymal Features at Baseline Dynamic Contrast-enhanced MRI and Cancer Occurrence in Women with Extremely Dense Breasts. Radiology 2023; 308:e222841. [PMID: 37552061 DOI: 10.1148/radiol.222841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/09/2023]
Abstract
Background Automated identification of quantitative breast parenchymal enhancement features on dynamic contrast-enhanced (DCE) MRI scans could provide added value in assessment of breast cancer risk in women with extremely dense breasts. Purpose To automatically identify quantitative properties of the breast parenchyma on baseline DCE MRI scans and assess their association with breast cancer occurrence in women with extremely dense breasts. Materials and Methods This study represents a secondary analysis of the Dense Tissue and Early Breast Neoplasm Screening trial. MRI was performed in eight hospitals between December 2011 and January 2016. After segmentation of fibroglandular tissue, quantitative features (including volumetric density, volumetric morphology, and enhancement characteristics) of the parenchyma were extracted from baseline MRI scans. Principal component analysis was used to identify parenchymal measures with the greatest variance. Multivariable Cox proportional hazards regression was applied to assess the association between breast cancer occurrence and quantitative parenchymal features, followed by stratification of significant features into tertiles. Results A total of 4553 women (mean age, 55.7 years ± 6 [SD]) with extremely dense breasts were included; of these women, 122 (3%) were diagnosed with breast cancer. Five principal components representing 96% of the variance were identified, and the component explaining the greatest independent variance (42%) consisted of MRI features relating to volume of enhancing parenchyma. Multivariable analysis showed that volume of enhancing parenchyma was associated with breast cancer occurrence (hazard ratio [HR], 1.09; 95% CI: 1.01, 1.18; P = .02). Additionally, women in the high tertile of volume of enhancing parenchyma showed a breast cancer occurrence twice that of women in the low tertile (HR, 2.09; 95% CI: 1.25, 3.61; P = .005). Conclusion In women with extremely dense breasts, a high volume of enhancing parenchyma on baseline DCE MRI scans was associated with increased occurrence of breast cancer as compared with a low volume of enhancing parenchyma. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Grimm in this issue.
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Affiliation(s)
- Hui Wang
- From the Image Sciences Institute (H.W., B.H.M.v.d.V., E.V., K.G.A.G.), Julius Center for Health Sciences and Primary Care (M.F.B., C.H.v.G.), and Department of Radiology (R.M.P., W.B.V.), University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, the Netherlands
| | - Bas H M van der Velden
- From the Image Sciences Institute (H.W., B.H.M.v.d.V., E.V., K.G.A.G.), Julius Center for Health Sciences and Primary Care (M.F.B., C.H.v.G.), and Department of Radiology (R.M.P., W.B.V.), University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, the Netherlands
| | - Erik Verburg
- From the Image Sciences Institute (H.W., B.H.M.v.d.V., E.V., K.G.A.G.), Julius Center for Health Sciences and Primary Care (M.F.B., C.H.v.G.), and Department of Radiology (R.M.P., W.B.V.), University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, the Netherlands
| | - Marije F Bakker
- From the Image Sciences Institute (H.W., B.H.M.v.d.V., E.V., K.G.A.G.), Julius Center for Health Sciences and Primary Care (M.F.B., C.H.v.G.), and Department of Radiology (R.M.P., W.B.V.), University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, the Netherlands
| | - Ruud M Pijnappel
- From the Image Sciences Institute (H.W., B.H.M.v.d.V., E.V., K.G.A.G.), Julius Center for Health Sciences and Primary Care (M.F.B., C.H.v.G.), and Department of Radiology (R.M.P., W.B.V.), University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, the Netherlands
| | - Wouter B Veldhuis
- From the Image Sciences Institute (H.W., B.H.M.v.d.V., E.V., K.G.A.G.), Julius Center for Health Sciences and Primary Care (M.F.B., C.H.v.G.), and Department of Radiology (R.M.P., W.B.V.), University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, the Netherlands
| | - Carla H van Gils
- From the Image Sciences Institute (H.W., B.H.M.v.d.V., E.V., K.G.A.G.), Julius Center for Health Sciences and Primary Care (M.F.B., C.H.v.G.), and Department of Radiology (R.M.P., W.B.V.), University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, the Netherlands
| | - Kenneth G A Gilhuijs
- From the Image Sciences Institute (H.W., B.H.M.v.d.V., E.V., K.G.A.G.), Julius Center for Health Sciences and Primary Care (M.F.B., C.H.v.G.), and Department of Radiology (R.M.P., W.B.V.), University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, the Netherlands
