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Aribal E, Seker ME, Guldogan N, Yilmaz E. Value of automated breast ultrasound in screening: Standalone and as a supplemental to digital breast tomosynthesis. Int J Cancer 2024; 155:1466-1475. [PMID: 38989802 DOI: 10.1002/ijc.35093] [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: 02/19/2024] [Revised: 06/22/2024] [Accepted: 06/27/2024] [Indexed: 07/12/2024]
Abstract
We aimed to determine the value of standalone and supplemental automated breast ultrasound (ABUS) in detecting cancers in an opportunistic screening setting with digital breast tomosynthesis (DBT) and compare this combined screening method to DBT and ABUS alone in women older than 39 years with BI-RADS B-D density categories. In this prospective opportunistic screening study, 3466 women aged 39 or older with BI-RADS B-D density categories and with a mean age of 50 were included. The screening protocol consisted of DBT mediolateral-oblique views, 2D craniocaudal views, and ABUS with three projections for both breasts. ABUS was evaluated blinded to mammography findings. Statistical analysis evaluated diagnostic performance for DBT, ABUS, and combined workflows. Twenty-nine cancers were screen-detected. ABUS and DBT exhibited the same cancer detection rates (CDR) at 7.5/1000 whereas DBT + ABUS showed 8.4/1000, with ABUS contributing an additional CDR of 0.9/1000. Standalone ABUS outperformed DBT in detecting 12.5% more invasive cancers. DBT displayed better accuracy (95%) compared to ABUS (88%) and combined approach (86%). Sensitivities for DBT and ABUS were the same (84%), with DBT + ABUS showing a higher rate (94%). DBT outperformed ABUS in specificity (95% vs. 88%). DBT + ABUS exhibited a higher recall rate (14.89%) compared to ABUS (12.38%) and DBT (6.03%) (p < .001). Standalone ABUS detected more invasive cancers compared to DBT, with a higher recall rate. The combined approach showed a higher CDR by detecting one additional cancer per thousand.
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Affiliation(s)
- Erkin Aribal
- School of Medicine, Department of Radiology, Acibadem Mehmet Ali Aydinlar University, Istanbul, Turkey
- Department of Radiology, Acibadem Altunizade Hospital, Istanbul, Turkey
| | - Mustafa Ege Seker
- School of Medicine, Department of Radiology, Acibadem Mehmet Ali Aydinlar University, Istanbul, Turkey
| | - Nilgün Guldogan
- Department of Radiology, Acibadem Altunizade Hospital, Istanbul, Turkey
| | - Ebru Yilmaz
- Department of Radiology, Acibadem Altunizade Hospital, Istanbul, Turkey
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2
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Juliano M, Samreen N, Chacko C, Heller SL. Clinical role of abbreviated and ultrafast MRI in breast imaging. Br J Radiol 2024; 97:1511-1516. [PMID: 38676660 PMCID: PMC11332674 DOI: 10.1093/bjr/tqae079] [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: 03/06/2023] [Revised: 03/04/2024] [Accepted: 04/11/2024] [Indexed: 04/29/2024] Open
Abstract
Current breast cancer screening relies on mammography, digital breast tomosynthesis and breast ultrasound. In select populations, breast MRI is also of great utility. However, multiple factors limit widespread use of breast MRI for screening. Efforts have been made to increase the availability of breast MRI for screening, in large part due to the increased cancer detection rate of breast MRI compared to mammography. Techniques include shortening standard breast MRI protocols with the potential for accommodating MRI screening in a higher number of patients. This review will explain the role of abbreviated breast MRI and ultrafast breast MRI in breast imaging, and detail how these approaches differ from standard dynamic contrast-enhanced breast MRI. In addition, limitations and advantages of these techniques will also be discussed.
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Affiliation(s)
- Mario Juliano
- UW Health Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, 53792, United States
| | - Naziya Samreen
- Department of Radiology, Confluence Health, Wenatchee, WA, 98802, United States
| | - Celin Chacko
- Department of Radiology, Columbia University, New York, NY, 10032, United States
| | - Samantha L Heller
- NYU Langone Department of Radiology, New York University, New York, NY, 10012, United States
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3
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Stout NK, Miglioretti DL, Su YR, Lee CI, Abraham L, Alagoz O, de Koning HJ, Hampton JM, Henderson L, Lowry KP, Mandelblatt JS, Onega T, Schechter CB, Sprague BL, Stein S, Trentham-Dietz A, van Ravesteyn NT, Wernli KJ, Kerlikowske K, Tosteson ANA. Breast Cancer Screening Using Mammography, Digital Breast Tomosynthesis, and Magnetic Resonance Imaging by Breast Density. JAMA Intern Med 2024:2822381. [PMID: 39186304 PMCID: PMC11348087 DOI: 10.1001/jamainternmed.2024.4224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Accepted: 07/01/2024] [Indexed: 08/27/2024]
Abstract
Importance Information on long-term benefits and harms of screening with digital breast tomosynthesis (DBT) with or without supplemental breast magnetic resonance imaging (MRI) is needed for clinical and policy discussions, particularly for patients with dense breasts. Objective To project long-term population-based outcomes for breast cancer mammography screening strategies (DBT or digital mammography) with or without supplemental MRI by breast density. Design, Setting, and Participants Collaborative modeling using 3 Cancer Intervention and Surveillance Modeling Network (CISNET) breast cancer simulation models informed by US Breast Cancer Surveillance Consortium data. Simulated women born in 1980 with average breast cancer risk were included. Modeling analyses were conducted from January 2020 to December 2023. Intervention Annual or biennial mammography screening with or without supplemental MRI by breast density starting at ages 40, 45, or 50 years through age 74 years. Main outcomes and Measures Lifetime breast cancer deaths averted, false-positive recall and false-positive biopsy recommendations per 1000 simulated women followed-up