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Matheson J, Elder K, Nickson C, Park A, Mann GB, Rose A. Contrast-enhanced mammography for surveillance in women with a personal history of breast cancer. Breast Cancer Res Treat 2024; 208:293-305. [PMID: 38963525 PMCID: PMC11455689 DOI: 10.1007/s10549-024-07419-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2024] [Accepted: 06/27/2024] [Indexed: 07/05/2024]
Abstract
PURPOSE Women with a personal history of breast cancer have an increased risk of subsequent breast malignancy and may benefit from more sensitive surveillance than conventional mammography (MG). We previously reported outcomes for first surveillance episode using contrast-enhanced mammography (CEM), demonstrating higher sensitivity and comparable specificity to MG. We now report CEM performance for subsequent surveillance. METHODS A retrospective study of 1,190 women in an Australian hospital setting undergoing annual surveillance following initial surveillance CEM between June 2016 and December 2022. Outcome measures were recall rate, cancer detection rate, contribution of contrast to recalls, false positive rate, interval cancer rate and characteristics of surveillance detected and interval cancers. RESULTS 2,592 incident surveillance episodes were analysed, of which 93% involved contrast-based imaging. Of 116 (4.5%) recall episodes, 40/116 (34%) recalls were malignant (27 invasive; 13 ductal carcinoma in situ), totalling 15.4 cancers per 1000 surveillance episodes. 55/116 (47%) recalls were contrast-directed including 17/40 (43%) true positive recalls. Tumour features were similar for contrast-directed recalls and other diagnoses. 8/9 (89%) of contrast-directed invasive recalls were Grade 2-3, and 5/9 (56%) were triple negative breast cancers. There were two symptomatic interval cancers (0.8 per 1000 surveillance episodes, program sensitivity 96%). CONCLUSION Routine use of CEM in surveillance of women with PHBC led to an increase in the detection of clinically significant malignant lesions, with a low interval cancer rate compared to previous published series. Compared to mammographic surveillance, contrast-enhanced mammography increases the sensitivity of surveillance programs for women with PHBC.
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Affiliation(s)
- Julia Matheson
- The Royal Melbourne Hospital, Grattan Street, Parkville, Australia
- Department of Surgery, The University of Melbourne, Parkville, Australia
| | - Kenneth Elder
- The Royal Melbourne Hospital, Grattan Street, Parkville, Australia
- The Royal Women's Hospital, Flemington Road, Parkville, Australia
| | - Carolyn Nickson
- Daffodil Centre, The University of Sydney, a joint venture with Cancer Council New South Wales, Sydney, Australia
- Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Australia
| | - Allan Park
- The Royal Melbourne Hospital, Grattan Street, Parkville, Australia
| | - Gregory Bruce Mann
- The Royal Melbourne Hospital, Grattan Street, Parkville, Australia.
- Department of Surgery, The University of Melbourne, Parkville, Australia.
- The Royal Women's Hospital, Flemington Road, Parkville, Australia.
| | - Allison Rose
- The Royal Melbourne Hospital, Grattan Street, Parkville, Australia
- The Royal Women's Hospital, Flemington Road, Parkville, Australia
- Department of Radiology, The University of Melbourne, Parkville, Australia
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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; 34:6358-6368. [PMID: 38683385 PMCID: PMC11399191 DOI: 10.1007/s00330-024-10758-9] [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: 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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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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Mann RM. Lost in Background Enhancement. Radiology 2024; 312:e241545. [PMID: 39012253 DOI: 10.1148/radiol.241545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/17/2024]
Affiliation(s)
- Ritse M Mann
- From the Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein 10, 6525 GA Nijmegen, the Netherlands; and Department of Radiology, Netherlands Cancer Institute, Amsterdam, the Netherlands
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Müller-Franzes G, Khader F, Tayebi Arasteh S, Huck L, Bode M, Han T, Lemainque T, Kather JN, Nebelung S, Kuhl C, Truhn D. Intraindividual Comparison of Different Methods for Automated BPE Assessment at Breast MRI: A Call for Standardization. Radiology 2024; 312:e232304. [PMID: 39012249 DOI: 10.1148/radiol.232304] [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: 07/17/2024]
Abstract
Background The level of background parenchymal enhancement (BPE) at breast MRI provides predictive and prognostic information and can have diagnostic implications. However, there is a lack of standardization regarding BPE assessment. Purpose To investigate how well results of quantitative BPE assessment methods correlate among themselves and with assessments made by radiologists experienced in breast MRI. Materials and Methods In this pseudoprospective analysis of 5773 breast MRI examinations from 3207 patients (mean age, 60 years ± 10 [SD]), the level of BPE was prospectively categorized according to the Breast Imaging Reporting and Data System by radiologists experienced in breast MRI. For automated extraction of BPE, fibroglandular tissue (FGT) was segmented in an automated pipeline. Four different published methods for automated quantitative BPE extractions were used: two methods (A and B) based on enhancement intensity and two methods (C and D) based on the volume of enhanced FGT. The results from all methods were correlated, and agreement was investigated in comparison with the respective radiologist-based categorization. For surrogate validation of BPE assessment, how accurately the methods distinguished premenopausal women with (n = 50) versus without (n = 896) antihormonal treatment was determined. Results Intensity-based methods (A and B) exhibited a correlation with radiologist-based categorization of 0.56 ± 0.01 and 0.55 ± 0.01, respectively, and volume-based methods (C and D) had a correlation of 0.52 ± 0.01 and 0.50 ± 0.01 (P < .001). There were notable correlation differences (P < .001) between the BPE determined with the four methods. Among the four quantitation methods, method D offered the highest accuracy for distinguishing women with versus without antihormonal therapy (P = .01). Conclusion Results of different methods for quantitative BPE assessment agree only moderately among themselves or with visual categories reported by experienced radiologists; intensity-based methods correlate more closely with radiologists' ratings than volume-based methods. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Mann in this issue.
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Affiliation(s)
- Gustav Müller-Franzes
- From the Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Pauwelsstr 30, 52074 Aachen, Germany (G.M.F., F.K., S.T.A., L.H., M.B., T.H., T.L., S.N., C.K., D.T.); National Center for Tumor Diseases, Heidelberg University Hospital, Heidelberg, Germany (J.N.K.); Department of Medical Oncology, Heidelberg University Hospital, Heidelberg, Germany (J.N.K.); Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany (J.N.K.); and Department of Medicine I, University Hospital Dresden, Dresden, Germany (J.N.K.)
| | - Firas Khader
- From the Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Pauwelsstr 30, 52074 Aachen, Germany (G.M.F., F.K., S.T.A., L.H., M.B., T.H., T.L., S.N., C.K., D.T.); National Center for Tumor Diseases, Heidelberg University Hospital, Heidelberg, Germany (J.N.K.); Department of Medical Oncology, Heidelberg University Hospital, Heidelberg, Germany (J.N.K.); Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany (J.N.K.); and Department of Medicine I, University Hospital Dresden, Dresden, Germany (J.N.K.)
| | - Soroosh Tayebi Arasteh
- From the Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Pauwelsstr 30, 52074 Aachen, Germany (G.M.F., F.K., S.T.A., L.H., M.B., T.H., T.L., S.N., C.K., D.T.); National Center for Tumor Diseases, Heidelberg University Hospital, Heidelberg, Germany (J.N.K.); Department of Medical Oncology, Heidelberg University Hospital, Heidelberg, Germany (J.N.K.); Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany (J.N.K.); and Department of Medicine I, University Hospital Dresden, Dresden, Germany (J.N.K.)
| | - Luisa Huck
- From the Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Pauwelsstr 30, 52074 Aachen, Germany (G.M.F., F.K., S.T.A., L.H., M.B., T.H., T.L., S.N., C.K., D.T.); National Center for Tumor Diseases, Heidelberg University Hospital, Heidelberg, Germany (J.N.K.); Department of Medical Oncology, Heidelberg University Hospital, Heidelberg, Germany (J.N.K.); Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany (J.N.K.); and Department of Medicine I, University Hospital Dresden, Dresden, Germany (J.N.K.)
| | - Maike Bode
- From the Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Pauwelsstr 30, 52074 Aachen, Germany (G.M.F., F.K., S.T.A., L.H., M.B., T.H., T.L., S.N., C.K., D.T.); National Center for Tumor Diseases, Heidelberg University Hospital, Heidelberg, Germany (J.N.K.); Department of Medical Oncology, Heidelberg University Hospital, Heidelberg, Germany (J.N.K.); Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany (J.N.K.); and Department of Medicine I, University Hospital Dresden, Dresden, Germany (J.N.K.)
| | - Tianyu Han
- From the Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Pauwelsstr 30, 52074 Aachen, Germany (G.M.F., F.K., S.T.A., L.H., M.B., T.H., T.L., S.N., C.K., D.T.); National Center for Tumor Diseases, Heidelberg University Hospital, Heidelberg, Germany (J.N.K.); Department of Medical Oncology, Heidelberg University Hospital, Heidelberg, Germany (J.N.K.); Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany (J.N.K.); and Department of Medicine I, University Hospital Dresden, Dresden, Germany (J.N.K.)
| | - Teresa Lemainque
- From the Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Pauwelsstr 30, 52074 Aachen, Germany (G.M.F., F.K., S.T.A., L.H., M.B., T.H., T.L., S.N., C.K., D.T.); National Center for Tumor Diseases, Heidelberg University Hospital, Heidelberg, Germany (J.N.K.); Department of Medical Oncology, Heidelberg University Hospital, Heidelberg, Germany (J.N.K.); Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany (J.N.K.); and Department of Medicine I, University Hospital Dresden, Dresden, Germany (J.N.K.)
| | - Jakob Nikolas Kather
- From the Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Pauwelsstr 30, 52074 Aachen, Germany (G.M.F., F.K., S.T.A., L.H., M.B., T.H., T.L., S.N., C.K., D.T.); National Center for Tumor Diseases, Heidelberg University Hospital, Heidelberg, Germany (J.N.K.); Department of Medical Oncology, Heidelberg University Hospital, Heidelberg, Germany (J.N.K.); Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany (J.N.K.); and Department of Medicine I, University Hospital Dresden, Dresden, Germany (J.N.K.)
| | - Sven Nebelung
- From the Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Pauwelsstr 30, 52074 Aachen, Germany (G.M.F., F.K., S.T.A., L.H., M.B., T.H., T.L., S.N., C.K., D.T.); National Center for Tumor Diseases, Heidelberg University Hospital, Heidelberg, Germany (J.N.K.); Department of Medical Oncology, Heidelberg University Hospital, Heidelberg, Germany (J.N.K.); Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany (J.N.K.); and Department of Medicine I, University Hospital Dresden, Dresden, Germany (J.N.K.)
| | - Christiane Kuhl
- From the Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Pauwelsstr 30, 52074 Aachen, Germany (G.M.F., F.K., S.T.A., L.H., M.B., T.H., T.L., S.N., C.K., D.T.); National Center for Tumor Diseases, Heidelberg University Hospital, Heidelberg, Germany (J.N.K.); Department of Medical Oncology, Heidelberg University Hospital, Heidelberg, Germany (J.N.K.); Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany (J.N.K.); and Department of Medicine I, University Hospital Dresden, Dresden, Germany (J.N.K.)
| | - Daniel Truhn
- From the Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Pauwelsstr 30, 52074 Aachen, Germany (G.M.F., F.K., S.T.A., L.H., M.B., T.H., T.L., S.N., C.K., D.T.); National Center for Tumor Diseases, Heidelberg University Hospital, Heidelberg, Germany (J.N.K.); Department of Medical Oncology, Heidelberg University Hospital, Heidelberg, Germany (J.N.K.); Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany (J.N.K.); and Department of Medicine I, University Hospital Dresden, Dresden, Germany (J.N.K.)
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Wang H, H M van der Velden B, Verburg E, Bakker MF, Pijnappel RM, Veldhuis WB, van Gils CH, Gilhuijs KGA. Automated rating of background parenchymal enhancement in MRI of extremely dense breasts without compromising the association with breast cancer in the DENSE trial. Eur J Radiol 2024; 175:111442. [PMID: 38583349 DOI: 10.1016/j.ejrad.2024.111442] [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: 10/24/2023] [Revised: 02/06/2024] [Accepted: 03/21/2024] [Indexed: 04/09/2024]
Abstract
OBJECTIVES Background parenchymal enhancement (BPE) on dynamic contrast-enhanced MRI (DCE-MRI) as rated by radiologists is subject to inter- and intrareader variability. We aim to automate BPE category from DCE-MRI. METHODS This study represents a secondary analysis of the Dense Tissue and Early Breast Neoplasm Screening trial. 4553 women with extremely dense breasts who received supplemental breast MRI screening in eight hospitals were included. Minimal, mild, moderate and marked BPE rated by radiologists were used as reference. Fifteen quantitative MRI features of the fibroglandular tissue were extracted to predict BPE using Random Forest, Naïve Bayes, and KNN classifiers. Majority voting was used to combine the predictions. Internal-external validation was used for training and validation. The inverse-variance weighted mean accuracy was used to express mean performance across the eight hospitals. Cox regression was used to verify non inferiority of the association between automated rating and breast cancer occurrence compared to the association for manual rating. RESULTS The accuracy of majority voting ranged between 0.56 and 0.84 across the eight hospitals. The weighted mean prediction accuracy for the four BPE categories was 0.76. The hazard ratio (HR) of BPE for breast cancer occurrence was comparable between automated rating and manual rating (HR = 2.12 versus HR = 1.97, P = 0.65 for mild/moderate/marked BPE relative to minimal BPE). CONCLUSION It is feasible to rate BPE automatically in DCE-MRI of women with extremely dense breasts without compromising the underlying association between BPE and breast cancer occurrence. The accuracy for minimal BPE is superior to that for other BPE categories.