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9
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Retson TA, Eghtedari M. Expanding Horizons: The Realities of CAD, the Promise of Artificial Intelligence, and Machine Learning's Role in Breast Imaging beyond Screening Mammography. Diagnostics (Basel) 2023; 13:2133. [PMID: 37443526 DOI: 10.3390/diagnostics13132133] [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: 05/02/2023] [Revised: 06/06/2023] [Accepted: 06/12/2023] [Indexed: 07/15/2023] Open
Abstract
Artificial intelligence (AI) applications in mammography have gained significant popular attention; however, AI has the potential to revolutionize other aspects of breast imaging beyond simple lesion detection. AI has the potential to enhance risk assessment by combining conventional factors with imaging and improve lesion detection through a comparison with prior studies and considerations of symmetry. It also holds promise in ultrasound analysis and automated whole breast ultrasound, areas marked by unique challenges. AI's potential utility also extends to administrative tasks such as MQSA compliance, scheduling, and protocoling, which can reduce the radiologists' workload. However, adoption in breast imaging faces limitations in terms of data quality and standardization, generalizability, benchmarking performance, and integration into clinical workflows. Developing methods for radiologists to interpret AI decisions, and understanding patient perspectives to build trust in AI results, will be key future endeavors, with the ultimate aim of fostering more efficient radiology practices and better patient care.
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Affiliation(s)
- Tara A Retson
- Department of Radiology, University of California, San Diego, CA 92093, USA
| | - Mohammad Eghtedari
- Department of Radiology, University of California, San Diego, CA 92093, USA
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10
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Acciavatti RJ, Lee SH, Reig B, Moy L, Conant EF, Kontos D, Moon WK. Beyond Breast Density: Risk Measures for Breast Cancer in Multiple Imaging Modalities. Radiology 2023; 306:e222575. [PMID: 36749212 PMCID: PMC9968778 DOI: 10.1148/radiol.222575] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 11/23/2022] [Accepted: 12/05/2022] [Indexed: 02/08/2023]
Abstract
Breast density is an independent risk factor for breast cancer. In digital mammography and digital breast tomosynthesis, breast density is assessed visually using the four-category scale developed by the American College of Radiology Breast Imaging Reporting and Data System (5th edition as of November 2022). Epidemiologically based risk models, such as the Tyrer-Cuzick model (version 8), demonstrate superior modeling performance when mammographic density is incorporated. Beyond just density, a separate mammographic measure of breast cancer risk is parenchymal textural complexity. With advancements in radiomics and deep learning, mammographic textural patterns can be assessed quantitatively and incorporated into risk models. Other supplemental screening modalities, such as breast US and MRI, offer independent risk measures complementary to those derived from mammography. Breast US allows the two components of fibroglandular tissue (stromal and glandular) to be visualized separately in a manner that is not possible with mammography. A higher glandular component at screening breast US is associated with higher risk. With MRI, a higher background parenchymal enhancement of the fibroglandular tissue has also emerged as an imaging marker for risk assessment. Imaging markers observed at mammography, US, and MRI are powerful tools in refining breast cancer risk prediction, beyond mammographic density alone.
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Affiliation(s)
| | | | - Beatriu Reig
- From the Department of Radiology, University of Pennsylvania, 3400
Spruce St, Philadelphia, PA 19104 (R.J.A., E.F.C., D.K.); Department of
Radiology, Seoul National University Hospital, Seoul, South Korea (S.H.L.,
W.K.M.); and Department of Radiology, NYU Langone Health, New York, NY (B.R.,
L.M.)
| | - Linda Moy
- From the Department of Radiology, University of Pennsylvania, 3400
Spruce St, Philadelphia, PA 19104 (R.J.A., E.F.C., D.K.); Department of
Radiology, Seoul National University Hospital, Seoul, South Korea (S.H.L.,
W.K.M.); and Department of Radiology, NYU Langone Health, New York, NY (B.R.,
L.M.)
| | - Emily F. Conant
- From the Department of Radiology, University of Pennsylvania, 3400
Spruce St, Philadelphia, PA 19104 (R.J.A., E.F.C., D.K.); Department of
Radiology, Seoul National University Hospital, Seoul, South Korea (S.H.L.,
W.K.M.); and Department of Radiology, NYU Langone Health, New York, NY (B.R.,
L.M.)