from age 40 years to death summarized as means and ranges across models. Results Biennial DBT screening for all simulated women started at age 50 vs 40 years averted 7.4 vs 8.5 breast cancer deaths, respectively, and led to 884 vs 1392 false-positive recalls and 151 vs 221 false-positive biopsy recommendations, respectively. Biennial digital mammography had similar deaths averted and slightly more false-positive test results than DBT screening. Adding MRI for women with extremely dense breasts to biennial DBT screening for women aged 50 to 74 years increased deaths averted (7.6 vs 7.4), false-positive recalls (919 vs 884), and false-positive biopsy recommendations (180 vs 151). Extending supplemental MRI to women with heterogeneously or extremely dense breasts further increased deaths averted (8.0 vs 7.4), false-positive recalls (1088 vs 884), and false-positive biopsy recommendations (343 vs 151). The same strategy for women aged 40 to 74 years averted 9.5 deaths but led to 1850 false-positive recalls and 628 false-positive biopsy recommendations. Annual screening modestly increased estimated deaths averted but markedly increased estimated false-positive results. Conclusions and relevance In this model-based comparative effectiveness analysis, supplemental MRI for women with dense breasts added to DBT screening led to greater benefits and increased harms. The balance of this trade-off for supplemental MRI use was more favorable when MRI was targeted to women with extremely dense breasts who comprise approximately 10% of the population.
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Affiliation(s)
- Natasha K. Stout
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts
| | - Diana L. Miglioretti
- Department of Public Health Sciences, University of California Davis School of Medicine, Davis
| | - Yu-Ru Su
- Kaiser Permanente Washington Health Research Institute, Seattle
| | - Christoph I. Lee
- Fred Hutchinson Cancer Center, University of Washington School of Medicine, Seattle
| | - Linn Abraham
- Kaiser Permanente Washington Health Research Institute, Seattle
| | - Oguzhan Alagoz
- Department of Industrial and Systems Engineering and Department of Population Health Sciences and Carbone Cancer Center, University of Wisconsin–Madison, Madison
| | - Harry J. de Koning
- Department of Public Health, Erasmus University Medical Center Rotterdam, the Netherlands
| | - John M. Hampton
- Department of Industrial and Systems Engineering and Department of Population Health Sciences and Carbone Cancer Center, University of Wisconsin–Madison, Madison
| | - Louise Henderson
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill
| | - Kathryn P. Lowry
- Fred Hutchinson Cancer Center University of Washington School of Medicine, Seattle
| | - Jeanne S. Mandelblatt
- Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Department of Oncology and Georgetown Lombardi Institute for Cancer and Aging REsearch (I-CARE), Georgetown University, Washington, DC
| | - Tracy Onega
- Department of Population Health Sciences, and the Huntsman Cancer Institute, University of Utah, Salt Lake City
| | - Clyde B. Schechter
- Department of Family and Social Medicine, Albert Einstein College of Medicine, Bronx, New York
| | - Brian L. Sprague
- Department of Surgery, University of Vermont Cancer Center, Burlington, Vermont
- University of Vermont Larner College of Medicine, Burlington
- Department of Radiology, University of Vermont Cancer Center, Burlington, Vermont
| | - Sarah Stein
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts
| | - Amy Trentham-Dietz
- Department of Population Health Sciences and Carbone Cancer Center, University of Wisconsin–Madison, Madison
| | | | - Karen J. Wernli
- Kaiser Permanente Washington Health Research Institute, Seattle
| | - Karla Kerlikowske
- Departments of Medicine and Epidemiology and Biostatistics, University of California, San Francisco, San Francisco
| | - Anna N. A. Tosteson
- The Dartmouth Institute for Health Policy and Clinical Practice, Departments of Medicine and of Community and Family Medicine, and Dartmouth Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire
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Goren Z, Zioni T, Lev D, Cohen Y, Perry ZH. The impact of preoperative breast MRI on the therapeutic management of breast cancer patients. Surg Oncol 2024; 55:102095. [PMID: 38986313 DOI: 10.1016/j.suronc.2024.102095] [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: 10/22/2023] [Revised: 03/09/2024] [Accepted: 06/28/2024] [Indexed: 07/12/2024]
Abstract
BACKGROUND The role of preoperative breast MRI to evaluate the extent of disease in breast cancer patients is considered controversial. We aimed at assessing the effect of breast MRI on the management of newly diagnosed breast cancer. MATERIALS A retrospective review of 202 consecutively seen patients who were newly diagnosed with breast cancer and who underwent preoperative breast MRIs at Assuta Ashdod between June 1, 2017, and June 1, 2020. Data included discovering suspicious lesions by conventional imaging, MRI findings, and surgical pathology results. This was analyzed to determine whether the MRI changed the management and whether it had a justified or unjustified effect on the treatment. RESULTS The mean age was 54.51 (standard deviation, 11.34 years). Breast MRI revealed additional findings in 56 % of patients and modified therapeutic management in 32 % of the cases evaluated, having a justified effect in 87.6 %. Patients with changed management had a statistically significantly higher mastectomy rate (36 %) than those who did not (14 %). No statistically significant association was found between independent variables such as breast density, tumor location on the breast, type of tumor, patient's demographic information, etc. And whether MRI findings changed the initial treatment plan. CONCLUSIONS MRI played an essential role in the preoperative staging of breast cancer in our study, modifying therapeutic planning in approximately one-third of the cases and having a justified effect on most of them. We, therefore, support preoperative breast MRI in newly diagnosed breast cancer patients.