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Affiliation(s)
- Hui Wang
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
| | | | - Erik Verburg
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Marije F Bakker
- Julius Center for Health Sciences and Primary Care, Utrecht, The Netherlands
| | - Ruud M Pijnappel
- Department of Radiology, University Medical Center Utrecht, The Netherlands
| | - Wouter B Veldhuis
- Department of Radiology, University Medical Center Utrecht, The Netherlands
| | - Carla H van Gils
- Julius Center for Health Sciences and Primary Care, Utrecht, The Netherlands
| | - Kenneth G A Gilhuijs
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands.
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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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8
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Li X, Yan F. Predictive value of background parenchymal enhancement on breast magnetic resonance imaging for pathological tumor response to neoadjuvant chemotherapy in breast cancers: a systematic review. Cancer Imaging 2024; 24:35. [PMID: 38462607 PMCID: PMC10926651 DOI: 10.1186/s40644-024-00672-0] [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: 07/19/2023] [Accepted: 02/09/2024] [Indexed: 03/12/2024] Open
Abstract
OBJECTIVES This review aimed to assess the predictive value of background parenchymal enhancement (BPE) on breast magnetic resonance imaging (MRI) as an imaging biomarker for pathologic complete response (pCR) after neoadjuvant chemotherapy (NACT). METHODS Two reviewers independently performed a systemic literature search using the PubMed, MEDLINE, and Embase databases for studies published up to 11 June 2022. Data from relevant articles were extracted to assess the relationship between BPE and pCR. RESULTS This systematic review included 13 studies with extensive heterogeneity in population characteristics, MRI follow-up points, MRI protocol, NACT protocol, pCR definition, and BPE assessment. Baseline BPE levels were not associated with pCR, except in 1 study that reported higher baseline BPE of the younger participants (< 55 years) in the pCR group than the non-pCR group. A total of 5 studies qualitatively assessed BPE levels and indicated a correlation between reduced BPE after NACT and pCR; however, among the studies that quantitatively measured BPE, the same association was observed only in the subgroup analysis of 2 articles that assessed the status of hormone receptor and human epidermal growth factor receptor 2. In addition, the predictive ability of early BPE changes for pCR was reported in several articles and remains controversial. CONCLUSIONS Changes in BPE may be a promising imaging biomarker for predicting pCR in breast cancer. Because current studies remain insufficient, particularly those that quantitatively measure BPE, prospective and multicenter large-sample studies are needed to confirm this relationship.
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Affiliation(s)
- Xue Li
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Ruijin Er Road, Shanghai, 200025, China
- Department of Radiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, No. 1 DaHua Road, Dong Dan, Beijing, 100730, PR China
- Graduate School of Peking, Union Medical College, Beijing, PR China
| | - Fuhua Yan
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Ruijin Er Road, Shanghai, 200025, China.
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Nowakowska S, Borkowski K, Ruppert CM, Landsmann A, Marcon M, Berger N, Boss A, Ciritsis A, Rossi C. Generalizable attention U-Net for segmentation of fibroglandular tissue and background parenchymal enhancement in breast DCE-MRI. Insights Imaging 2023; 14:185. [PMID: 37932462 PMCID: PMC10628070 DOI: 10.1186/s13244-023-01531-5] [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: 07/11/2023] [Accepted: 09/25/2023] [Indexed: 11/08/2023] Open
Abstract
OBJECTIVES Development of automated segmentation models enabling standardized volumetric quantification of fibroglandular tissue (FGT) from native volumes and background parenchymal enhancement (BPE) from subtraction volumes of dynamic contrast-enhanced breast MRI. Subsequent assessment of the developed models in the context of FGT and BPE Breast Imaging Reporting and Data System (BI-RADS)-compliant classification. METHODS For the training and validation of attention U-Net models, data coming from a single 3.0-T scanner was used. For testing, additional data from 1.5-T scanner and data acquired in a different institution with a 3.0-T scanner was utilized. The developed models were used to quantify the amount of FGT and BPE in 80 DCE-MRI examinations, and a correlation between these volumetric measures and the classes assigned by radiologists was performed. RESULTS To assess the model performance using application-relevant metrics, the correlation between the volumes of breast, FGT, and BPE calculated from ground truth masks and predicted masks was checked. Pearson correlation coefficients ranging from 0.963 ± 0.004 to 0.999 ± 0.001 were achieved. The Spearman correlation coefficient for the quantitative and qualitative assessment, i.e., classification by radiologist, of FGT amounted to 0.70 (p < 0.0001), whereas BPE amounted to 0.37 (p = 0.0006). CONCLUSIONS Generalizable algorithms for FGT and BPE segmentation were developed and tested. Our results suggest that when assessing FGT, it is sufficient to use volumetric measures alone. However, for the evaluation of BPE, additional models considering voxels' intensity distribution and morphology are required. CRITICAL RELEVANCE STATEMENT A standardized assessment of FGT density can rely on volumetric measures, whereas in the case of BPE, the volumetric measures constitute, along with voxels' intensity distribution and morphology, an important factor. KEY POINTS • Our work contributes to the standardization of FGT and BPE assessment. • Attention U-Net can reliably segment intricately shaped FGT and BPE structures. • The developed models were robust to domain shift.
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Affiliation(s)
- Sylwia Nowakowska
- Diagnostic and interventional Radiology, University Hospital Zurich, University Zurich, Rämistrasse 100, 8091, Zurich, Switzerland.
| | | | - Carlotta M Ruppert
- Diagnostic and interventional Radiology, University Hospital Zurich, University Zurich, Rämistrasse 100, 8091, Zurich, Switzerland
| | - Anna Landsmann
- Diagnostic and interventional Radiology, University Hospital Zurich, University Zurich, Rämistrasse 100, 8091, Zurich, Switzerland
| | - Magda Marcon
- Diagnostic and interventional Radiology, University Hospital Zurich, University Zurich, Rämistrasse 100, 8091, Zurich, Switzerland
| | - Nicole Berger
- Diagnostic and interventional Radiology, University Hospital Zurich, University Zurich, Rämistrasse 100, 8091, Zurich, Switzerland
- Present Address: Institut RadiologieSpital Lachen, Oberdorfstrasse 41, 8853, Lachen, Switzerland
| | - Andreas Boss
- Diagnostic and interventional Radiology, University Hospital Zurich, University Zurich, Rämistrasse 100, 8091, Zurich, Switzerland
- Present address: GZO AG Spital Wetzikon, Spitalstrasse 66, 8620, Wetzikon, Switzerland
| | - Alexander Ciritsis
- Diagnostic and interventional Radiology, University Hospital Zurich, University Zurich, Rämistrasse 100, 8091, Zurich, Switzerland
- b-rayZ AG, Wagistrasse 21, 8952, Schlieren, Switzerland
| | - Cristina Rossi
- Diagnostic and interventional Radiology, University Hospital Zurich, University Zurich, Rämistrasse 100, 8091, Zurich, Switzerland
- b-rayZ AG, Wagistrasse 21, 8952, Schlieren, Switzerland
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10
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Su GH, Xiao Y, You C, Zheng RC, Zhao S, Sun SY, Zhou JY, Lin LY, Wang H, Shao ZM, Gu YJ, Jiang YZ. Radiogenomic-based multiomic analysis reveals imaging intratumor heterogeneity phenotypes and therapeutic targets. SCIENCE ADVANCES 2023; 9:eadf0837. [PMID: 37801493 PMCID: PMC10558123 DOI: 10.1126/sciadv.adf0837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Accepted: 09/06/2023] [Indexed: 10/08/2023]
Abstract
Intratumor heterogeneity (ITH) profoundly affects therapeutic responses and clinical outcomes. However, the widespread methods for assessing ITH based on genomic sequencing or pathological slides, which rely on limited tissue samples, may lead to inaccuracies due to potential sampling biases. Using a newly established multicenter breast cancer radio-multiomic dataset (n = 1474) encompassing radiomic features extracted from dynamic contrast-enhanced magnetic resonance images, we formulated a noninvasive radiomics methodology to effectively investigate ITH. Imaging ITH (IITH) was associated with genomic and pathological ITH, predicting poor prognosis independently in breast cancer. Through multiomic analysis, we identified activated oncogenic pathways and metabolic dysregulation in high-IITH tumors. Integrated metabolomic and transcriptomic analyses highlighted ferroptosis as a vulnerability and potential therapeutic target of high-IITH tumors. Collectively, this work emphasizes the superiority of radiomics in capturing ITH. Furthermore, we provide insights into the biological basis of IITH and propose therapeutic targets for breast cancers with elevated IITH.
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Affiliation(s)
- Guan-Hua Su
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Yi Xiao
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Chao You
- Department of Radiology, Fudan University Shanghai Cancer Center and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Ren-Cheng Zheng
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai 201203, China
| | - Shen Zhao
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Shi-Yun Sun
- Department of Radiology, Fudan University Shanghai Cancer Center and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Jia-Yin Zhou
- Department of Radiology, Fudan University Shanghai Cancer Center and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Lu-Yi Lin
- Department of Radiology, Fudan University Shanghai Cancer Center and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - He Wang
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai 201203, China
| | - Zhi-Ming Shao
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Ya-Jia Gu
- Department of Radiology, Fudan University Shanghai Cancer Center and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Yi-Zhou Jiang
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center and Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
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11
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Goodburn R, Kousi E, Sanders C, Macdonald A, Scurr E, Bunce C, Khabra K, Reddy M, Wilkinson L, O'Flynn E, Allen S, Schmidt MA. Quantitative background parenchymal enhancement and fibro-glandular density at breast MRI: Association with BRCA status. Eur Radiol 2023; 33:6204-6212. [PMID: 37017702 PMCID: PMC10415521 DOI: 10.1007/s00330-023-09592-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 02/20/2023] [Accepted: 02/22/2023] [Indexed: 04/06/2023]
Abstract
OBJECTIVES To investigate whether MRI-based measurements of fibro-glandular tissue volume, breast density (MRBD), and background parenchymal enhancement (BPE) could be used to stratify two cohorts of healthy women: BRCA carriers and women at population risk of breast cancer. METHODS Pre-menopausal women aged 40-50 years old were scanned at 3 T, employing a standard breast protocol including a DCE-MRI (35 and 30 participants in high- and low-risk groups, respectively). The dynamic range of the DCE protocol was characterised and both breasts were masked and segmented with minimal user input to produce measurements of fibro-glandular tissue volume, MRBD, and voxelwise BPE. Statistical tests were performed to determine inter- and intra-user repeatability, evaluate the symmetry between metrics derived from left and right breasts, and investigate MRBD and BPE differences between the high- and low-risk cohorts. RESULTS Intra- and inter-user reproducibility in estimates of fibro-glandular tissue volume, MRBD, and median BPE estimations were good, with coefficients of variation < 15%. Coefficients of variation between left and right breasts were also low (< 25%). There were no significant correlations between fibro-glandular tissue volume, MRBD, and BPE for either risk group. However, the high-risk group had higher BPE kurtosis, although linear regression analysis did not reveal significant associations between BPE kurtosis and breast cancer risk. CONCLUSIONS This study found no significant differences or correlations in fibro-glandular tissue volume, MRBD, or BPE metrics between the two groups of women with different levels of breast cancer risk. However, the results support further investigation into the heterogeneity of parenchymal enhancement. KEY POINTS • A semi-automated method enabled quantitative measurements of fibro-glandular tissue volume, breast density, and background parenchymal enhancement with minimal user intervention. • Background parenchymal enhancement was quantified over the entire parenchyma, segmented in pre-contrast images, thus avoiding region selection. • No significant differences and correlations in fibro-glandular tissue volume, breast density, and breast background parenchymal enhancement were found between two cohorts of women at high and low levels of breast cancer risk.
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Affiliation(s)
- Rosie Goodburn
- CRUK Cancer Imaging Centre, The Institute of Cancer Research and Royal Marsden Foundation Trust, London, UK.