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11
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Mavragani A, Bradley H, Jin Y, Zhou L, Sun S, Xu X, Li S, Yang H, Zhang Q, Wang Y. An Assessment of the Predictive Performance of Current Machine Learning-Based Breast Cancer Risk Prediction Models: Systematic Review. JMIR Public Health Surveill 2022; 8:e35750. [PMID: 36426919 PMCID: PMC9837707 DOI: 10.2196/35750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 06/17/2022] [Accepted: 11/25/2022] [Indexed: 11/27/2022] Open
Abstract
BACKGROUND Several studies have explored the predictive performance of machine learning-based breast cancer risk prediction models and have shown controversial conclusions. Thus, the performance of the current machine learning-based breast cancer risk prediction models and their benefits and weakness need to be evaluated for the future development of feasible and efficient risk prediction models. OBJECTIVE The aim of this review was to assess the performance and the clinical feasibility of the currently available machine learning-based breast cancer risk prediction models. METHODS We searched for papers published until June 9, 2021, on machine learning-based breast cancer risk prediction models in PubMed, Embase, and Web of Science. Studies describing the development or validation models for predicting future breast cancer risk were included. The Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to assess the risk of bias and the clinical applicability of the included studies. The pooled area under the curve (AUC) was calculated using the DerSimonian and Laird random-effects model. RESULTS A total of 8 studies with 10 data sets were included. Neural network was the most common machine learning method for the development of breast cancer risk prediction models. The pooled AUC of the machine learning-based optimal risk prediction model reported in each study was 0.73 (95% CI 0.66-0.80; approximate 95% prediction interval 0.56-0.96), with a high level of heterogeneity between studies (Q=576.07, I2=98.44%; P<.001). The results of head-to-head comparison of the performance difference between the 2 types of models trained by the same data set showed that machine learning models had a slightly higher advantage than traditional risk factor-based models in predicting future breast cancer risk. The pooled AUC of the neural network-based risk prediction model was higher than that of the nonneural network-based optimal risk prediction model (0.71 vs 0.68, respectively). Subgroup analysis showed that the incorporation of imaging features in risk models resulted in a higher pooled AUC than the nonincorporation of imaging features in risk models (0.73 vs 0.61; Pheterogeneity=.001, respectively). The PROBAST analysis indicated that many machine learning models had high risk of bias and poorly reported calibration analysis. CONCLUSIONS Our review shows that the current machine learning-based breast cancer risk prediction models have some technical pitfalls and that their clinical feasibility and reliability are unsatisfactory.
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Affiliation(s)
| | | | - Yujing Jin
- Health Management Center, Tianjin Medical University General Hospital, Tianjin, China
| | - Lengxiao Zhou
- Health Management Center, Tianjin Medical University General Hospital, Tianjin, China
| | - Shaomei Sun
- Health Management Center, Tianjin Medical University General Hospital, Tianjin, China
| | - Xiaoqian Xu
- Health Management Center, Tianjin Medical University General Hospital, Tianjin, China
| | - Shuqian Li
- Health Management Center, Tianjin Medical University General Hospital, Tianjin, China
| | - Hongxi Yang
- Department of Bioinformatics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Qing Zhang
- Health Management Center, Tianjin Medical University General Hospital, Tianjin, China
| | - Yaogang Wang
- School of Public Health, Tianjin Medical University, Tianjin, China
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12
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Eskreis-Winkler S, Sutton EJ, D’Alessio D, Gallagher K, Saphier N, Stember J, Martinez DF, Morris EA, Pinker K. Breast MRI Background Parenchymal Enhancement Categorization Using Deep Learning: Outperforming the Radiologist. J Magn Reson Imaging 2022; 56:1068-1076. [PMID: 35167152 PMCID: PMC9376189 DOI: 10.1002/jmri.28111] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 02/01/2022] [Accepted: 02/02/2022] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Background parenchymal enhancement (BPE) is assessed on breast MRI reports as mandated by the Breast Imaging Reporting and Data System (BI-RADS) but is prone to inter and intrareader variation. Semiautomated and fully automated BPE assessment tools have been developed but none has surpassed radiologist BPE designations. PURPOSE To develop a deep learning model for automated BPE classification and to compare its performance with current standard-of-care radiology report BPE designations. STUDY TYPE