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Affiliation(s)
- Zohar Goren
- Goldman Medical School, The Faculty of Health Sciences, Ben-Gurion University, Israel
| | - Tammy Zioni
- The Breast Center, Assuta Ashdod Medical Center, Israel
| | - Dina Lev
- The Breast Center, Assuta Ashdod Medical Center, Israel
| | - Yaron Cohen
- Goldman Medical School, The Faculty of Health Sciences, Ben-Gurion University, Israel
| | - Zvi Howard Perry
- Goldman Medical School, The Faculty of Health Sciences, Ben-Gurion University, Israel; Surgery A, Soroka University Medical Center, Beer-Sheva, Israel.
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Liu Y, Jia X, Zhao J, Peng Y, Yao X, Hu X, Cui J, Chen H, Chen X, Wu J, Hong N, Wang S, Wang Y. A Machine Learning-Based Unenhanced Radiomics Approach to Distinguishing Between Benign and Malignant Breast Lesions Using T2-Weighted and Diffusion-Weighted MRI. J Magn Reson Imaging 2024; 60:600-612. [PMID: 37933890 DOI: 10.1002/jmri.29111] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 10/17/2023] [Accepted: 10/17/2023] [Indexed: 11/08/2023] Open
Abstract
BACKGROUND Breast MRI has been recommended as supplemental screening tool to mammography and breast ultrasound of breast cancer by international guidelines, but its long examination time and use of contrast material remains concerning. PURPOSE To develop an unenhanced radiomics model with using non-gadolinium based sequences for detecting breast cancer based on T2-weighted (T2W) and diffusion-weighted (DW) MRI. STUDY TYPE Retrospective analysis followed by retrospective and prospective cohorts study. POPULATION 1760 patients: Of these, 1293 for model construction (n = 775 for training and 518 for validation). The remaining patients for model testing in internal retrospective (n = 167), internal prospective (n = 188), and external retrospective (n = 112) cohorts. FIELD STRENGTH/SEQUENCE 3.0T MR scanners from two institution. T2WI, DWI, and first contrast-enhanced T1-weighted sequence. ASSESSMENT AUCs in distinguishing breast cancer were compared between combined model with gadolinium agent sequence and unenhanced model. Subsequently, the AUCs in testing cohorts of unenhanced model was compared with two radiologists' diagnosis for this research. Finally, patient subgroup analysis in testing cohorts was performed based on clinical subgroups and different types of malignancies. STATISTICAL TESTS Mann-Whitney U test, Kruskal-Wallis H test, chi-square test, weighted kappa test, and DeLong's test. RESULTS The unenhanced radiomics model performed best under Gaussian process (GP) classifiers (AUC: training, 0.893; validation, 0.848) compared to support vector machine (SVM) and logistic, showing favorable prediction in testing cohorts (AUCs, 0.818-0.840). The AUCs for the unenhanced radiomics model were not statistically different in five cohorts from those of the combined radiomics model (P, 0.317-0.816), as well as the two radiologists (P, 0.181-0.918). The unenhanced radiomics model was least successful in identifying ductal carcinoma in situ, whereas did not show statistical significance in other subgroups. DATA CONCLUSION An unenhanced radiomics model based on T2WI and DWI has comparable diagnostic accuracy to the combined model using the gadolinium agent. LEVEL OF EVIDENCE 4 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Yulu Liu
- Department of Radiology, Peking University People's Hospital, Beijing, China
| | - Xiaoxuan Jia
- Department of Radiology, Peking University People's Hospital, Beijing, China
| | - Jiaqi Zhao
- Department of Radiology, Jiangmen Central Hospital, Jiangmen, China
| | - Yuan Peng
- Department of Breast Surgery, Peking University People's Hospital, Beijing, China
| | - Xun Yao
- Department of Radiology, Peking University People's Hospital, Beijing, China
| | - Xuege Hu
- Department of Breast Surgery, Peking University People's Hospital, Beijing, China
| | - Jingjing Cui
- Department of Research and Development, United Imaging Intelligence (Beijing) Co., Ltd., Beijing, China
| | - Haoquan Chen
- Department of Radiology, Peking University People's Hospital, Beijing, China
| | - Xiufeng Chen
- Department of General Surgery, Beijing Aerospace General Hospital, Beijing, China
| | - Jing Wu
- Department of Radiology, Peking University People's Hospital, Beijing, China
| | - Nan Hong
- Department of Radiology, Peking University People's Hospital, Beijing, China
| | - Shu Wang
- Department of Breast Surgery, Peking University People's Hospital, Beijing, China
| | - Yi Wang
- Department of Radiology, Peking University People's Hospital, Beijing, China
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Okolie A, Dirrichs T, Huck LC, Nebelung S, Arasteh ST, Nolte T, Han T, Kuhl CK, Truhn D. Accelerating breast MRI acquisition with generative AI models. Eur Radiol 2024:10.1007/s00330-024-10853-x. [PMID: 39088043 DOI: 10.1007/s00330-024-10853-x] [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: 08/25/2023] [Revised: 04/27/2024] [Accepted: 06/03/2024] [Indexed: 08/02/2024]
Abstract
OBJECTIVES To investigate the use of the score-based diffusion model to accelerate breast MRI reconstruction. MATERIALS AND METHODS We trained a score-based model on 9549 MRI examinations of the female breast and employed it to reconstruct undersampled MRI images with undersampling factors of 2, 5, and 20. Images were evaluated by two experienced radiologists who rated the images based on their overall quality and diagnostic value on an independent test set of 100 additional MRI examinations. RESULTS The score-based model produces MRI images of high quality and diagnostic value. Both T1- and T2-weighted MRI images could be reconstructed to a high degree of accuracy. Two radiologists rated the images as almost indistinguishable from the original images (rating 4 or 5 on a scale of 5) in 100% (radiologist 1) and 99% (radiologist 2) of cases when the acceleration factor was 2. This fraction dropped to 88% and 70% for an acceleration factor of 5 and to 5% and 21% with an extreme acceleration factor of 20. CONCLUSION Score-based models can reconstruct MRI images at high fidelity, even at comparatively high acceleration factors, but further work on a larger scale of images is needed to ensure that diagnostic quality holds. CLINICAL RELEVANCE STATEMENT The number of MRI examinations of the breast is expected to rise with MRI screening recommended for women with dense breasts. Accelerated image acquisition methods can help in making this examination more accessible. KEY POINTS Accelerating breast MRI reconstruction remains a significant challenge in clinical settings. Score-based diffusion models can achieve near-perfect reconstruction for moderate undersampling factors. Faster breast MRI scans with maintained image quality could revolutionize clinic workflows and patient experience.