- The Royal Marsden NHS Foundation Trust, Sutton, UK.
| | - Evanthia Kousi
- CRUK Cancer Imaging Centre, The Institute of Cancer Research and Royal Marsden Foundation Trust, London, UK
- The Royal Marsden NHS Foundation Trust, Sutton, UK
| | | | | | - Erica Scurr
- The Royal Marsden NHS Foundation Trust, Sutton, UK
| | - Catey Bunce
- The Royal Marsden NHS Foundation Trust, Sutton, UK
| | - Komel Khabra
- The Royal Marsden NHS Foundation Trust, Sutton, UK
| | - Mamatha Reddy
- St Georges University Hospitals NHS Foundation Trust, London, UK
| | | | | | - Steven Allen
- The Royal Marsden NHS Foundation Trust, Sutton, UK
| | - Maria Angélica Schmidt
- CRUK Cancer Imaging Centre, The Institute of Cancer Research and Royal Marsden Foundation Trust, London, UK
- The Royal Marsden NHS Foundation Trust, Sutton, UK
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12
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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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13
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Watt GP, Thakran S, Sung JS, Jochelson MS, Lobbes MBI, Weinstein SP, Bradbury AR, Buys SS, Morris EA, Apte A, Patel P, Woods M, Liang X, Pike MC, Kontos D, Bernstein JL. Association of Breast Cancer Odds with Background Parenchymal Enhancement Quantified Using a Fully Automated Method at MRI: The IMAGINE Study. Radiology 2023; 308:e230367. [PMID: 37750771 PMCID: PMC10546291 DOI: 10.1148/radiol.230367] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 06/13/2023] [Accepted: 06/16/2023] [Indexed: 09/27/2023]
Abstract
Background Background parenchymal enhancement (BPE) at breast MRI has been associated with increased breast cancer risk in several independent studies. However, variability of subjective BPE assessments have precluded its use in clinical practice. Purpose To examine the association between fully objective measures of BPE at MRI and odds of breast cancer. Materials and Methods This prospective case-control study included patients who underwent a bilateral breast MRI examination and were receiving care at one of three centers in the United States from November 2010 to July 2017. Breast volume, fibroglandular tissue (FGT) volume, and BPE were quantified using fully automated software. Fat volume was defined as breast volume minus FGT volume. BPE extent was defined as the proportion of FGT voxels with enhancement of 20% or more. Spearman rank correlation between quantitative BPE extent and Breast Imaging Reporting and Data System (BI-RADS) BPE categories assigned by an experienced board-certified breast radiologist was estimated. With use of multivariable logistic regression, breast cancer case-control status was regressed on tertiles (low, moderate, and high) of BPE, FGT volume, and fat volume, with adjustment for covariates. Results In total, 536 case participants with breast cancer (median age, 48 years [IQR, 43-55 years]) and 940 cancer-free controls (median age, 46 years [IQR, 38-55 years]) were included. BPE extent was positively associated with BI-RADS BPE (rs = 0.54; P < .001). Compared with low BPE extent (range, 2.9%-34.2%), high BPE extent (range, 50.7%-97.3%) was associated with increased odds of breast cancer (odds ratio [OR], 1.74 [95% CI: 1.23, 2.46]; P for trend = .002) in a multivariable model also including FGT volume (OR, 1.39 [95% CI: 0.97, 1.98]) and fat volume (OR, 1.46 [95% CI: 1.04, 2.06]). The association of high BPE extent with increased odds of breast cancer was similar for premenopausal and postmenopausal women (ORs, 1.75 and 1.83, respectively; interaction P = .73). Conclusion Objectively measured BPE at breast MRI is associated with increased breast cancer odds for both premenopausal and postmenopausal women. Clinical trial registration no. NCT02301767 © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Bokacheva in this issue.
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Affiliation(s)
- Gordon P. Watt
- From the Department of Epidemiology and Biostatistics (G.P.W., P.P., M.W., X.L., M.C.P., J.L.B.), Department of Radiology (J.S.S., M.S.J.), and Department of Medical Physics (A.A.), Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065; Department of Radiology, Perelman Center for Advanced Medicine at the University of Pennsylvania, Philadelphia, Pa (S.T., S.P.W., A.R.B., D.K.); Department of Medical Imaging, Zuyderland Medical Center, Sittard-Geleen, the Netherlands (M.B.I.L.); Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, the Netherlands (M.B.I.L.); GROW School for Oncology and Reproduction, Maastricht University, Maastricht, the Netherlands (M.B.I.L.); Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah (S.S.B.); and Department of Radiology, University of California Davis Medical Center, Davis, Calif (E.A.M.)
| | - Snekha Thakran
- From the Department of Epidemiology and Biostatistics (G.P.W., P.P., M.W., X.L., M.C.P., J.L.B.), Department of Radiology (J.S.S., M.S.J.), and Department of Medical Physics (A.A.), Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065; Department of Radiology, Perelman Center for Advanced Medicine at the University of Pennsylvania, Philadelphia, Pa (S.T., S.P.W., A.R.B., D.K.); Department of Medical Imaging, Zuyderland Medical Center, Sittard-Geleen, the Netherlands (M.B.I.L.); Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, the Netherlands (M.B.I.L.); GROW School for Oncology and Reproduction, Maastricht University, Maastricht, the Netherlands (M.B.I.L.); Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah (S.S.B.); and Department of Radiology, University of California Davis Medical Center, Davis, Calif (E.A.M.)
| | - Janice S. Sung
- From the Department of Epidemiology and Biostatistics (G.P.W., P.P., M.W., X.L., M.C.P., J.L.B.), Department of Radiology (J.S.S., M.S.J.), and Department of Medical Physics (A.A.), Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065; Department of Radiology, Perelman Center for Advanced Medicine at the University of Pennsylvania, Philadelphia, Pa (S.T., S.P.W., A.R.B., D.K.); Department of Medical Imaging, Zuyderland Medical Center, Sittard-Geleen, the Netherlands (M.B.I.L.); Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, the Netherlands (M.B.I.L.); GROW School for Oncology and Reproduction, Maastricht University, Maastricht, the Netherlands (M.B.I.L.); Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah (S.S.B.); and Department of Radiology, University of California Davis Medical Center, Davis, Calif (E.A.M.)
| | - Maxine S. Jochelson
- From the Department of Epidemiology and Biostatistics (G.P.W., P.P., M.W., X.L., M.C.P., J.L.B.), Department of Radiology (J.S.S., M.S.J.), and Department of Medical Physics (A.A.), Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065; Department of Radiology, Perelman Center for Advanced Medicine at the University of Pennsylvania, Philadelphia, Pa (S.T., S.P.W., A.R.B., D.K.); Department of Medical Imaging, Zuyderland Medical Center, Sittard-Geleen, the Netherlands (M.B.I.L.); Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, the Netherlands (M.B.I.L.); GROW School for Oncology and Reproduction, Maastricht University, Maastricht, the Netherlands (M.B.I.L.); Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah (S.S.B.); and Department of Radiology, University of California Davis Medical Center, Davis, Calif (E.A.M.)
| | - Marc B. I. Lobbes
- From the Department of Epidemiology and Biostatistics (G.P.W., P.P., M.W., X.L., M.C.P., J.L.B.), Department of Radiology (J.S.S., M.S.J.), and Department of Medical Physics (A.A.), Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065; Department of Radiology, Perelman Center for Advanced Medicine at the University of Pennsylvania, Philadelphia, Pa (S.T., S.P.W., A.R.B., D.K.); Department of Medical Imaging, Zuyderland Medical Center, Sittard-Geleen, the Netherlands (M.B.I.L.); Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, the Netherlands (M.B.I.L.); GROW School for Oncology and Reproduction, Maastricht University, Maastricht, the Netherlands (M.B.I.L.); Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah (S.S.B.); and Department of Radiology, University of California Davis Medical Center, Davis, Calif (E.A.M.)
| | - Susan P. Weinstein
- From the Department of Epidemiology and Biostatistics (G.P.W., P.P., M.W., X.L., M.C.P., J.L.B.), Department of Radiology (J.S.S., M.S.J.), and Department of Medical Physics (A.A.), Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065; Department of Radiology, Perelman Center for Advanced Medicine at the University of Pennsylvania, Philadelphia, Pa (S.T., S.P.W., A.R.B., D.K.); Department of Medical Imaging, Zuyderland Medical Center, Sittard-Geleen, the Netherlands (M.B.I.L.); Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, the Netherlands (M.B.I.L.); GROW School for Oncology and Reproduction, Maastricht University, Maastricht, the Netherlands (M.B.I.L.); Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah (S.S.B.); and Department of Radiology, University of California Davis Medical Center, Davis, Calif (E.A.M.)
| | - Angela R. Bradbury
- From the Department of Epidemiology and Biostatistics (G.P.W., P.P., M.W., X.L., M.C.P., J.L.B.), Department of Radiology (J.S.S., M.S.J.), and Department of Medical Physics (A.A.), Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065; Department of Radiology, Perelman Center for Advanced Medicine at the University of Pennsylvania, Philadelphia, Pa (S.T., S.P.W., A.R.B., D.K.); Department of Medical Imaging, Zuyderland Medical Center, Sittard-Geleen, the Netherlands (M.B.I.L.); Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, the Netherlands (M.B.I.L.); GROW School for Oncology and Reproduction, Maastricht University, Maastricht, the Netherlands (M.B.I.L.); Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah (S.S.B.); and Department of Radiology, University of California Davis Medical Center, Davis, Calif (E.A.M.)
| | - Saundra S. Buys
- From the Department of Epidemiology and Biostatistics (G.P.W., P.P., M.W., X.L., M.C.P., J.L.B.), Department of Radiology (J.S.S., M.S.J.), and Department of Medical Physics (A.A.), Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065; Department of Radiology, Perelman Center for Advanced Medicine at the University of Pennsylvania, Philadelphia, Pa (S.T., S.P.W., A.R.B., D.K.); Department of Medical Imaging, Zuyderland Medical Center, Sittard-Geleen, the Netherlands (M.B.I.L.); Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, the Netherlands (M.B.I.L.); GROW School for Oncology and Reproduction, Maastricht University, Maastricht, the Netherlands (M.B.I.L.); Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah (S.S.B.); and Department of Radiology, University of California Davis Medical Center, Davis, Calif (E.A.M.)
| | - Elizabeth A. Morris
- From the Department of Epidemiology and Biostatistics (G.P.W., P.P., M.W., X.L., M.C.P., J.L.B.), Department of Radiology (J.S.S., M.S.J.), and Department of Medical Physics (A.A.), Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065; Department of Radiology, Perelman Center for Advanced Medicine at the University of Pennsylvania, Philadelphia, Pa (S.T., S.P.W., A.R.B., D.K.); Department of Medical Imaging, Zuyderland Medical Center, Sittard-Geleen, the Netherlands (M.B.I.L.); Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, the Netherlands (M.B.I.L.); GROW School for Oncology and Reproduction, Maastricht University, Maastricht, the Netherlands (M.B.I.L.); Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah (S.S.B.); and Department of Radiology, University of California Davis Medical Center, Davis, Calif (E.A.M.)
| | - Aditya Apte
- From the Department of Epidemiology and Biostatistics (G.P.W., P.P., M.W., X.L., M.C.P., J.L.B.), Department of Radiology (J.S.S., M.S.J.), and Department of Medical Physics (A.A.), Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065; Department of Radiology, Perelman Center for Advanced Medicine at the University of Pennsylvania, Philadelphia, Pa (S.T., S.P.W., A.R.B., D.K.); Department of Medical Imaging, Zuyderland Medical Center, Sittard-Geleen, the Netherlands (M.B.I.L.); Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, the Netherlands (M.B.I.L.); GROW School for Oncology and Reproduction, Maastricht University, Maastricht, the Netherlands (M.B.I.L.); Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah (S.S.B.); and Department of Radiology, University of California Davis Medical Center, Davis, Calif (E.A.M.)
| | - Prusha Patel
- From the Department of Epidemiology and Biostatistics (G.P.W., P.P., M.W., X.L., M.C.P., J.L.B.), Department of Radiology (J.S.S., M.S.J.), and Department of Medical Physics (A.A.), Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065; Department of Radiology, Perelman Center for Advanced Medicine at the University of Pennsylvania, Philadelphia, Pa (S.T., S.P.W., A.R.B., D.K.); Department of Medical Imaging, Zuyderland Medical Center, Sittard-Geleen, the Netherlands (M.B.I.L.); Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, the Netherlands (M.B.I.L.); GROW School for Oncology and Reproduction, Maastricht University, Maastricht, the Netherlands (M.B.I.L.); Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah (S.S.B.); and Department of Radiology, University of California Davis Medical Center, Davis, Calif (E.A.M.)