Retrospective. POPULATION Consecutive high-risk patients (i.e. >20% lifetime risk of breast cancer) who underwent contrast-enhanced screening breast MRI from October 2013 to January 2019. The study included 5224 breast MRIs, divided into 3998 training, 444 validation, and 782 testing exams. On radiology reports, 1286 exams were categorized as high BPE (i.e., marked or moderate) and 3938 as low BPE (i.e., mild or minimal). FIELD STRENGTH/SEQUENCE A 1.5 T or 3 T system; one precontrast and three postcontrast phases of fat-saturated T1-weighted dynamic contrast-enhanced imaging. ASSESSMENT Breast MRIs were used to develop two deep learning models (Slab artificial intelligence (AI); maximum intensity projection [MIP] AI) for BPE categorization using radiology report BPE labels. Models were tested on a heldout test sets using radiology report BPE and three-reader averaged consensus as the reference standards. STATISTICAL TESTS Model performance was assessed using receiver operating characteristic curve analysis. Associations between high BPE and BI-RADS assessments were evaluated using McNemar's chi-square test (α* = 0.025). RESULTS The Slab AI model significantly outperformed the MIP AI model across the full test set (area under the curve of 0.84 vs. 0.79) using the radiology report reference standard. Using three-reader consensus BPE labels reference standard, our AI model significantly outperformed radiology report BPE labels. Finally, the AI model was significantly more likely than the radiologist to assign "high BPE" to suspicious breast MRIs and significantly less likely than the radiologist to assign "high BPE" to negative breast MRIs. DATA CONCLUSION Fully automated BPE assessments for breast MRIs could be more accurate than BPE assessments from radiology reports. LEVEL OF EVIDENCE 4 TECHNICAL EFFICACY STAGE: 3.
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Affiliation(s)
- Sarah Eskreis-Winkler
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th Street, New York, NY 10065, USA
| | - Elizabeth J. Sutton
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th Street, New York, NY 10065, USA
| | - Donna D’Alessio
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th Street, New York, NY 10065, USA
| | - Katherine Gallagher
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th Street, New York, NY 10065, USA
| | - Nicole Saphier
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th Street, New York, NY 10065, USA
| | - Joseph Stember
- Department of Radiology, Neuroradiology Service, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA
| | - Danny F Martinez
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th Street, New York, NY 10065, USA
| | | | - Katja Pinker
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th Street, New York, NY 10065, USA
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13
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Jones MA, Islam W, Faiz R, Chen X, Zheng B. Applying artificial intelligence technology to assist with breast cancer diagnosis and prognosis prediction. Front Oncol 2022; 12:980793. [PMID: 36119479 PMCID: PMC9471147 DOI: 10.3389/fonc.2022.980793] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 08/04/2022] [Indexed: 12/27/2022] Open
Abstract
Breast cancer remains the most diagnosed cancer in women. Advances in medical imaging modalities and technologies have greatly aided in the early detection of breast cancer and the decline of patient mortality rates. However, reading and interpreting breast images remains difficult due to the high heterogeneity of breast tumors and fibro-glandular tissue, which results in lower cancer detection sensitivity and specificity and large inter-reader variability. In order to help overcome these clinical challenges, researchers have made great efforts to develop computer-aided detection and/or diagnosis (CAD) schemes of breast images to provide radiologists with decision-making support tools. Recent rapid advances in high throughput data analysis methods and artificial intelligence (AI) technologies, particularly radiomics and deep learning techniques, have led to an exponential increase in the development of new AI-based models of breast images that cover a broad range of application topics. In this review paper, we focus on reviewing recent advances in better understanding the association between radiomics features and tumor microenvironment and the progress in developing new AI-based quantitative image feature analysis models in three realms of breast cancer: predicting breast cancer risk, the likelihood of tumor malignancy, and tumor response to treatment. The outlook and three major challenges of applying new AI-based models of breast images to clinical practice are also discussed. Through this review we conclude that although developing new AI-based models of breast images has achieved significant progress and promising results, several obstacles to applying these new AI-based models to clinical practice remain. Therefore, more research effort is needed in future studies.