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Affiliation(s)
- Augustine Okolie
- Department of Radiology, University Hospital RWTH Aachen, Aachen, Germany.
| | - Timm Dirrichs
- Department of Radiology, University Hospital RWTH Aachen, Aachen, Germany
| | | | - Sven Nebelung
- Department of Radiology, University Hospital RWTH Aachen, Aachen, Germany
| | | | - Teresa Nolte
- Department of Radiology, University Hospital RWTH Aachen, Aachen, Germany
| | - Tianyu Han
- Department of Radiology, University Hospital RWTH Aachen, Aachen, Germany
| | | | - Daniel Truhn
- Department of Radiology, University Hospital RWTH Aachen, Aachen, Germany
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7
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Patel BK, Carnahan MB, Northfelt D, Anderson K, Mazza GL, Pizzitola VJ, Giurescu ME, Lorans R, Eversman WG, Sharpe RE, Harper LK, Apsey H, Cronin P, Kling J, Ernst B, Palmieri J, Fraker J, Mina L, Batalini F, Pockaj B. Prospective Study of Supplemental Screening With Contrast-Enhanced Mammography in Women With Elevated Risk of Breast Cancer: Results of the Prevalence Round. J Clin Oncol 2024:JCO2202819. [PMID: 39058970 DOI: 10.1200/jco.22.02819] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 03/14/2024] [Accepted: 05/01/2024] [Indexed: 07/28/2024] Open
Abstract
PURPOSE Contrast-enhanced mammography (CEM) and magnetic resonance imaging (MRI) have shown similar diagnostic performance in detection of breast cancer. Limited CEM data are available for high-risk breast cancer screening. The purpose of the study was to prospectively investigate the efficacy of supplemental screening CEM in elevated risk patients. MATERIALS AND METHODS A prospective, single-institution, institutional review board-approved observational study was conducted in asymptomatic elevated risk women age 35 years or older who had a negative conventional two-dimensional digital breast tomosynthesis screening mammography (MG) and no additional supplemental screening within the prior 12 months. RESULTS Four hundred sixty women were enrolled from February 2019 to April 2021. The median age was 56.8 (range, 35.0-79.2) years; 408 of 460 (88.7%) were mammographically dense. Biopsy revealed benign changes in 22 women (22/37, 59%), high-risk lesions in four women (4/37, 11%), and breast cancer in 11 women (11/37, 30%). Fourteen cancers (10 invasive, tumor size range 4-15 mm, median 9 mm) were diagnosed in 11 women. The overall supplemental cancer detection rate was 23.9 per 1,000 patients, 95% CI (12.0 to 42.4). All cancers were grade 1 or 2, ER+ ERBB2-, and node negative. CEM imaging screening offered high specificity (0.875 [95% CI, 0.844 to 0.906]), high NPV (0.998 [95% CI, 0.993 to 1.000), moderate PPV1 (0.164 [95% CI, 0.076 to 0.253), moderate PPV3 (0.275 [95% CI, 0.137 to 0.413]), and high sensitivity (0.917 [95% CI, 0.760 to 1.000]). At least 1 year of imaging follow-up was available on all patients, and one interval cancer was detected on breast MRI 4 months after negative screening CEM. CONCLUSION A pilot trial demonstrates a supplemental cancer detection rate of 23.9 per 1,000 in women at an elevated risk for breast cancer. Larger, multi-institutional, multiyear CEM trials in patients at elevated risk are needed for validation.