| | - Meghan Woods
- From the Department of Epidemiology and Biostatistics (G.P.W., P.P., M.W., X.L., M.C.P., J.L.B.), Department of Radiology (J.S.S., M.S.J.), and Department of Medical Physics (A.A.), Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065; Department of Radiology, Perelman Center for Advanced Medicine at the University of Pennsylvania, Philadelphia, Pa (S.T., S.P.W., A.R.B., D.K.); Department of Medical Imaging, Zuyderland Medical Center, Sittard-Geleen, the Netherlands (M.B.I.L.); Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, the Netherlands (M.B.I.L.); GROW School for Oncology and Reproduction, Maastricht University, Maastricht, the Netherlands (M.B.I.L.); Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah (S.S.B.); and Department of Radiology, University of California Davis Medical Center, Davis, Calif (E.A.M.)
| | - Xiaolin Liang
- From the Department of Epidemiology and Biostatistics (G.P.W., P.P., M.W., X.L., M.C.P., J.L.B.), Department of Radiology (J.S.S., M.S.J.), and Department of Medical Physics (A.A.), Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065; Department of Radiology, Perelman Center for Advanced Medicine at the University of Pennsylvania, Philadelphia, Pa (S.T., S.P.W., A.R.B., D.K.); Department of Medical Imaging, Zuyderland Medical Center, Sittard-Geleen, the Netherlands (M.B.I.L.); Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, the Netherlands (M.B.I.L.); GROW School for Oncology and Reproduction, Maastricht University, Maastricht, the Netherlands (M.B.I.L.); Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah (S.S.B.); and Department of Radiology, University of California Davis Medical Center, Davis, Calif (E.A.M.)
| | - Malcolm C. Pike
- From the Department of Epidemiology and Biostatistics (G.P.W., P.P., M.W., X.L., M.C.P., J.L.B.), Department of Radiology (J.S.S., M.S.J.), and Department of Medical Physics (A.A.), Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065; Department of Radiology, Perelman Center for Advanced Medicine at the University of Pennsylvania, Philadelphia, Pa (S.T., S.P.W., A.R.B., D.K.); Department of Medical Imaging, Zuyderland Medical Center, Sittard-Geleen, the Netherlands (M.B.I.L.); Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, the Netherlands (M.B.I.L.); GROW School for Oncology and Reproduction, Maastricht University, Maastricht, the Netherlands (M.B.I.L.); Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah (S.S.B.); and Department of Radiology, University of California Davis Medical Center, Davis, Calif (E.A.M.)
| | - Despina Kontos
- From the Department of Epidemiology and Biostatistics (G.P.W., P.P., M.W., X.L., M.C.P., J.L.B.), Department of Radiology (J.S.S., M.S.J.), and Department of Medical Physics (A.A.), Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065; Department of Radiology, Perelman Center for Advanced Medicine at the University of Pennsylvania, Philadelphia, Pa (S.T., S.P.W., A.R.B., D.K.); Department of Medical Imaging, Zuyderland Medical Center, Sittard-Geleen, the Netherlands (M.B.I.L.); Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, the Netherlands (M.B.I.L.); GROW School for Oncology and Reproduction, Maastricht University, Maastricht, the Netherlands (M.B.I.L.); Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah (S.S.B.); and Department of Radiology, University of California Davis Medical Center, Davis, Calif (E.A.M.)
| | - Jonine L. Bernstein
- From the Department of Epidemiology and Biostatistics (G.P.W., P.P., M.W., X.L., M.C.P., J.L.B.), Department of Radiology (J.S.S., M.S.J.), and Department of Medical Physics (A.A.), Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065; Department of Radiology, Perelman Center for Advanced Medicine at the University of Pennsylvania, Philadelphia, Pa (S.T., S.P.W., A.R.B., D.K.); Department of Medical Imaging, Zuyderland Medical Center, Sittard-Geleen, the Netherlands (M.B.I.L.); Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, the Netherlands (M.B.I.L.); GROW School for Oncology and Reproduction, Maastricht University, Maastricht, the Netherlands (M.B.I.L.); Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah (S.S.B.); and Department of Radiology, University of California Davis Medical Center, Davis, Calif (E.A.M.)
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14
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Wu Z, Lin Q, Wang H, Wang G, Fu G, Bian T. An MRI-Based Radiomics Nomogram to Distinguish Ductal Carcinoma In Situ with Microinvasion From Ductal Carcinoma In Situ of Breast Cancer. Acad Radiol 2023; 30 Suppl 2:S71-S81. [PMID: 37211478 DOI: 10.1016/j.acra.2023.03.038] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 03/24/2023] [Accepted: 03/25/2023] [Indexed: 05/23/2023]
Abstract
RATIONALE AND OBJECTIVES Accurate preoperative differentiation between ductal carcinoma in situ with microinvasion (DCISM) and ductal carcinoma in situ (DCIS) could facilitate treatment optimization and individualized risk assessment. The present study aims to build and validate a radiomics nomogram based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) that could distinguish DCISM from pure DCIS breast cancer. MATERIALS AND METHODS MR images of 140 patients obtained between March 2019 and November 2022 at our institution were included. Patients were randomly divided into a training (n = 97) and a test set (n = 43). Patients in both sets were further split into DCIS and DCISM subgroups. The independent clinical risk factors were selected by multivariate logistic regression to establish the clinical model. The optimal radiomics features were chosen by the least absolute shrinkage and selection operator, and a radiomics signature was built. The nomogram model was constructed by integrating the radiomics signature and independent risk factors. The discrimination efficacy of our nomogram was assessed by using calibration and decision curves. RESULTS Six features were selected to construct the radiomics signature for distinguishing DCISM from DCIS. The radiomics signature and nomogram model exhibited better calibration and validation performance in the training (AUC 0.815, 0.911, 95% confidence interval [CI], 0.703-0.926, 0.848-0.974) and test (AUC 0.830, 0.882, 95% CI, 0.672-0.989, 0.764-0.999) sets than in the clinical factor model (AUC 0.672, 0.717, 95% CI, 0.544-0.801, 0.527-0.907). The decision curve also demonstrated that the nomogram model exhibited good clinical utility. CONCLUSION The proposed noninvasive MRI-based radiomics nomogram model showed good performance in distinguishing DCISM from DCIS.
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Affiliation(s)
- Zengjie Wu
- Department of Radiology, the Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong, China (Z.W.)
| | - Qing Lin
- Breast Disease Center, the Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong, China (Q.L., H.W., T.B.)
| | - Haibo Wang
- Breast Disease Center, the Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong, China (Q.L., H.W., T.B.)
| | - Guanqun Wang
- Department of Pathology, the Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong, China (G.W., G.F.)
| | - Guangming Fu
- Department of Pathology, the Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong, China (G.W., G.F.)
| | - Tiantian Bian
- Breast Disease Center, the Affiliated Hospital of Qingdao University, Qingdao 266000, Shandong, China (Q.L., H.W., T.B.).
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15
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Müller-Franzes G, Müller-Franzes F, Huck L, Raaff V, Kemmer E, Khader F, Arasteh ST, Lemainque T, Kather JN, Nebelung S, Kuhl C, Truhn D. Fibroglandular tissue segmentation in breast MRI using vision transformers: a multi-institutional evaluation. Sci Rep 2023; 13:14207. [PMID: 37648728 PMCID: PMC10468506 DOI: 10.1038/s41598-023-41331-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 08/24/2023] [Indexed: 09/01/2023] Open
Abstract
Accurate and automatic segmentation of fibroglandular tissue in breast MRI screening is essential for the quantification of breast density and background parenchymal enhancement. In this retrospective study, we developed and evaluated a transformer-based neural network for breast segmentation (TraBS) in multi-institutional MRI data, and compared its performance to the well established convolutional neural network nnUNet. TraBS and nnUNet were trained and tested on 200 internal and 40 external breast MRI examinations using manual segmentations generated by experienced human readers. Segmentation performance was assessed in terms of the Dice score and the average symmetric surface distance. The Dice score for nnUNet was lower than for TraBS on the internal testset (0.909 ± 0.069 versus 0.916 ± 0.067, P < 0.001) and on the external testset (0.824 ± 0.144 versus 0.864 ± 0.081, P = 0.004). Moreover, the average symmetric surface distance was higher (= worse) for nnUNet than for TraBS on the internal (0.657 ± 2.856 versus 0.548 ± 2.195, P = 0.001) and on the external testset (0.727 ± 0.620 versus 0.584 ± 0.413, P = 0.03). Our study demonstrates that transformer-based networks improve the quality of fibroglandular tissue segmentation in breast MRI compared to convolutional-based models like nnUNet. These findings might help to enhance the accuracy of breast density and parenchymal enhancement quantification in breast MRI screening.
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Affiliation(s)
- Gustav Müller-Franzes
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH, Aachen, Germany
| | - Fritz Müller-Franzes
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH, Aachen, Germany
| | - Luisa Huck
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH, Aachen, Germany
| | - Vanessa Raaff
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH, Aachen, Germany
| | - Eva Kemmer
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH, Aachen, Germany
| | - Firas Khader
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH, Aachen, Germany
| | - Soroosh Tayebi Arasteh
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH, Aachen, Germany
| | - Teresa Lemainque
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH, Aachen, Germany
| | - Jakob Nikolas Kather
- Else Kroener Fresenius Center for Digital Health, Technical University, Dresden, Germany
- Department of Medicine III, University Hospital RWTH, Aachen, Germany
| | - Sven Nebelung
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH, Aachen, Germany
| | - Christiane Kuhl
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH, Aachen, Germany
| | - Daniel Truhn
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH, Aachen, Germany.
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16
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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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17
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Gastounioti A, Cohen EA, Pantalone L, Ehsan S, Vasudevan S, Kurudi A, Conant EF, Chen J, Kontos D, McCarthy AM. Changes in mammographic density and risk of breast cancer among a diverse cohort of women undergoing mammography screening. Breast Cancer Res Treat 2023; 198:535-544. [PMID: 36800118 DOI: 10.1007/s10549-023-06879-2] [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: 06/18/2022] [Accepted: 02/01/2023] [Indexed: 02/18/2023]
Abstract
PURPOSE Mammographic density (MD) is a strong breast cancer risk factor. MD may change over time, with potential implications for breast cancer risk. Few studies have assessed associations between MD change and breast cancer in racially diverse populations. We investigated the relationships between MD and MD change over time and breast cancer risk in a large, diverse screening cohort. MATERIALS AND METHODS We retrospectively analyzed data from 8462 women who underwent ≥ 2 screening mammograms from Sept. 2010 to Jan. 2015 (N = 20,766 exams); 185 breast cancers were diagnosed 1-7 years after screening. Breast percent density (PD) and dense area (DA) were estimated from raw digital mammograms (Hologic Inc.) using LIBRA (v1.0.4). For each MD measure, we modeled breast density change between two sequential visits as a function of demographic and risk covariates. We used Cox regression to examine whether varying degrees of breast density change were associated with breast cancer risk, accounting for multiple exams per woman. RESULTS PD at any screen was significantly associated with breast cancer risk (hazard ratio (HR) for PD = 1.03 (95% CI [1.01, 1.05], p < 0.0005), but neither change in breast density nor more extreme than expected changes in breast density were associated with breast cancer risk. We found no evidence of differences in density change or breast cancer risk due to density change by race. Results using DA were essentially identical. CONCLUSIONS Using a large racially diverse cohort, we found no evidence of association between short-term change in MD and risk of breast cancer, suggesting that short-term MD change is not a strong predictor for risk.
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Affiliation(s)
- Aimilia Gastounioti
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Eric A Cohen
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Lauren Pantalone
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sarah Ehsan
- Department of Biostatistics, Epidemiology & Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sanjana Vasudevan
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Avinash Kurudi
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Emily F Conant
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jinbo Chen
- Department of Biostatistics, Epidemiology & Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Despina Kontos
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Anne Marie McCarthy
- Department of Biostatistics, Epidemiology & Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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18
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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: 17] [Impact Index Per Article: 17.0] [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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19
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Brown JC, Ligibel JA, Crane TE, Kontos D, Yang S, Conant EF, Mack JA, Ahima RS, Schmitz KH. Obesity and metabolic dysfunction correlate with background parenchymal enhancement in premenopausal women. Obesity (Silver Spring) 2023; 31:479-486. [PMID: 36628617 PMCID: PMC10141499 DOI: 10.1002/oby.23649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Revised: 10/14/2022] [Accepted: 10/17/2022] [Indexed: 01/12/2023]
Abstract
OBJECTIVE This study tested the hypothesis that obesity and metabolic abnormalities correlate with background parenchymal enhancement (BPE), the volume and intensity of enhancing fibroglandular breast tissue on dynamic contrast-enhanced magnetic resonance imaging. METHODS Participants included 59 premenopausal women at high risk of breast cancer. Obesity was defined as BMI ≥ 30 kg/m2 . Metabolic parameters included dual-energy x-ray absorptiometry-quantified body composition, plasma biomarkers of insulin resistance, adipokines, inflammation, lipids, and urinary sex hormones. BPE was assessed using computerized algorithms on dynamic contrast-enhanced magnetic resonance imaging. RESULTS BMI was positively correlated with BPE (r = 0.69; p < 0.001); participants with obesity had higher BPE than those without obesity (404.9 ± 189.6 vs. 261.8 ± 143.8 cm2 ; Δ: 143.1 cm2 [95% CI: 49.5-236.7]; p = 0.003). Total body fat mass (r = 0.68; p < 0.001), body fat percentage (r = 0.64; p < 0.001), visceral adipose tissue area (r = 0.65; p < 0.001), subcutaneous adipose tissue area (r = 0.60; p < 0.001), insulin (r = 0.59; p < 0.001), glucose (r = 0.35; p = 0.011), homeostatic model of insulin resistance (r = 0.62; p < 0.001), and leptin (r = 0.60; p < 0.001) were positively correlated with BPE. Adiponectin (r = -0.44; p < 0.001) was negatively correlated with BPE. Plasma biomarkers of inflammation and lipids and urinary sex hormones were not correlated with BPE. CONCLUSIONS In premenopausal women at high risk of breast cancer, increased BPE is associated with obesity, insulin resistance, leptin, and adiponectin.