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Affiliation(s)
- Meredith A. Jones
- School of Biomedical Engineering, University of Oklahoma, Norman, OK, United States
- *Correspondence: Meredith A. Jones,
| | - Warid Islam
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, United States
| | - Rozwat Faiz
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, United States
| | - Xuxin Chen
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, United States
| | - Bin Zheng
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, United States
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14
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Grøvik E, Hoff SR. Editorial for "Breast MRI Background Parenchymal Enhancement Categorization Using Deep Learning: Outperforming the Radiologist". J Magn Reson Imaging 2022; 56:1077-1078. [PMID: 35343010 DOI: 10.1002/jmri.28183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Accepted: 03/15/2022] [Indexed: 11/10/2022] Open
Affiliation(s)
- Endre Grøvik
- Department of Radiology, Ålesund Hospital, Møre og Romsdal Hospital Trust, Alesund, Norway.,Department of Physics, Norwegian University of Science and Technology, Trondheim, Norway
| | - Solveig Roth Hoff
- Department of Radiology, Ålesund Hospital, Møre og Romsdal Hospital Trust, Alesund, Norway.,Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
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15
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Tadavarthi Y, Makeeva V, Wagstaff W, Zhan H, Podlasek A, Bhatia N, Heilbrun M, Krupinski E, Safdar N, Banerjee I, Gichoya J, Trivedi H. Overview of Noninterpretive Artificial Intelligence Models for Safety, Quality, Workflow, and Education Applications in Radiology Practice. Radiol Artif Intell 2022; 4:e210114. [PMID: 35391770 DOI: 10.1148/ryai.210114] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 12/17/2021] [Accepted: 01/11/2022] [Indexed: 12/17/2022]
Abstract
Artificial intelligence has become a ubiquitous term in radiology over the past several years, and much attention has been given to applications that aid radiologists in the detection of abnormalities and diagnosis of diseases. However, there are many potential applications related to radiologic image quality, safety, and workflow improvements that present equal, if not greater, value propositions to radiology practices, insurance companies, and hospital systems. This review focuses on six major categories for artificial intelligence applications: study selection and protocoling, image acquisition, worklist prioritization, study reporting, business applications, and resident education. All of these categories can substantially affect different aspects of radiology practices and workflows. Each of these categories has different value propositions in terms of whether they could be used to increase efficiency, improve patient safety, increase revenue, or save costs. Each application is covered in depth in the context of both current and future areas of work. Keywords: Use of AI in Education, Application Domain, Supervised Learning, Safety © RSNA, 2022.
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Affiliation(s)
- Yasasvi Tadavarthi
- Department of Medicine, Medical College of Georgia, Augusta, Ga (Y.T.); Department of Radiology and Imaging Sciences (V.M., W.W., H.Z., M.H., E.K., N.S., J.G., H.T.), School of Medicine (N.B.), and Department of Biomedical Informatics (I.B.), Emory University, 1364 E Clifton Rd NE, Atlanta, GA 30322; and Southend University Hospital NHS Foundation Trust, Westcliff-on-Sea, UK (A.P.)
| | - Valeria Makeeva
- Department of Medicine, Medical College of Georgia, Augusta, Ga (Y.T.); Department of Radiology and Imaging Sciences (V.M., W.W., H.Z., M.H., E.K., N.S., J.G., H.T.), School of Medicine (N.B.), and Department of Biomedical Informatics (I.B.), Emory University, 1364 E Clifton Rd NE, Atlanta, GA 30322; and Southend University Hospital NHS Foundation Trust, Westcliff-on-Sea, UK (A.P.)
| | - William Wagstaff
- Department of Medicine, Medical College of Georgia, Augusta, Ga (Y.T.); Department of Radiology and Imaging Sciences (V.M., W.W., H.Z., M.H., E.K., N.S., J.G., H.T.), School of Medicine (N.B.), and Department of Biomedical Informatics (I.B.), Emory University, 1364 E Clifton Rd NE, Atlanta, GA 30322; and Southend University Hospital NHS Foundation Trust, Westcliff-on-Sea, UK (A.P.)