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Affiliation(s)
- Bhavika K Patel
- Department of Radiology, Mayo Clinic in Arizona, Phoenix, AZ
| | | | - Donald Northfelt
- Department of Medical Oncology, Mayo Clinic in Arizona, Phoenix, AZ
| | - Karen Anderson
- Department of Medical Oncology, Mayo Clinic in Arizona, Phoenix, AZ
| | - Gina L Mazza
- Department of Quantitative Health Sciences, Mayo Clinic in Arizona, Phoenix, AZ
| | | | | | - Roxanne Lorans
- Department of Radiology, Mayo Clinic in Arizona, Phoenix, AZ
| | | | | | - Laura K Harper
- Department of Radiology, Mayo Clinic in Arizona, Phoenix, AZ
| | - Heidi Apsey
- Division of Women's Health Internal Medicine, Mayo Clinic in Arizona, Phoenix, AZ
| | - Patricia Cronin
- Department of Surgical Oncology, Mayo Clinic in Arizona, Phoenix, AZ
| | - Juliana Kling
- Division of Women's Health Internal Medicine, Mayo Clinic in Arizona, Phoenix, AZ
| | - Brenda Ernst
- Department of Medical Oncology, Mayo Clinic in Arizona, Phoenix, AZ
| | | | - Jessica Fraker
- Department of Surgical Oncology, Mayo Clinic in Arizona, Phoenix, AZ
| | - Lida Mina
- Department of Medical Oncology, Mayo Clinic in Arizona, Phoenix, AZ
| | - Felipe Batalini
- Department of Medical Oncology, Mayo Clinic in Arizona, Phoenix, AZ
| | - Barbara Pockaj
- Division of Women's Health Internal Medicine, Mayo Clinic in Arizona, Phoenix, AZ
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Wang P, Wang H, Nie P, Dang Y, Liu R, Qu M, Wang J, Mu G, Jia T, Shang L, Zhu K, Feng J, Chen B. Enabling AI-Generated Content for Gadolinium-Free Contrast-Enhanced Breast Magnetic Resonance Imaging. J Magn Reson Imaging 2024. [PMID: 39052258 DOI: 10.1002/jmri.29528] [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: 04/01/2024] [Revised: 06/19/2024] [Accepted: 06/20/2024] [Indexed: 07/27/2024] Open
Abstract
BACKGROUND There is increasing interest in utilizing AI-generated content for gadolinium-free contrast-enhanced breast MRI. PURPOSE To develop a generative model for gadolinium-free contrast-enhanced breast MRI and evaluate the diagnostic utility of the generated scans. STUDY TYPE Retrospective. POPULATION Two hundred seventy-six women with 304 breast MRI examinations (49 ± 13 years, 243/61 for training/testing). FIELD STRENGTH/SEQUENCE ZOOMit diffusion-weighted imaging (DWI), T1-weighted volumetric interpolated breath-hold examination (T1W VIBE), and axial T2 3D SPACE at 3.0 T. ASSESSMENT A generative model was developed to generate contrast-enhanced scans using precontrast T1W VIBE and DWI images. The generated and real images were quantitatively compared using the structural similarity index (SSIM), mean absolute error (MAE), and Dice similarity coefficient. Three radiologists with 8, 5, and 5 years of experience independently rated the image quality and lesion visibility on AI-generated and real images within various subgroups using a five-point scale. Four breast radiologists, with 8, 8, 5, and 5 years of experience, independently and blindly interpreted four reading protocols: unenhanced MRI protocol alone and combined with AI-generated scans, abbreviated MRI protocol, and full-MRI protocol. STATISTICAL ANALYSIS Results were assessed using t-tests and McNemar tests. Using pathology diagnosis as reference standard, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for each reading protocol. A P value <0.05 was considered significant. RESULTS In the test set, the generated images showed similarity to the real images (SSIM: 0.935 ± 0.047 [SD], MAE: 0.015 ± 0.012 [SD], and Dice coefficient: 0.726 ± 0.177 [SD]). No significant difference in lesion visibility was observed between real and AI-generated scans of the mass, non-mass, and benign lesion subgroups. Adding AI-generated scans to the unenhanced MRI protocol slightly improved breast cancer detection (sensitivity: 92.86% vs. 85.71%, NPV: 76.92% vs. 70.00%); achieved non-inferior diagnostic utility compared to the AB-MRI protocol and full-protocol (sensitivity: 92.86%, 95.24%; NPV: 75.00%, 81.82%). DATA CONCLUSION AI-generated gadolinium-free contrast-enhanced breast MRI has potential to improve the sensitivity of unenhanced MRI in detecting breast cancer. EVIDENCE LEVEL 4 TECHNICAL EFFICACY: Stage 3.