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Affiliation(s)
- Justin C. Brown
- Pennington Biomedical Research Center, 6400 Perkins Rd, Baton Rouge, LA 70808, USA
- LSU Health Sciences Center New Orleans School of Medicine, 1901 Perdido St, New Orleans, LA 70112, USA
- Stanley S. Scott Cancer Center, Louisiana State University Health Sciences Center, 533 Bolivar St, New Orleans, LA, 70112, USA
| | | | - Tracy E. Crane
- University of Miami, Miller School of Medicine, 1600 NW 10 Ave, Miami, FL 33136
| | - Despina Kontos
- University of Pennsylvania, Perelman School of Medicine, 3400 Civic Center, Boulevard, Philadelphia, PA, 10104
| | - Shengping Yang
- Pennington Biomedical Research Center, 6400 Perkins Rd, Baton Rouge, LA 70808, USA
| | - Emily F. Conant
- University of Pennsylvania, Perelman School of Medicine, 3400 Civic Center, Boulevard, Philadelphia, PA, 10104
| | - Julie A. Mack
- Penn State College of Medicine, 500 University Drive, Hershey, PA 17033
| | - Rexford S. Ahima
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, Johns Hopkins University School of Medicine, 1830 E. Monument St., Baltimore, MD 21287
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20
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Niell BL. Background Parenchymal Enhancement at Breast MRI: More Is Not Better. Radiology 2023; 306:100-101. [PMID: 36040339 PMCID: PMC9792709 DOI: 10.1148/radiol.221901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 08/15/2022] [Accepted: 08/16/2022] [Indexed: 01/19/2023]
Affiliation(s)
- Bethany L. Niell
- From the Division of Breast Imaging, Department of Diagnostic Imaging
and Interventional Radiology, H. Lee Moffitt Cancer Center and Research
Institute, 12902 USF Magnolia Dr, Tampa, FL 33612; and Department of Oncologic
Sciences, Morsani College of Medicine, University of South Florida, Tampa,
Fla
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21
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Rahmat K, Mumin NA, Hamid MTR, Hamid SA, Ng WL. MRI Breast: Current Imaging Trends, Clinical Applications, and Future Research Directions. Curr Med Imaging 2022; 18:1347-1361. [PMID: 35430976 DOI: 10.2174/1573405618666220415130131] [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: 12/13/2021] [Revised: 02/11/2022] [Accepted: 03/02/2022] [Indexed: 01/25/2023]
Abstract
Magnetic Resonance Imaging (MRI) is the most sensitive and advanced imaging technique in diagnosing breast cancer and is essential in improving cancer detection, lesion characterization, and determining therapy response. In addition to the dynamic contrast-enhanced (DCE) technique, functional techniques such as magnetic resonance spectroscopy (MRS), diffusion-weighted imaging (DWI), diffusion kurtosis imaging (DKI), and intravoxel incoherent motion (IVIM) further characterize and differentiate benign and malignant lesions thus, improving diagnostic accuracy. There is now an increasing clinical usage of MRI breast, including screening in high risk and supplementary screening tools in average-risk patients. MRI is becoming imperative in assisting breast surgeons in planning breast-conserving surgery for preoperative local staging and evaluation of neoadjuvant chemotherapy response. Other clinical applications for MRI breast include occult breast cancer detection, investigation of nipple discharge, and breast implant assessment. There is now an abundance of research publications on MRI Breast with several areas that still remain to be explored. This review gives a comprehensive overview of the clinical trends of MRI breast with emphasis on imaging features and interpretation using conventional and advanced techniques. In addition, future research areas in MRI breast include developing techniques to make MRI more accessible and costeffective for screening. The abbreviated MRI breast procedure and an area of focused research in the enhancement of radiologists' work with artificial intelligence have high impact for the future in MRI Breast.
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Affiliation(s)
- Kartini Rahmat
- Department of Biomedical Imaging, University Malaya Research Imaging Centre, Faculty of Medicine, Kuala Lumpur, Malaysia
| | - Nazimah Ab Mumin
- Department of Radiology, Faculty of Medicine, University Teknologi MARA, Sungai Buloh, Selangor, Malaysia
| | - Marlina Tanty Ramli Hamid
- Department of Radiology, Faculty of Medicine, University Teknologi MARA, Sungai Buloh, Selangor, Malaysia
| | - Shamsiah Abdul Hamid
- Department of Radiology, Faculty of Medicine, University Teknologi MARA, Sungai Buloh, Selangor, Malaysia
| | - Wei Lin Ng
- Department of Biomedical Imaging, University Malaya Research Imaging Centre, Faculty of Medicine, Kuala Lumpur, Malaysia
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22
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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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23
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The potential of predictive and prognostic breast MRI (P2-bMRI). Eur Radiol Exp 2022; 6:42. [PMID: 35989400 PMCID: PMC9393116 DOI: 10.1186/s41747-022-00291-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 06/08/2022] [Indexed: 11/10/2022] Open
Abstract
Magnetic resonance imaging (MRI) is an important part of breast cancer diagnosis and multimodal workup. It provides unsurpassed soft tissue contrast to analyse the underlying pathophysiology, and it is adopted for a variety of clinical indications. Predictive and prognostic breast MRI (P2-bMRI) is an emerging application next to these indications. The general objective of P2-bMRI is to provide predictive and/or prognostic biomarkers in order to support personalisation of breast cancer treatment. We believe P2-bMRI has a great clinical potential, thanks to the in vivo examination of the whole tumour and of the surrounding tissue, establishing a link between pathophysiology and response to therapy (prediction) as well as patient outcome (prognostication). The tools used for P2-bMRI cover a wide spectrum: standard and advanced multiparametric pulse sequences; structured reporting criteria (for instance BI-RADS descriptors); artificial intelligence methods, including machine learning (with emphasis on radiomics data analysis); and deep learning that have shown compelling potential for this purpose. P2-bMRI reuses the imaging data of examinations performed in the current practice. Accordingly, P2-bMRI could optimise clinical workflow, enabling cost savings and ultimately improving personalisation of treatment. This review introduces the concept of P2-bMRI, focusing on the clinical application of P2-bMRI by using semantic criteria.
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Dwivedi AK. How to write statistical analysis section in medical research. J Investig Med 2022; 70:1759-1770. [PMID: 35710142 DOI: 10.1136/jim-2022-002479] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/01/2022] [Indexed: 12/15/2022]
Abstract
Reporting of statistical analysis is essential in any clinical and translational research study. However, medical research studies sometimes report statistical analysis that is either inappropriate or insufficient to attest to the accuracy and validity of findings and conclusions. Published works involving inaccurate statistical analyses and insufficient reporting influence the conduct of future scientific studies, including meta-analyses and medical decisions. Although the biostatistical practice has been improved over the years due to the involvement of statistical reviewers and collaborators in research studies, there remain areas of improvement for transparent reporting of the statistical analysis section in a study. Evidence-based biostatistics practice throughout the research is useful for generating reliable data and translating meaningful data to meaningful interpretation and decisions in medical research. Most existing research reporting guidelines do not provide guidance for reporting methods in the statistical analysis section that helps in evaluating the quality of findings and data interpretation. In this report, we highlight the global and critical steps to be reported in the statistical analysis of grants and research articles. We provide clarity and the importance of understanding study objective types, data generation process, effect size use, evidence-based biostatistical methods use, and development of statistical models through several thematic frameworks. We also provide published examples of adherence or non-adherence to methodological standards related to each step in the statistical analysis and their implications. We believe the suggestions provided in this report can have far-reaching implications for education and strengthening the quality of statistical reporting and biostatistical practice in medical research.
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Affiliation(s)
- Alok Kumar Dwivedi
- Department of Molecular and Translational Medicine, Division of Biostatistics and Epidemiology, Texas Tech University Health Sciences Center El Paso, El Paso, Texas, USA
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Loi L, Goerke S, Zimmermann F, Korzowski A, Meissner JE, Breitling J, Schott S, Bachert P, Ladd ME, Schlemmer HP, Bickelhaupt S, Paech D. Assessing the influence of the menstrual cycle on APT CEST-MRI in the human breast. Magn Reson Imaging 2022; 91:24-31. [PMID: 35550841 DOI: 10.1016/j.mri.2022.05.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Revised: 05/05/2022] [Accepted: 05/05/2022] [Indexed: 10/18/2022]
Abstract
PURPOSE In fibroglandular breast tissue, conventional dynamic contrast-enhanced MR-mammography is known to be affected by water content changes during the menstrual cycle. Likewise, amide proton transfer (APT) chemical exchange saturation transfer (CEST)-MRI might be inherently prone to the menstrual cycle, as CEST signals are indirectly detected via the water signal. The purpose of this study was to investigate the influence of the menstrual cycle on APT CEST-MRI in fibroglandular breast tissue. METHOD Ten healthy premenopausal women (19-34 years) were included in this IRB approved prospective study and examined twice during their menstrual cycle. Examination one and two were performed during the first half (day 2-8) and the second half (day 15-21) of the menstrual cycle, respectively. As a reference for the APT signal in malignant breast tumor tissue, previously reported data of nine breast cancer patients were included in this study. CEST-MRI (B1 = 0.7μT) was performed on a 7 T whole-body scanner followed by a multi-Lorentzian fit analysis. The APT signal was corrected for B0/B1-field inhomogeneities, fat signal contribution, and relaxation effects of the water signal and evaluated in the fibroglandular breast tissue. Intra-individual APT signal differences between examination one and two were compared using the Wilcoxon signed-rank test. The level of significance was set at p < 0.05. RESULTS The APT signal showed no significant difference in the fibroglandular breast tissue of healthy premenopausal volunteers throughout the menstrual cycle (p = 1.00) (examination 1 vs. examination 2: mean and standard deviation = 3.24 ± 0.68%Hz vs. 3.30 ± 0.73%Hz, median and IQR = 3.36%Hz and 0.87%Hz vs. 3.38%Hz and 0.71%Hz). CONCLUSION The present study provides an important basis for the clinical application of APT CEST-MRI as an additional contrast mechanism in MR-mammography, as menstrual cycle-related APT signal fluctuations seem to be negligible compared to the APT signal increase in breast cancer tissue.
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Affiliation(s)
- Lisa Loi
- German Cancer Research Center, Department of Radiology, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany; Faculty of Medicine, University of Heidelberg, Im Neuenheimer Feld 672, 69120 Heidelberg, Germany
| | - Steffen Goerke
- German Cancer Research Center, Department of Medical Physics in Radiology, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
| | - Ferdinand Zimmermann
- German Cancer Research Center, Department of Medical Physics in Radiology, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
| | - Andreas Korzowski
- German Cancer Research Center, Department of Medical Physics in Radiology, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
| | - Jan-Eric Meissner
- German Cancer Research Center, Department of Medical Physics in Radiology, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
| | - Johannes Breitling
- German Cancer Research Center, Department of Medical Physics in Radiology, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
| | - Sarah Schott
- University Hospital Heidelberg, Department of Gynecology and Obstetrics, Im Neuenheimer Feld 440, 69120 Heidelberg, Germany
| | - Peter Bachert
- German Cancer Research Center, Department of Medical Physics in Radiology, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany; Faculty of Physics and Astronomy, University of Heidelberg, Im Neuenheimer Feld 226, 69120 Heidelberg, Germany
| | - Mark E Ladd
- Faculty of Medicine, University of Heidelberg, Im Neuenheimer Feld 672, 69120 Heidelberg, Germany; German Cancer Research Center, Department of Medical Physics in Radiology, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany; Faculty of Physics and Astronomy, University of Heidelberg, Im Neuenheimer Feld 226, 69120 Heidelberg, Germany
| | - Heinz-Peter Schlemmer
- German Cancer Research Center, Department of Radiology, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany; Faculty of Medicine, University of Heidelberg, Im Neuenheimer Feld 672, 69120 Heidelberg, Germany
| | - Sebastian Bickelhaupt
- German Cancer Research Center, Department of Radiology, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany; German Cancer Research Center, Junior Group Medical Imaging and Radiology - Cancer Prevention, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
| | - Daniel Paech
- German Cancer Research Center, Department of Radiology, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany.