| | - Henry Zhan
- Department of Medicine, Medical College of Georgia, Augusta, Ga (Y.T.); Department of Radiology and Imaging Sciences (V.M., W.W., H.Z., M.H., E.K., N.S., J.G., H.T.), School of Medicine (N.B.), and Department of Biomedical Informatics (I.B.), Emory University, 1364 E Clifton Rd NE, Atlanta, GA 30322; and Southend University Hospital NHS Foundation Trust, Westcliff-on-Sea, UK (A.P.)
| | - Anna Podlasek
- Department of Medicine, Medical College of Georgia, Augusta, Ga (Y.T.); Department of Radiology and Imaging Sciences (V.M., W.W., H.Z., M.H., E.K., N.S., J.G., H.T.), School of Medicine (N.B.), and Department of Biomedical Informatics (I.B.), Emory University, 1364 E Clifton Rd NE, Atlanta, GA 30322; and Southend University Hospital NHS Foundation Trust, Westcliff-on-Sea, UK (A.P.)
| | - Neil Bhatia
- Department of Medicine, Medical College of Georgia, Augusta, Ga (Y.T.); Department of Radiology and Imaging Sciences (V.M., W.W., H.Z., M.H., E.K., N.S., J.G., H.T.), School of Medicine (N.B.), and Department of Biomedical Informatics (I.B.), Emory University, 1364 E Clifton Rd NE, Atlanta, GA 30322; and Southend University Hospital NHS Foundation Trust, Westcliff-on-Sea, UK (A.P.)
| | - Marta Heilbrun
- Department of Medicine, Medical College of Georgia, Augusta, Ga (Y.T.); Department of Radiology and Imaging Sciences (V.M., W.W., H.Z., M.H., E.K., N.S., J.G., H.T.), School of Medicine (N.B.), and Department of Biomedical Informatics (I.B.), Emory University, 1364 E Clifton Rd NE, Atlanta, GA 30322; and Southend University Hospital NHS Foundation Trust, Westcliff-on-Sea, UK (A.P.)
| | - Elizabeth Krupinski
- Department of Medicine, Medical College of Georgia, Augusta, Ga (Y.T.); Department of Radiology and Imaging Sciences (V.M., W.W., H.Z., M.H., E.K., N.S., J.G., H.T.), School of Medicine (N.B.), and Department of Biomedical Informatics (I.B.), Emory University, 1364 E Clifton Rd NE, Atlanta, GA 30322; and Southend University Hospital NHS Foundation Trust, Westcliff-on-Sea, UK (A.P.)
| | - Nabile Safdar
- Department of Medicine, Medical College of Georgia, Augusta, Ga (Y.T.); Department of Radiology and Imaging Sciences (V.M., W.W., H.Z., M.H., E.K., N.S., J.G., H.T.), School of Medicine (N.B.), and Department of Biomedical Informatics (I.B.), Emory University, 1364 E Clifton Rd NE, Atlanta, GA 30322; and Southend University Hospital NHS Foundation Trust, Westcliff-on-Sea, UK (A.P.)
| | - Imon Banerjee
- Department of Medicine, Medical College of Georgia, Augusta, Ga (Y.T.); Department of Radiology and Imaging Sciences (V.M., W.W., H.Z., M.H., E.K., N.S., J.G., H.T.), School of Medicine (N.B.), and Department of Biomedical Informatics (I.B.), Emory University, 1364 E Clifton Rd NE, Atlanta, GA 30322; and Southend University Hospital NHS Foundation Trust, Westcliff-on-Sea, UK (A.P.)
| | - Judy Gichoya
- Department of Medicine, Medical College of Georgia, Augusta, Ga (Y.T.); Department of Radiology and Imaging Sciences (V.M., W.W., H.Z., M.H., E.K., N.S., J.G., H.T.), School of Medicine (N.B.), and Department of Biomedical Informatics (I.B.), Emory University, 1364 E Clifton Rd NE, Atlanta, GA 30322; and Southend University Hospital NHS Foundation Trust, Westcliff-on-Sea, UK (A.P.)
| | - Hari Trivedi
- Department of Medicine, Medical College of Georgia, Augusta, Ga (Y.T.); Department of Radiology and Imaging Sciences (V.M., W.W., H.Z., M.H., E.K., N.S., J.G., H.T.), School of Medicine (N.B.), and Department of Biomedical Informatics (I.B.), Emory University, 1364 E Clifton Rd NE, Atlanta, GA 30322; and Southend University Hospital NHS Foundation Trust, Westcliff-on-Sea, UK (A.P.)