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Affiliation(s)
- Pingping Wang
- Department of Xi'an International Medical Center Hospital, Northwest University, Xi'an, China
- Department of Information Science & Technology, Northwest University, Xi'an, China
| | - Hongyu Wang
- Department of School of Computer Science & Technology, Xi'an University of Posts and Telecommunications, Xi'an, China
| | - Pin Nie
- Department of Xi'an International Medical Center Hospital, Northwest University, Xi'an, China
| | - Yanli Dang
- Department of Xi'an International Medical Center Hospital, Northwest University, Xi'an, China
| | - Rumei Liu
- Department of Xi'an International Medical Center Hospital, Northwest University, Xi'an, China
| | - Mingzhu Qu
- Department of Xi'an International Medical Center Hospital, Northwest University, Xi'an, China
| | - Jiawei Wang
- Department of Xi'an International Medical Center Hospital, Northwest University, Xi'an, China
| | - Gengming Mu
- Department of Xi'an International Medical Center Hospital, Northwest University, Xi'an, China
| | - Tianju Jia
- Department of Xi'an International Medical Center Hospital, Northwest University, Xi'an, China
| | - Lei Shang
- Department of Health Statistics, School of Preventive Medicine, Fourth Military Medical University, Xi'an, China
| | - Kaiguo Zhu
- Department of Xi'an International Medical Center Hospital, Northwest University, Xi'an, China
| | - Jun Feng
- Department of Information Science & Technology, Northwest University, Xi'an, China
| | - Baoying Chen
- Department of Xi'an International Medical Center Hospital, Northwest University, Xi'an, China
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9
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Salim M, Liu Y, Sorkhei M, Ntoula D, Foukakis T, Fredriksson I, Wang Y, Eklund M, Azizpour H, Smith K, Strand F. AI-based selection of individuals for supplemental MRI in population-based breast cancer screening: the randomized ScreenTrustMRI trial. Nat Med 2024:10.1038/s41591-024-03093-5. [PMID: 38977914 DOI: 10.1038/s41591-024-03093-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Accepted: 05/23/2024] [Indexed: 07/10/2024]
Abstract
Screening mammography reduces breast cancer mortality, but studies analyzing interval cancers diagnosed after negative screens have shown that many cancers are missed. Supplemental screening using magnetic resonance imaging (MRI) can reduce the number of missed cancers. However, as qualified MRI staff are lacking, the equipment is expensive to purchase and cost-effectiveness for screening may not be convincing, the utilization of MRI is currently limited. An effective method for triaging individuals to supplemental MRI screening is therefore needed. We conducted a randomized clinical trial, ScreenTrustMRI, using a recently developed artificial intelligence (AI) tool to score each mammogram. We offered trial participation to individuals with a negative screening mammogram and a high AI score (top 6.9%). Upon agreeing to participate, individuals were assigned randomly to one of two groups: those receiving supplemental MRI and those not receiving MRI. The primary endpoint of ScreenTrustMRI is advanced breast cancer defined as either interval cancer, invasive component larger than 15 mm or lymph node positive cancer, based on a 27-month follow-up time from the initial screening. Secondary endpoints, prespecified in the study protocol to be reported before the primary outcome, include cancer detected by supplemental MRI, which is the focus of the current paper. Compared with traditional breast density measures used in a previous clinical trial, the current AI method was nearly four times more efficient in terms of cancers detected per 1,000 MRI examinations (64 versus 16.5). Most additional cancers detected were invasive and several were multifocal, suggesting that their detection was timely. Altogether, our results show that using an AI-based score to select a small proportion (6.9%) of individuals for supplemental MRI after negative mammography detects many missed cancers, making the cost per cancer detected comparable with screening mammography. ClinicalTrials.gov registration: NCT04832594 .
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Affiliation(s)
- Mattie Salim
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
- Breast Radiology Unit, Karolinska University Hospital, Stockholm, Sweden
| | - Yue Liu
- School of Computer Science and Technology, Royal Institute of Technology (KTH), Stockholm, Sweden
- Science for Life Laboratory, Stockholm, Sweden
| | - Moein Sorkhei
- School of Computer Science and Technology, Royal Institute of Technology (KTH), Stockholm, Sweden
- Science for Life Laboratory, Stockholm, Sweden
| | - Dimitra Ntoula
- Breast Radiology Unit, Karolinska University Hospital, Stockholm, Sweden
| | - Theodoros Foukakis
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
| | - Irma Fredriksson
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
| | - Yanlu Wang
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
| | - Martin Eklund
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Hossein Azizpour
- Division of Robotics, Perception, and Learning, Karolinska Institutet, Stockholm, Sweden
| | - Kevin Smith
- School of Computer Science and Technology, Royal Institute of Technology (KTH), Stockholm, Sweden
- Science for Life Laboratory, Stockholm, Sweden
| | - Fredrik Strand
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden.
- Breast Radiology Unit, Karolinska University Hospital, Stockholm, Sweden.
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10
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Seely JM, Domonkos V, Verma R. Auditing Abbreviated Breast MR Imaging: Clinical Considerations and Implications. Radiol Clin North Am 2024; 62:687-701. [PMID: 38777543 DOI: 10.1016/j.rcl.2023.12.010] [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
Abbreviated breast MR (AB-MR) imaging is a relatively new breast imaging tool, which maintains diagnostic accuracy while reducing image times compared with full-protocol breast MR (FP-MR) imaging. Breast imaging audits involve calculating individual and organizational metrics, which can be compared with established benchmarks, providing a standard against which performance can be measured. Unlike FP-MR imaging, there are no established benchmarks for AB-MR imaging but studies demonstrate comparable performance for cancer detection rate, positive predictive value 3, sensitivity, and specificity with T2. We review the basics of performing an audit, including strategies to implement if benchmarks are not being met.
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Affiliation(s)
- Jean M Seely
- Department of Radiology, The Ottawa Hospital, General Campus, 501 Smyth Road, Ottawa, Ontario K1H 8L6, Canada.