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Rella R, Bufi E, Belli P, Scrofani AR, Petta F, Borghetti A, Marazzi F, Valentini V, Manfredi R. Association between contralateral background parenchymal enhancement on MRI and outcome in patients with unilateral invasive breast cancer breast receiving neoadjuvant chemotherapy. Diagn Interv Imaging 2022; 103:486-494. [DOI: 10.1016/j.diii.2022.04.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 04/08/2022] [Accepted: 04/10/2022] [Indexed: 11/03/2022]
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Park GE, Kim SH, Lee EB, Nam Y, Sung W. Ipsilateral Recurrence of DCIS in Relation to Radiomics Features on Contrast Enhanced Breast MRI. Tomography 2022; 8:596-606. [PMID: 35314626 PMCID: PMC8938812 DOI: 10.3390/tomography8020049] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Revised: 02/13/2022] [Accepted: 02/25/2022] [Indexed: 12/20/2022] Open
Abstract
The purpose of this retrospective study was to investigate the association between ipsilateral recurrence of ductal carcinoma in situ (DCIS) and radiomics features from DCIS and contralateral normal breast on contrast enhanced breast MR imaging. A total of 163 patients with DCIS who underwent preoperative MR imaging between January 2010 and December 2014 were included (training cohort; n = 117, validation cohort; n = 46). Radiomics features were extracted from whole tumor volume of DCIS on early dynamic T1-subtraction images and from the contralateral normal breast on precontrast T1 and early dynamic T1-subtraction images. After feature selection, a Rad-score was established by LASSO Cox regression model. Performance of Rad-score was evaluated by the receiver operating characteristic (ROC) curve and Kaplan Meier curve with log rank test. The Rad-score was significantly associated with ipsilateral recurrence free survival (RFS). The low-risk group with a low Rad-score showed higher ipsilateral RFS than the high-risk group with a high Rad-score in both training and validation cohorts (p < 0.01). The Rad-score based on radiomics features from DCIS and contralateral normal breast on breast MR imaging showed the potential for prediction of ipsilateral RFS of DCIS.
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Affiliation(s)
- Ga Eun Park
- Department of Radiology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea; (G.E.P.); (E.B.L.)
| | - Sung Hun Kim
- Department of Radiology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea; (G.E.P.); (E.B.L.)
- Correspondence: ; Tel.: +82-2-2258-6250
| | - Eun Byul Lee
- Department of Radiology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea; (G.E.P.); (E.B.L.)
| | - Yoonho Nam
- Division of Biomedical Engineering, Hankuk University of Foreign Studies, Yongin 17035, Korea;
| | - Wonmo Sung
- Department of Biomedical Engineering, Research Institute of Biomedical Engineering, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea;
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Mathelin C, Barranger E, Boisserie-Lacroix M, Boutet G, Brousse S, Chabbert-Buffet N, Coutant C, Daraï E, Delpech Y, Duraes M, Espié M, Fornecker L, Golfier F, Grosclaude P, Hamy AS, Kermarrec E, Lavoué V, Lodi M, Luporsi É, Maugard CM, Molière S, Seror JY, Taris N, Uzan C, Vaysse C, Fritel X. [Non-genetic indications for risk reducing mastectomies: Guidelines of the National College of French Gynecologists and Obstetricians (CNGOF)]. GYNECOLOGIE, OBSTETRIQUE, FERTILITE & SENOLOGIE 2022; 50:107-120. [PMID: 34920167 DOI: 10.1016/j.gofs.2021.12.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
OBJECTIVE To determine the value of performing a risk-reducting mastectomy (RRM) in the absence of a deleterious variant of a breast cancer susceptibility gene, in 4 clinical situations at risk of breast cancer. DESIGN The CNGOF Commission of Senology, composed of 26 experts, developed these recommendations. A policy of declaration and monitoring of links of interest was applied throughout the process of making the recommendations. Similarly, the development of these recommendations did not benefit from any funding from a company marketing a health product. The Commission of Senology adhered to the AGREE II (Advancing guideline development, reporting and evaluation in healthcare) criteria and followed the Grading of Recommendations Assessment, Development and Evaluation (GRADE) method to assess the quality of the evidence on which the recommendations were based. The potential drawbacks of making recommendations in the presence of poor quality or insufficient evidence were highlighted. METHODS The Commission of Senology considered 8 questions on 4 topics, focusing on histological, familial (no identified genetic abnormality), radiological (of unrecognized cancer), and radiation (history of Hodgkin's disease) risk. For each situation, it was determined whether performing RRM compared with surveillance would decrease the risk of developing breast cancer and/or increase survival. RESULTS The Commission of Senology synthesis and application of the GRADE method resulted in 11 recommendations, 6 with a high level of evidence (GRADE 1±) and 5 with a low level of evidence (GRADE 2±). CONCLUSION There was significant agreement among the Commission of Senology members on recommendations to improve practice for performing or not performing RRM in the clinical setting.
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Affiliation(s)
- Carole Mathelin
- CHRU, avenue Molière, 67200 Strasbourg, France; ICANS, 17, rue Albert-Calmette, 67033 Strasbourg cedex, France.
| | | | | | - Gérard Boutet
- AGREGA, service de chirurgie gynécologique et médecine de la reproduction, centre Aliénor d'Aquitaine, centre hospitalier universitaire de Bordeaux, groupe hospitalier Pellegrin, place Amélie-Raba-Léon, 33000 Bordeaux, France.
| | - Susie Brousse
- CHU de Rennes, 2, rue Henri-le-Guilloux, 35033 Rennes cedex 9, France.
| | | | - Charles Coutant
- Département d'oncologie chirurgicale, centre Georges-François-Leclerc, 1, rue du Pr-Marion, 21079 Dijon cedex, France.
| | - Emile Daraï
- Hôpital Tenon, service de gynécologie-obstétrique, 4, rue de la Chine, 75020 Paris, France.
| | - Yann Delpech
- Centre Antoine-Lacassagne, 33, avenue de Valombrose, 06189 Nice, France.
| | - Martha Duraes
- CHU de Montpellier, 191, avenue du Doyen-Giraud, 34295 Montpellier cedex, France.
| | - Marc Espié
- Hôpital Saint-Louis, 1, avenue Claude-Vellefaux, 75010 Paris, France.
| | - Luc Fornecker
- Département d'onco-hématologie, ICANS, 17, rue Albert-Calmette, 67033 Strasbourg cedex, France.
| | - François Golfier
- Centre hospitalier Lyon Sud, bâtiment 3B, 165, chemin du Grand-Revoyet, 69495 Pierre-Bénite, France.
| | | | | | - Edith Kermarrec
- Hôpital Tenon, service de radiologie, 4, rue de la Chine, 75020 Paris, France.
| | - Vincent Lavoué
- CHU, service de gynécologie, 16, boulevard de Bulgarie, 35200 Rennes, France.
| | | | - Élisabeth Luporsi
- Oncologie médicale et oncogénétique, CHR Metz-Thionville, hôpital de Mercy, 1, allée du Château, 57085 Metz, France.
| | - Christine M Maugard
- Service de génétique oncologique clinique, unité de génétique oncologique moléculaire, hôpitaux universitaires de Strasbourg, 1, avenue Molière, 67200 Strasbourg, France.
| | | | | | - Nicolas Taris
- Oncogénétique, ICANS, 17, rue Albert-Calmette, 67033 Strasbourg, France.
| | - Catherine Uzan
- Hôpital Pitié-Salpetrière, 47, boulevard de l'Hôpital, 75013 Paris, France.
| | - Charlotte Vaysse
- Service de chirurgie oncologique, CHU Toulouse, institut universitaire du cancer de Toulouse-Oncopole, 1, avenue Irène-Joliot-Curie, 31059 Toulouse, France.
| | - Xavier Fritel
- Centre hospitalo-universitaire de Poitiers, 2, rue de la Milétrie, 86021 Poitiers, France.
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Wright JL, Rahbar H, Obeng-Gyasi S, Carlos R, Tjoe J, Wolff AC. Overcoming Barriers in Ductal Carcinoma In Situ Management: From Overtreatment to Optimal Treatment. J Clin Oncol 2022; 40:225-230. [PMID: 34813345 PMCID: PMC8760161 DOI: 10.1200/jco.21.01674] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 10/19/2021] [Accepted: 10/25/2021] [Indexed: 01/22/2023] Open
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Covington MF, Parent EE, Dibble EH, Rauch GM, Fowler AM. Advances and Future Directions in Molecular Breast Imaging. J Nucl Med 2022; 63:17-21. [PMID: 34887334 PMCID: PMC8717200 DOI: 10.2967/jnumed.121.261988] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 11/16/2021] [Indexed: 12/11/2022] Open
Abstract
Molecular breast imaging (MBI) using 99mTc-sestamibi has advanced rapidly over the past decade. Technical advances allow lower-dose, higher-resolution imaging and biopsy capability. MBI can be used for supplemental breast cancer screening with mammography for women with dense breasts, as well as to assess neoadjuvant therapy response, evaluate disease extent, and predict breast cancer risk. This article highlights the current state of the art and future directions in MBI.
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Affiliation(s)
- Matthew F Covington
- Center for Quantitative Cancer Imaging, Huntsman Cancer Institute and University of Utah Department of Radiology and Imaging Sciences, Salt Lake City, Utah;
| | | | - Elizabeth H Dibble
- Warren Alpert Medical School of Brown University/Rhode Island Hospital Department of Diagnostic Imaging, Providence, Rhode Island
| | - Gaiane M Rauch
- M.D. Anderson Cancer Center, Departments of Abdominal and Breast Imaging, Houston, Texas; and
| | - Amy M Fowler
- University of Wisconsin School of Medicine and Public Health, Departments of Radiology and Medical Physics and the University of Wisconsin Carbone Cancer Center, Madison, Wisconsin
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Meng L, Zhao X, Lu L, Xing Q, Wang K, Guo Y, Shang H, Chen Y, Huang M, Sun Y, Zhang X. A Comparative Assessment of MR BI-RADS 4 Breast Lesions With Kaiser Score and Apparent Diffusion Coefficient Value. Front Oncol 2021; 11:779642. [PMID: 34926290 PMCID: PMC8675081 DOI: 10.3389/fonc.2021.779642] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2021] [Accepted: 11/10/2021] [Indexed: 12/24/2022] Open
Abstract
Objectives To investigate the diagnostic performance of the Kaiser score and apparent diffusion coefficient (ADC) to differentiate Breast Imaging Reporting and Data System (BI-RADS) Category 4 lesions at dynamic contrast-enhanced (DCE) MRI. Methods This was a single-institution retrospective study of patients who underwent breast MRI from March 2020 to June 2021. All image data were acquired with a 3-T MRI system. Kaiser score of each lesion was assigned by an experienced breast radiologist. Kaiser score+ was determined by combining ADC and Kaiser score. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of Kaiser score+, Kaiser score, and ADC. The area under the curve (AUC) values were calculated and compared by using the Delong test. The differences in sensitivity and specificity between different indicators were determined by the McNemar test. Results The study involved 243 women (mean age, 43.1 years; age range, 18-67 years) with 268 MR BI-RADS 4 lesions. Overall diagnostic performance for Kaiser score (AUC, 0.902) was significantly higher than for ADC (AUC, 0.81; p = 0.004). There were no significant differences in AUCs between Kaiser score and Kaiser score+ (p = 0.134). The Kaiser score was superior to ADC in avoiding unnecessary biopsies (p < 0.001). Compared with the Kaiser score alone, the specificity of Kaiser score+ increased by 7.82%, however, at the price of a lower sensitivity. Conclusion For MR BI-RADS category 4 breast lesions, the Kaiser score was superior to ADC mapping regarding the potential to avoid unnecessary biopsies. However, the combination of both indicators did not significantly contribute to breast cancer diagnosis of this subgroup.