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16
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Cè M, Caloro E, Pellegrino ME, Basile M, Sorce A, Fazzini D, Oliva G, Cellina M. Artificial intelligence in breast cancer imaging: risk stratification, lesion detection and classification, treatment planning and prognosis-a narrative review. EXPLORATION OF TARGETED ANTI-TUMOR THERAPY 2022; 3:795-816. [PMID: 36654817 PMCID: PMC9834285 DOI: 10.37349/etat.2022.00113] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Accepted: 09/28/2022] [Indexed: 12/28/2022] Open
Abstract
The advent of artificial intelligence (AI) represents a real game changer in today's landscape of breast cancer imaging. Several innovative AI-based tools have been developed and validated in recent years that promise to accelerate the goal of real patient-tailored management. Numerous studies confirm that proper integration of AI into existing clinical workflows could bring significant benefits to women, radiologists, and healthcare systems. The AI-based approach has proved particularly useful for developing new risk prediction models that integrate multi-data streams for planning individualized screening protocols. Furthermore, AI models could help radiologists in the pre-screening and lesion detection phase, increasing diagnostic accuracy, while reducing workload and complications related to overdiagnosis. Radiomics and radiogenomics approaches could extrapolate the so-called imaging signature of the tumor to plan a targeted treatment. The main challenges to the development of AI tools are the huge amounts of high-quality data required to train and validate these models and the need for a multidisciplinary team with solid machine-learning skills. The purpose of this article is to present a summary of the most important AI applications in breast cancer imaging, analyzing possible challenges and new perspectives related to the widespread adoption of these new tools.
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Affiliation(s)
- Maurizio Cè
- Postgraduate School in Diagnostic and Interventional Radiology, University of Milan, 20122 Milan, Italy,Correspondence: Maurizio Cè, Postgraduate School in Diagnostic and Interventional Radiology, University of Milan, Via Festa del Perdono, 7, 20122 Milan, Italy.
| | - Elena Caloro
- Postgraduate School in Diagnostic and Interventional Radiology, University of Milan, 20122 Milan, Italy
| | - Maria E. Pellegrino
- Postgraduate School in Diagnostic and Interventional Radiology, University of Milan, 20122 Milan, Italy
| | - Mariachiara Basile
- Postgraduate School in Diagnostic and Interventional Radiology, University of Milan, 20122 Milan, Italy
| | - Adriana Sorce
- Postgraduate School in Diagnostic and Interventional Radiology, University of Milan, 20122 Milan, Italy
| | | | - Giancarlo Oliva
- Department of Radiology, ASST Fatebenefratelli Sacco, 20121 Milan, Italy
| | - Michaela Cellina
- Department of Radiology, ASST Fatebenefratelli Sacco, 20121 Milan, Italy
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17
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Bauer E, Levy MS, Domachevsky L, Anaby D, Nissan N. Background parenchymal enhancement and uptake as breast cancer imaging biomarkers: A state-of-the-art review. Clin Imaging 2021; 83:41-50. [PMID: 34953310 DOI: 10.1016/j.clinimag.2021.11.021] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 10/29/2021] [Accepted: 11/15/2021] [Indexed: 12/20/2022]
Abstract
Within the past decade, background parenchymal enhancement (BPE) and background parenchymal uptake (BPU) have emerged as novel imaging-derived biomarkers in the diagnosis and treatment monitoring of breast cancer. Growing evidence supports the role of breast parenchyma vascularity and metabolic activity as probable risk factors for breast cancer development. Furthermore, in the presence of a newly-diagnosed breast cancer, added clinically-relevant data was surprisingly found in the respective imaging properties of the non-affected contralateral breast. Evaluation of the contralateral BPE and BPU have been found to be especially instrumental in predicting the prognosis of a patient with breast cancer and even anticipating their response to neoadjuvant chemotherapy. Simultaneously, further research has found a link between these two biomarkers, even though they represent different physical properties. The aim of this review is to provide an up to date summary of the current clinical applications of BPE and BPU as breast cancer imaging biomarkers with the hope that it propels their further usage in clinical practice.