| | - Victoria Domonkos
- Department of Radiology, The Ottawa Hospital, General Campus, 501 Smyth Road, Ottawa, Ontario K1H 8L6, Canada
| | - Raman Verma
- Department of Radiology, The Ottawa Hospital, General Campus, 501 Smyth Road, Ottawa, Ontario K1H 8L6, Canada. https://twitter.com/RamanVermaMD
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11
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Liebert A, Das BK, Kapsner LA, Eberle J, Skwierawska D, Folle L, Schreiter H, Laun FB, Ohlmeyer S, Uder M, Wenkel E, Bickelhaupt S. Smart forecasting of artifacts in contrast-enhanced breast MRI before contrast agent administration. Eur Radiol 2024; 34:4752-4763. [PMID: 38099964 PMCID: PMC11213750 DOI: 10.1007/s00330-023-10469-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Revised: 10/05/2023] [Accepted: 10/21/2023] [Indexed: 12/21/2023]
Abstract
OBJECTIVES To evaluate whether artifacts on contrast-enhanced (CE) breast MRI maximum intensity projections (MIPs) might already be forecast before gadolinium-based contrast agent (GBCA) administration during an ongoing examination by analyzing the unenhanced T1-weighted images acquired before the GBCA injection. MATERIALS AND METHODS This IRB-approved retrospective analysis consisted of n = 2884 breast CE MRI examinations after intravenous administration of GBCA, acquired with n = 4 different MRI devices at different field strengths (1.5 T/3 T) during clinical routine. CE-derived subtraction MIPs were used to conduct a multi-class multi-reader evaluation of the presence and severity of artifacts with three independent readers. An ensemble classifier (EC) of five DenseNet models was used to predict artifacts for the post-contrast subtraction MIPs, giving as the input source only the pre-contrast T1-weighted sequence. Thus, the acquisition directly preceded the GBCA injection. The area under ROC (AuROC) and diagnostics accuracy scores were used to assess the performance of the neural network in an independent holdout test set (n = 285). RESULTS After majority voting, potentially significant artifacts were detected in 53.6% (n = 1521) of all breast MRI examinations (age 49.6 ± 12.6 years). In the holdout test set (mean age 49.7 ± 11.8 years), at a specificity level of 89%, the EC could forecast around one-third of artifacts (sensitivity 31%) before GBCA administration, with an AuROC = 0.66. CONCLUSION This study demonstrates the capability of a neural network to forecast the occurrence of artifacts on CE subtraction data before the GBCA administration. If confirmed in larger studies, this might enable a workflow-blended approach to prevent breast MRI artifacts by implementing in-scan personalized predictive algorithms. CLINICAL RELEVANCE STATEMENT Some artifacts in contrast-enhanced breast MRI maximum intensity projections might be predictable before gadolinium-based contrast agent injection using a neural network. KEY POINTS • Potentially significant artifacts can be observed in a relevant proportion of breast MRI subtraction sequences after gadolinium-based contrast agent administration (GBCA). • Forecasting the occurrence of such artifacts in subtraction maximum intensity projections before GBCA administration for individual patients was feasible at 89% specificity, which allowed correctly predicting one in three future artifacts. • Further research is necessary to investigate the clinical value of such smart personalized imaging approaches.
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Affiliation(s)
- Andrzej Liebert
- Institute of Radiology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.
| | - Badhan K Das
- Institute of Radiology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Lorenz A Kapsner
- Institute of Radiology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
- Medical Center for Information and Communication Technology, Universitätsklinikum Erlangen, Erlangen, Germany
| | - Jessica Eberle
- Institute of Radiology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Dominika Skwierawska
- Institute of Radiology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Lukas Folle
- Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Hannes Schreiter
- Institute of Radiology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Frederik B Laun
- Institute of Radiology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Sabine Ohlmeyer
- Institute of Radiology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Michael Uder
- Institute of Radiology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Evelyn Wenkel
- Medizinische Fakultät, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
- Radiologie München, München, Germany
| | - Sebastian Bickelhaupt
- Institute of Radiology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
- German Cancer Research Center (DKFZ), Heidelberg, Germany
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12
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Kočo L, Balkenende L, Appelman L, Moman MR, Sponsel A, Schimanski M, Prokop M, Mann RM. Optimized, Person-Centered Workflow Design for a High-Throughput Breast MRI Screening Facility-A Simulation Study. Invest Radiol 2024; 59:538-544. [PMID: 38193779 DOI: 10.1097/rli.0000000000001059] [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: 01/10/2024]
Abstract
OBJECTIVES This project aims to model an optimal scanning environment for breast magnetic resonance imaging (MRI) screening based on real-life data to identify to what extent the logistics of breast MRI can be optimized. MATERIALS AND METHODS A novel concept for a breast MRI screening facility was developed considering layout of the building, workflow steps, used resources, and MRI protocols. The envisioned screening facility is person centered and aims for an efficient workflow-oriented design. Real-life data, collected from existing breast MRI screening workflows, during 62 scans in 3 different hospitals, were imported into a 3D simulation software for designing and testing new concepts. The model provided several realistic, virtual, logistical pathways for MRI screening and their outcome measures: throughput, waiting times, and other relevant variables. RESULTS The total average appointment time in the baseline scenario was 25:54 minutes, with 19:06 minutes of MRI room occupation. Simulated improvements consisted of optimizing processes and resources, facility layout, and scanning protocol. In the simulation, time spent in the MRI room was reduced by introducing an optimized facility layout, dockable tables, and adoption of an abbreviated MRI scanning protocol. The total average appointment time was reduced to 19:36 minutes, and in this scenario, the MRI room was occupied for 06:21 minutes. In the most promising scenario, screening of about 68 people per day (10 hours) on a single MRI scanner could be feasible, compared with 36 people per day in the baseline scenario. CONCLUSIONS This study suggests that by optimizing workflow MRI for breast screening total appointment duration and MRI occupation can be reduced. A throughput of up to 6 people per hour may be achieved, compared with 3 people per hour in the current setup.
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Affiliation(s)
- Lejla Kočo
- From the Department of Imaging, Radboud University Medical Center, Nijmegen, the Netherlands (L.K., L.A., M.P., R.M.M.); Department of Radiology, The Netherlands Cancer Institute (Antoni van Leeuwenhoek), Amsterdam, the Netherlands (L.B., R.M.M.); Department of Radiology, Alexander Monro Hospital, Bilthoven, the Netherlands (L.A., M.R.M.); and Siemens Healthcare GmbH, Erlangen, Germany (A.S., M.S.)