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Affiliation(s)
- Lingsong Meng
- Department of Radiology, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xin Zhao
- Department of Radiology, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Lin Lu
- Department of Radiology, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Qingna Xing
- Department of Radiology, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Kaiyu Wang
- Magnetic Resonance (MR) Research China, General Electric (GE) Healthcare, Beijing, China
| | - Yafei Guo
- Department of Radiology, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Honglei Shang
- Department of Radiology, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yan Chen
- Department of Radiology, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Mengyue Huang
- Department of Magnetic Resonance Imaging (MRI), The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yongbing Sun
- Department of Radiology, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xiaoan Zhang
- Department of Radiology, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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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: 16] [Impact Index Per Article: 5.3] [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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Dubey P, Thakur B, Rodriguez S, Cox J, Sanchez S, Fonseca A, Reddy S, Clegg D, Dwivedi AK. A systematic review and meta-analysis of the association between maternal polycystic ovary syndrome and neuropsychiatric disorders in children. Transl Psychiatry 2021; 11:569. [PMID: 34750348 PMCID: PMC8575994 DOI: 10.1038/s41398-021-01699-8] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/08/2021] [Revised: 10/16/2021] [Accepted: 10/21/2021] [Indexed: 11/23/2022] Open
Abstract
There is emerging evidence demonstrating an association between maternal polycystic ovary syndrome (PCOS) and autism spectrum disorder (ASD) in children, however, the cumulative effect of maternal PCOS on the development of ASD or other neuropsychiatry disorders (NPD) in children and separately for males and females has not been examined. We sought to systematically evaluate the influence of maternal PCOS on a wide range of NPD including ASD, attention deficit hyperactivity disorder (ADHD), chronic tic disorder (CDT), other behavior disorders, anxiety, depression, bipolar disorder, schizophrenia in children as well as in women of reproductive age only. We queried electronic databases including PubMed, EMBASE, and Google Scholar, until March 2021. We used DerSimonian and Laird (D-L) random effects method to compute pooled effect size in terms of odds ratio (OR). Nineteen studies (1667851 mothers, 2260622 children) were included in this study. Mothers with PCOS had an increased odds of children diagnosed with ASD (OR = 1.40, p < 0.001), ADHD (OR = 1.42, p < 0.001), CTD (OR = 1.44, p = 0.001), anxiety (OR = 1.33, p < 0.001), as well as other behavioral symptoms (OR = 1.45, p < 0.001) in the adjusted analysis. The association between maternal PCOS and ASD (OR: 1.43 vs. 1.66), ADHD (OR: 1.39 vs. 1.54), and CTD (OR: 1.42 vs. 1.51) was found to be significantly consistent between males and females, respectively. Our data do not suggest increased fetal testosterone exposure is associated with increased autistic traits in children. However, PCOS was significantly associated with increased odds of a wide range of NPD in women themselves. Maternal PCOS is a risk factor for various NPD with a similar extent in their children regardless of their underlying comorbidities. Managing PCOS is essential for women's health as well as for their children's health. More research is needed to determine the mechanisms and links between maternal PCOS and NPD in children.
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Affiliation(s)
- Pallavi Dubey
- grid.416992.10000 0001 2179 3554Department of Obstetrics and Gynecology, Paul L. Foster School of Medicine, Texas Tech University Health Sciences Center El Paso, El Paso, TX USA 79905
| | - Bhaskar Thakur
- grid.416992.10000 0001 2179 3554Division of Biostatistics and Epidemiology, Department of Molecular and Translational Medicine, Paul L. Foster School of Medicine, Texas Tech University Health Sciences Center El Paso, El Paso, TX USA 79905
| | - Sheryl Rodriguez
- grid.416992.10000 0001 2179 3554Graduate School of Biomedical Sciences, Texas Tech University Health Sciences Center El Paso, El Paso, TX USA 79905
| | - Jessika Cox
- grid.416992.10000 0001 2179 3554Graduate School of Biomedical Sciences, Texas Tech University Health Sciences Center El Paso, El Paso, TX USA 79905
| | - Sheralyn Sanchez
- grid.416992.10000 0001 2179 3554Department of Obstetrics and Gynecology, Paul L. Foster School of Medicine, Texas Tech University Health Sciences Center El Paso, El Paso, TX USA 79905
| | - Anacani Fonseca
- grid.416992.10000 0001 2179 3554Department of Pediatrics, Paul L. Foster School of Medicine, Texas Tech University Health Sciences Center El Paso, El Paso, TX USA 79905
| | - Sireesha Reddy
- grid.416992.10000 0001 2179 3554Department of Obstetrics and Gynecology, Paul L. Foster School of Medicine, Texas Tech University Health Sciences Center El Paso, El Paso, TX USA 79905
| | - Deborah Clegg
- grid.416992.10000 0001 2179 3554Office of Research, Texas Tech University Health Sciences Center El Paso, El Paso, TX USA 79905 ,grid.416992.10000 0001 2179 3554Department of Internal Medicine, Paul L. Foster School of Medicine, Texas Tech University Health Sciences Center El Paso, El Paso, TX USA 79905
| | - Alok Kumar Dwivedi
- Division of Biostatistics and Epidemiology, Department of Molecular and Translational Medicine, Paul L. Foster School of Medicine, Texas Tech University Health Sciences Center El Paso, El Paso, TX, USA, 79905. .,Graduate School of Biomedical Sciences, Texas Tech University Health Sciences Center El Paso, El Paso, TX, USA, 79905. .,Office of Research, Texas Tech University Health Sciences Center El Paso, El Paso, TX, USA, 79905.
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Liang T, Cong S, Yi Z, Liu J, Huang C, Shen J, Pei S, Chen G, Liu Z. Ultrasound-Based Nomogram for Distinguishing Malignant Tumors from Nodular Sclerosing Adenoses in Solid Breast Lesions. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2021; 40:2189-2200. [PMID: 33438775 DOI: 10.1002/jum.15612] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2020] [Revised: 12/01/2020] [Accepted: 12/14/2020] [Indexed: 06/12/2023]
Abstract
OBJECTIVES Nodular sclerosing adenoses (NSAs) and malignant tumors (MTs) may coexist and are often classified into the same Breast Imaging Reporting and Data System (BI-RADS) category. We aimed to build and validate an ultrasound-based nomogram to distinguish MT from NSA for building a precise sequence of biopsies. MATERIALS AND METHODS The training cohort included 156 patients (156 masses) with NSA or MT at one study institution. We used best subset regression to determine the predictors for building a nomogram from ultrasonic characteristics and patients' age. Model performance and clinical utility were evaluated using Brier score, concordance (C)-index, calibration curve, and decision curve analysis. The independent validation cohort consisted of 162 patients (162 masses) from a separate institution. RESULTS Through best subset regression, we selected 6 predictors to develop nomogram: age, calcification, echogenic rim, vascularity distribution, tumor size, and thickness of breast parenchyma. Brier score and C-index of the nomogram in the training cohort were 0.068 and 0.967 (95% confidence interval [CI]: 0.941-0.993), respectively. In addition, calibration curve demonstrated good agreement between prediction and pathological result. In the validation cohort, the nomogram still obtained a favorable C-index score of 0.951 (95% CI: 0.919-0.983) and fine calibration. Decision curve analysis showed that the model was clinically useful. CONCLUSIONS If multiple NSA and MT masses are present in the same patient and are classified into the same BI-RADS category, our nomogram can be used as a supplement to the BI-RADS category for accurate biopsy of the mass most likely to be MT.
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Affiliation(s)
- Ting Liang
- The Second School of Clinical Medicine, Southern Medical University, No.253 Gongye Middle Avenue, Guangzhou, Guangdong, People's Republic of China
- Department of Ultrasound, Affiliated Hospital of Guangdong Medical University, No.57 People's Avenue South, Zhanjiang, Guangdong, People's Republic of China
| | - Shuzhen Cong
- Department of Ultrasound, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, No.106 Zhongshan Er Road, Guangzhou, Guangdong, 510080, People's Republic of China
| | - Zongjian Yi
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, No.106 Zhongshan Er Road, Guangzhou, Guangdong, 510080, People's Republic of China
| | - Juanjuan Liu
- Department of Ultrasound, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, No.106 Zhongshan Er Road, Guangzhou, Guangdong, 510080, People's Republic of China
| | - Chunwang Huang
- Department of Ultrasound, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, No.106 Zhongshan Er Road, Guangzhou, Guangdong, 510080, People's Republic of China
| | - Junhui Shen
- Department of Anesthesiology, Affiliated Hospital of Guangdong Medical University, No.57 People's Avenue South, Zhanjiang, Guangdong, People's Republic of China
| | - Shufang Pei
- Department of Ultrasound, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, No.106 Zhongshan Er Road, Guangzhou, Guangdong, 510080, People's Republic of China
| | - Gaowen Chen
- The Second School of Clinical Medicine, Southern Medical University, No.253 Gongye Middle Avenue, Guangzhou, Guangdong, People's Republic of China
| | - Zaiyi Liu
- The Second School of Clinical Medicine, Southern Medical University, No.253 Gongye Middle Avenue, Guangzhou, Guangdong, People's Republic of China
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, No.106 Zhongshan Er Road, Guangzhou, Guangdong, 510080, People's Republic of China
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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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You C, Zhang Y, Chen Y, Hu X, Hu D, Wu J, Gu Y, Peng W. Evaluation of Background Parenchymal Enhancement and Histogram-Based Diffusion-Weighted Image in Determining the Molecular Subtype of Breast Cancer. J Comput Assist Tomogr 2021; 45:711-716. [PMID: 34546678 DOI: 10.1097/rct.0000000000001239] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
RATIONALE AND OBJECTIVES This study aimed to evaluate the value of background parenchymal enhancement (BPE) and diffusion-weighted image (DWI) histogram features in differentiating among different molecular subtypes of breast cancers and investigate the relationship between BPE and DWI features. MATERIALS AND METHODS We prospectively enrolled 142 patients with breast cancer between January and November 2018. All patients underwent breast magnetic resonance imaging before core needle biopsy. The quantitative BPE from dynamic enhanced images and the first-order histogram features extracted from DWI were analyzed. Univariate analysis of variance was used to compare differences in DWI histogram features and BPE characteristics among different molecular subtypes. Spearman test was used to compare the correlation between these imaging indexes. RESULTS A total of 142 patients had 142 lesions, including 17 cases of triple-negative breast cancer, 12 cases of luminal A type breast cancer, 39 cases of luminal B type breast cancer, and 74 cases of human epidermal growth factor receptor 2-positive breast cancer. The apparent diffusion coefficient (ADC) 95th percentile, ADC kurtosis, and BPE were significantly different among 4 subtype groups (P < 0.05), especially between the triple-negative subtype and any other subtype (P < 0.05 in pairwise comparisons). There was a weak but significant correlation between BPE and kurtosis of ADC (r = -0.176, P = 0.036). CONCLUSIONS Diffusion-weighted image histogram features (95th percentile ADC value and kurtosis value of ADC) and BPE features were different in the 4 molecular subtypes of breast cancer, especially in the triple-negative breast cancer subtype. Background parenchymal enhancement was negatively correlated with the kurtosis value of ADC.
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Affiliation(s)
- Chao You
- From the Department of Radiology, Fudan University Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University
| | - Yunyan Zhang
- Department of Radiology, Shanghai Proton and Heavy Ion Center
| | - Yanqiong Chen
- From the Department of Radiology, Fudan University Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University
| | - Xiaoxin Hu
- From the Department of Radiology, Fudan University Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University
| | - Danting Hu
- From the Department of Radiology, Fudan University Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University
| | - Jiong Wu
- Department of Breast Surgery, Fudan University Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, PR China
| | - Yajia Gu
- From the Department of Radiology, Fudan University Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University
| | - Weijun Peng
- From the Department of Radiology, Fudan University Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University
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Vong S, Ronco AJ, Najafpour E, Aminololama-Shakeri S. Screening Breast MRI and the Science of Premenopausal Background Parenchymal Enhancement. JOURNAL OF BREAST IMAGING 2021; 3:407-415. [PMID: 38424792 DOI: 10.1093/jbi/wbab045] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Indexed: 03/02/2024]
Abstract
The significance of background parenchymal enhancement (BPE) on screening and diagnostic breast MRI continues to be elucidated. Background parenchymal enhancement was initially deemed probably benign and followed or thought of as an artifact degrading the accuracy of breast cancer detection on breast MRI examinations. Subsequent research has focused on understanding the role of BPE regarding screening breast MRI. Today, there is growing evidence that a myriad of factors affect BPE, which in turn may influence patient outcomes. Additionally, BPE could represent an important risk factor for the future development of breast cancer. This article aims to describe the most up-to-date research on BPE as it relates to screening breast MRI in premenopausal women.