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Affiliation(s)
- Ethan Bauer
- Department of Radiology, Sheba Medical Center, Israel; Sackler School of Medicine, Tel Aviv University, Israel
| | - Miri Sklair Levy
- Department of Radiology, Sheba Medical Center, Israel; Sackler School of Medicine, Tel Aviv University, Israel
| | - Liran Domachevsky
- Department of Radiology, Sheba Medical Center, Israel; Sackler School of Medicine, Tel Aviv University, Israel
| | - Debbie Anaby
- Department of Radiology, Sheba Medical Center, Israel; Sackler School of Medicine, Tel Aviv University, Israel
| | - Noam Nissan
- Department of Radiology, Sheba Medical Center, Israel; Sackler School of Medicine, Tel Aviv University, Israel.
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18
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Bahl M. Updates in Artificial Intelligence for Breast Imaging. Semin Roentgenol 2021; 57:160-167. [PMID: 35523530 PMCID: PMC9077006 DOI: 10.1053/j.ro.2021.12.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 12/23/2021] [Indexed: 12/19/2022]
Abstract
Artificial intelligence (AI) for breast imaging has rapidly moved from the experimental to implementation phase. As of this writing, Food and Drug Administration (FDA)-approved mammographic applications are available for triage, lesion detection and classification, and breast density assessment. For sonography and MRI, FDA-approved applications are available for lesion classification. Numerous other interpretive and noninterpretive AI applications are in the development phase. This article reviews AI applications for mammography, sonography, and MRI that are currently available for clinical use. In addition, clinical implementation and the future of AI for breast imaging are discussed.
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19
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Chalfant JS, Mortazavi S, Lee-Felker SA. Background Parenchymal Enhancement on Breast MRI: Assessment and Clinical Implications. CURRENT RADIOLOGY REPORTS 2021. [DOI: 10.1007/s40134-021-00386-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
Abstract
Purpose of Review
To present recent literature regarding the assessment and clinical implications of background parenchymal enhancement on breast MRI.
Recent Findings
The qualitative assessment of BPE remains variable within the literature, as well as in clinical practice. Several different quantitative approaches have been investigated in recent years, most commonly region of interest-based and segmentation-based assessments. However, quantitative assessment has not become standard in clinical practice to date. Numerous studies have demonstrated a clear association between higher BPE and future breast cancer risk. While higher BPE does not appear to significantly impact cancer detection, it may result in a higher abnormal interpretation rate. BPE is also likely a marker of pathologic complete response after neoadjuvant chemotherapy, with decreases in BPE during and after neoadjuvant chemotherapy correlated with pCR. In contrast, pre-treatment BPE does not appear to be predictive of pCR. The association between BPE and prognosis is less clear, with heterogeneous results in the literature.
Summary
Assessment of BPE continues to evolve, with heterogeneity in approaches to both qualitative and quantitative assessment. The level of BPE has important clinical implications, with associations with future breast cancer risk and treatment response. BPE may also be an imaging marker of prognosis, but future research is needed on this topic.
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20
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Reig B, Heacock L, Geras KJ, Moy L. Machine learning in breast MRI. J Magn Reson Imaging 2019; 52:998-1018. [PMID: 31276247 DOI: 10.1002/jmri.26852] [Citation(s) in RCA: 84] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2019] [Revised: 06/18/2019] [Accepted: 06/19/2019] [Indexed: 12/13/2022] Open
Abstract
Machine-learning techniques have led to remarkable advances in data extraction and analysis of medical imaging. Applications of machine learning to breast MRI continue to expand rapidly as increasingly accurate 3D breast and lesion segmentation allows the combination of radiologist-level interpretation (eg, BI-RADS lexicon), data from advanced multiparametric imaging techniques, and patient-level data such as genetic risk markers. Advances in breast MRI feature extraction have led to rapid dataset analysis, which offers promise in large pooled multiinstitutional data analysis. The object of this review is to provide an overview of machine-learning and deep-learning techniques for breast MRI, including supervised and unsupervised methods, anatomic breast segmentation, and lesion segmentation. Finally, it explores the role of machine learning, current limitations, and future applications to texture analysis, radiomics, and radiogenomics. Level of Evidence: 3 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019. J. Magn. Reson. Imaging 2020;52:998-1018.
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Affiliation(s)
- Beatriu Reig
- The Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Laura Heacock
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Krzysztof J Geras
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Linda Moy
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA.,Center for Advanced Imaging Innovation and Research (CAI2 R), New York University School of Medicine, New York, New York, USA
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