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Affiliation(s)
- Stephen Vong
- University of California Davis, Department of Radiology, Sacramento, CA, USA
| | - Anthony J Ronco
- University of California Davis, Department of Radiology, Sacramento, CA, USA
| | - Elham Najafpour
- University of California Davis, Department of Radiology, Sacramento, CA, USA
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Quantitative Measures of Background Parenchymal Enhancement Predict Breast Cancer Risk. AJR Am J Roentgenol 2021; 217:64-75. [PMID: 32876474 PMCID: PMC9801515 DOI: 10.2214/ajr.20.23804] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
BACKGROUND. Higher categories of background parenchymal enhancement (BPE) increase breast cancer risk. However, current clinical BPE categorization is subjective. OBJECTIVE. Using a semiautomated segmentation algorithm, we calculated quantitative BPE measures and investigated the utility of individual features and feature pairs in significantly predicting subsequent breast cancer risk compared with radiologist-assigned BPE category. METHODS. In this retrospective case-control study, we identified 95 women at high risk of breast cancer but without a personal history of breast cancer who underwent breast MRI. Of these women, 19 subsequently developed breast cancer and were included as cases. Each case was age matched to four control patients (76 control patients total). Sociodemographic characteristics were compared between the cases and matched control patients using the Mann-Whitney U test. From each dynamic contrast-enhanced MRI examination, quantitative fibroglandular tissue and BPE measures were computed by averaging enhancing voxels above enhancement ratio thresholds (0-100%), totaling the enhancing volume above thresholds (BPE volume in cm3), and estimating the percentage of enhancing tissue above thresholds relative to total breast volume (BPE%) on each gadolinium-enhanced phase. For the 91 imaging features generated, we compared predictive performance using conditional logistic regression with 80:20 hold-out cross validation and ROC curve analysis. ROC AUC was the figure of merit. Sensitivity, specificity, PPV, and NPV were also computed. All feature pairs were exhaustively searched to identify those with the highest AUC and Youden index. A DeLong test was used to compare predictive performance (AUCs). RESULTS. Women subsequently diagnosed with breast cancer were more likely to have mild, moderate, or marked BPE (odds ratio, 3.0; 95% CI, 0.9-10.0; p = .07). According to ROC curve analysis, a BPE category threshold greater than minimal resulted in a maximized AUC (0.62) in distinguishing cases from control patients. Compared with BPE category, the first gadolinium-enhanced (phase 1) BPE% at the 30% and 40% enhancement ratio thresholds yielded significantly higher AUC values of 0.85 (p = .0007) and 0.84 (p = .0004), respectively. Feature combinations showed similar AUC values with improved sensitivity. CONCLUSION. Preliminary data indicate that quantitative BPE measures may outperform radiologist-assigned category in breast cancer risk prediction. CLINICAL IMPACT. Future risk prediction models that incorporate quantitative measures warrant additional investigation.
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Dietzel M, Krug B, Clauser P, Burke C, Hellmich M, Maintz D, Uder M, Bickel H, Helbich T, Baltzer PAT. A Multicentric Comparison of Apparent Diffusion Coefficient Mapping and the Kaiser Score in the Assessment of Breast Lesions. Invest Radiol 2021; 56:274-282. [PMID: 33122603 DOI: 10.1097/rli.0000000000000739] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
MATERIALS AND METHODS In this multicentric study, individual patient data from 3 different centers were analyzed. Consecutive patients receiving standardized multiparametric breast magnetic resonance imaging for standard nonscreening indications were included. At each center, 2 experienced radiologists with more than 5 years of experience retrospectively interpreted the examinations in consensus and applied the KS to every histologically verified lesion. The corresponding mean ADC of each lesion was measured using a Wielema type 4 region of interest. According to established methods, the KS and ADC were combined, yielding the KS+ score. Diagnostic accuracy was evaluated by the area under the receiver operating characteristics curve (AUROC) and compared between the KS, ADC, and KS+ (DeLong test). Likewise, the potential to help avoid unnecessary biopsies was compared between the KS, ADC, and KS+ based on established high sensitivity thresholds (McNemar test). RESULTS A total of 450 lesions in 414 patients (mean age, 51.5 years; interquartile range, 42-60.8 years) were included, with 219 lesions being malignant (48.7%; 95% confidence interval [CI], 44%-53.4%). The performance of the KS (AUROC, 0.915; CI, 0.886-0.939) was significantly better than that of the ADC (AUROC, 0.848; CI, 0.811-0.880; P < 0.001). The largest difference between these parameters was observed when assessing subcentimeter lesions (AUROC, 0.909 for KS; CI, 0.849-0.950 vs 0.811 for ADC; CI, 0.737-0.871; P = 0.02).The use of the KS+ (AUROC, 0.918; CI, 0.889-0.942) improved the performance slightly, but without any significant difference relative to a single KS or ADC reading (P = 0.64).When applying high sensitivity thresholds for avoiding unnecessary biopsies, the KS and ADC achieved equal sensitivity (97.7% for both; cutoff values, >4 for KS and ≤1.4 × 10-3 mm2/s for ADC). However, the rate of potentially avoidable biopsies was higher when using the KS (specificity: 65.4% for KS vs 32.9% for ADC; P < 0.0001). The KS was superior to the KS+ in avoiding unnecessary biopsies. CONCLUSIONS Both the KS and ADC may be used to distinguish benign from malignant breast lesions. However, KS proved superior in this task including, most of all, when assessing small lesions less than 1 cm. Using the KS may avoid twice as many unnecessary biopsies, and the combination of both the KS and ADS does not improve diagnostic performance.
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Affiliation(s)
- Matthias Dietzel
- From the Department of Radiology, University Hospital Erlangen, Erlangen, Germany
| | - Barbara Krug
- Department of Diagnostic and Interventional Radiology, University Hospital Cologne
| | - Paola Clauser
- Division of Molecular and Gender Imaging, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Christina Burke
- Department of Diagnostic and Interventional Radiology, University Hospital Cologne
| | - Martin Hellmich
- Institute of Medical Statistics and Bioinformatics, University Cologne, Cologne, Germany
| | - David Maintz
- Department of Diagnostic and Interventional Radiology, University Hospital Cologne
| | - Michael Uder
- From the Department of Radiology, University Hospital Erlangen, Erlangen, Germany
| | - Hubert Bickel
- Division of Molecular and Gender Imaging, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Thomas Helbich
- Division of Molecular and Gender Imaging, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Pascal A T Baltzer
- Division of Molecular and Gender Imaging, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
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Hesse LS, Kuling G, Veta M, Martel AL. Intensity Augmentation to Improve Generalizability of Breast Segmentation Across Different MRI Scan Protocols. IEEE Trans Biomed Eng 2021; 68:759-770. [DOI: 10.1109/tbme.2020.3016602] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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Rella R, Contegiacomo A, Bufi E, Mercogliano S, Belli P, Manfredi R. Background parenchymal enhancement and breast cancer: a review of the emerging evidences about its potential use as imaging biomarker. Br J Radiol 2021; 94:20200630. [PMID: 33035073 DOI: 10.1259/bjr.20200630] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
OBJECTIVES To conduct a systematic review of evidences about the relationship between background parenchymal enhancement (BPE) of the contralateral healthy breast and breast cancer: its association with clinicopathological breast cancer characteristics, its potential as predictive and prognostic biomarker and the biological linkage between BPE and breast cancer. METHODS A computerized literature search using PubMed and Google Scholar was performed up to June 2020. Two authors independently conducted search, screening, quality assessment, and extraction of data from the eligible studies. Studies were assessed for quality and risk of bias using the revised Quality Assessment of Diagnostic Accuracy Studies tool. RESULTS Of the 476 articles identified, 22 articles met the inclusion criteria. No significant association was found between BPE and invasiveness, histological cancer type, T- and N-stage, multifocality, lymphatic and vascular invasion and histological tumour grade while the association between BPE and molecular subtypes is still unclear. As predictive biomarker, a greater decrease in BPE during and after neoadjuvant chemotherapy was associated with pathological complete response. Results about the role of BPE as prognostic factor were inconsistent. An association between high BPE and microvessel density, CD34 and VEGF (histological markers of vascularization and angiogenesis) was found. CONCLUSIONS BPE of the contralateral breast is associated with breast cancer in several aspects, therefore it has been proposed as a tool to refine breast cancer decision-making process. ADVANCES IN KNOWLEDGE Additional researches with standardized BPE assessment are needed to translate this emerging biomarker into clinical practice in the era of personalized medicine.
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Affiliation(s)
- Rossella Rella
- UOC di Diagnostica per immagini ed Interventistica Generale, Dipartimento di diagnostica per immagini, radioterapia oncologica ed ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Largo A. Gemelli 8, 00168 Roma, Italia
| | - Andrea Contegiacomo
- UOC di Diagnostica per immagini ed Interventistica Generale, Dipartimento di diagnostica per immagini, radioterapia oncologica ed ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Largo A. Gemelli 8, 00168 Roma, Italia
| | - Enida Bufi
- UOC di Diagnostica per immagini ed Interventistica Generale, Dipartimento di diagnostica per immagini, radioterapia oncologica ed ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Largo A. Gemelli 8, 00168 Roma, Italia
| | - Sara Mercogliano
- Università Cattolica Sacro Cuore, Largo F. Vito 1, 00168 Roma, Italia
| | - Paolo Belli
- UOC di Diagnostica per immagini ed Interventistica Generale, Dipartimento di diagnostica per immagini, radioterapia oncologica ed ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Largo A. Gemelli 8, 00168 Roma, Italia.,Università Cattolica Sacro Cuore, Largo F. Vito 1, 00168 Roma, Italia
| | - Riccardo Manfredi
- UOC di Diagnostica per immagini ed Interventistica Generale, Dipartimento di diagnostica per immagini, radioterapia oncologica ed ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Largo A. Gemelli 8, 00168 Roma, Italia.,Università Cattolica Sacro Cuore, Largo F. Vito 1, 00168 Roma, Italia
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Yu L, Wang Y, Xing D, Gong P, Chen Q, Lv Y. Background parenchymal enhancement on contrast-enhanced spectral mammography does not represent an influencing factor for breast cancer: A preliminary study. Medicine (Baltimore) 2020; 99:e23857. [PMID: 33350778 PMCID: PMC7769306 DOI: 10.1097/md.0000000000023857] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Accepted: 11/05/2020] [Indexed: 11/26/2022] Open
Abstract
To compare the relationship between background parenchymal enhancement (BPE) on contrast-enhanced spectral mammography (CESM), mammographic breast density (MBD), age, in the group with benign vs malignant breast lesions.Four hundred thirty three non-high-risk patients from January 2018 to May 2019 were retrospectively analyzed. Patients were assigned into 4 groups: premenopausal benign lesions, premenopausal malignant lesions, postmenopausal benign lesions, and postmenopausal malignant lesions. The differences in CESM BPE and MBD between premenopausal benign lesions and premenopausal malignant lesions, between postmenopausal benign lesions and postmenopausal malignant lesions, between premenopausal and postmenopausal benign lesions, and between premenopausal and postmenopausal malignant lesions were evaluated. Pearson Chi-Squared test was used to analyze the differences between the above groups. Spearman rank correlation analysis was used to evaluate the correlations between BPE, MBD, and age. Multiple logistic regression was used to analyze the influencing factors of breast cancer. P < .05 was considered statistically significant.There was no significant difference in CESM BPE or MBD of benign and malignant lesions regardless of premenopausal or postmenopausal status, but there was a significant difference in CESM BPE and MBD of premenopausal and postmenopausal patients regardless of the presence of benign or malignant lesions. The intensity of CESM BPE was positively correlated with MBD, and the intensity of CESM BPE and MBD were negatively correlated with age. Multiple logistic regression analysis showed that age was an influencing factor for breast cancer in both premenopausal and postmenopausal patients.For non-high-risk women, CESM BPE and MBD were not correlated with benign or malignant breast lesions, and age was an influencing factor for breast cancer.
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Affiliation(s)
| | | | - Dong Xing
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong
| | - Peiyou Gong
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong
| | - Qianqian Chen
- GE Healthcare, Institute of Precision Medicine, Shanghai, PR China
| | - Yongbin Lv
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong
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Mann RM, Pinker K. Is Background Parenchymal Enhancement an Important Risk Factor for Breast Cancer Development in Women with Increased Risk? Radiology 2019; 292:562-563. [PMID: 31237810 DOI: 10.1148/radiol.2019191164] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Affiliation(s)
- Ritse M Mann
- From the Department of Radiology and Nuclear Medicine, Radboud University Medical Centre, Nijmegen, the Netherlands (R.M.M.); Department of Radiology, Netherlands Cancer Institute, Amsterdam, the Netherlands (R.M.M.); Department of Radiology, Breast Imaging Service, Memorial Sloan-Kettering Cancer Center, New York, NY (K.P.); and Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, Vienna, Austria (K.P.)
| | - Katja Pinker
- From the Department of Radiology and Nuclear Medicine, Radboud University Medical Centre, Nijmegen, the Netherlands (R.M.M.); Department of Radiology, Netherlands Cancer Institute, Amsterdam, the Netherlands (R.M.M.); Department of Radiology, Breast Imaging Service, Memorial Sloan-Kettering Cancer Center, New York, NY (K.P.); and Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, Vienna, Austria (K.P.)
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