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13
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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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14
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Faheem M, Tam HZ, Nougom M, Suaris T, Jahan N, Lloyd T, Johnson L, Aggarwal S, Ullah M, Thompson EW, Brentnall AR. Role of Supplemental Breast MRI in Screening Women with Mammographically Dense Breasts: A Systematic Review and Meta-analysis. JOURNAL OF BREAST IMAGING 2024:wbae019. [PMID: 38912622 DOI: 10.1093/jbi/wbae019] [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: 10/02/2023] [Indexed: 06/25/2024]
Abstract
BACKGROUND High mammographic density increases breast cancer risk and reduces mammographic sensitivity. We reviewed evidence on accuracy of supplemental MRI for women with dense breasts at average or increased risk. METHODS PubMed and Embase were searched 1995-2022. Articles were included if women received breast MRI following 2D or tomosynthesis mammography. Risk of bias was assessed using QUADAS-2. Analysis used independent studies from the articles. Fixed-effect meta-analytic summaries were estimated for predefined groups (PROSPERO: 230277). RESULTS Eighteen primary research articles (24 studies) were identified in women aged 19-87 years. Breast density was heterogeneously or extremely dense (BI-RADS C/D) in 15/18 articles and extremely dense (BI-RADS D) in 3/18 articles. Twelve of 18 articles reported on increased-risk populations. Following 21 440 negative mammographic examinations, 288/320 cancers were detected by MRI. Substantial variation was observed between studies in MRI cancer detection rate, partly associated with prevalent vs incident MRI exams (prevalent: 16.6/1000 exams, 12 studies; incident: 6.8/1000 exams, 7 studies). MRI had high sensitivity for mammographically occult cancer (20 studies with at least 1-year follow-up). In 5/18 articles with sufficient data to estimate relative MRI detection rate, approximately 2 in 3 cancers were detected by MRI (66.3%, 95% CI, 56.3%-75.5%) but not mammography. Positive predictive value was higher for more recent studies. Risk of bias was low in most studies. CONCLUSION Supplemental breast MRI following negative mammography in women with dense breasts has breast cancer detection rates of ~16.6/1000 at prevalent and ~6.8/1000 at incident MRI exams, considering both high and average risk settings.
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Affiliation(s)
- Michael Faheem
- Department of Breast Surgery, Barts Health NHS Trust, London, UK
| | - Hui Zhen Tam
- Wolfson Institute of Population Health, Centre for Evaluation and Methods, Queen Mary University of London, London, UK
| | - Magd Nougom
- Department of Breast Surgery, Barts Health NHS Trust, London, UK
| | - Tamara Suaris
- Department of Breast Radiology, Barts Health NHS Trust, London, UK
| | - Noor Jahan
- Department of Breast Radiology, Barts Health NHS Trust, London, UK
| | - Thomas Lloyd
- Department of Radiology, Princess Alexandra Hospital, Brisbane, Australia
| | - Laura Johnson
- Department of Breast Surgery, Barts Health NHS Trust, London, UK
| | - Shweta Aggarwal
- Department of Breast Surgery, Barts Health NHS Trust, London, UK
| | - MdZaker Ullah
- Department of Breast Surgery, Barts Health NHS Trust, London, UK
| | - Erik W Thompson
- Institute of Health and Biomedical Innovation and School of Biomedical Sciences, Queensland University of Technology, Brisbane, Australia
- Translational Research Institute, Brisbane, Australia
| | - Adam R Brentnall
- Wolfson Institute of Population Health, Centre for Evaluation and Methods, Queen Mary University of London, London, UK
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15
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Katsika L, Boureka E, Kalogiannidis I, Tsakiridis I, Tirodimos I, Lallas K, Tsimtsiou Z, Dagklis T. Screening for Breast Cancer: A Comparative Review of Guidelines. Life (Basel) 2024; 14:777. [PMID: 38929759 PMCID: PMC11204612 DOI: 10.3390/life14060777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2024] [Revised: 06/14/2024] [Accepted: 06/18/2024] [Indexed: 06/28/2024] Open
Abstract
Breast cancer is the most common malignancy diagnosed in the female population worldwide and the leading cause of death among perimenopausal women. Screening is essential, since earlier detection in combination with improvements in breast cancer treatment can reduce the associated mortality. The aim of this study was to review and compare the recommendations from published guidelines on breast cancer screening. A total of 14 guidelines on breast cancer screening issued between 2014 and 2022 were identified. A descriptive review of relevant guidelines by the World Health Organization (WHO), the U.S. Preventive Services Task Force (USPSTF), the American Cancer Society (ACS), the National Comprehensive Cancer Network (NCCN), the American College of Obstetricians and Gynecologists (ACOG), the American Society of Breast Surgeons (ASBrS), the American College of Radiology (ACR), the Task Force on Preventive Health Care (CTFPHC), the European Commission Initiative on Breast Cancer (ECIBC), the European Society for Medical Oncology (ESMO), the Royal Australian College of General Practitioners (RACGP) and the Japanese Journal of Clinical Oncology (JJCO) for women both at average and high-risk was carried out. There is a consensus among all the reviewed guidelines that mammography is the gold standard screening modality for average-risk women. For this risk group, most of the guidelines suggest annual or biennial mammographic screening at 40-74 years, while screening should particularly focus at 50-69 years. Most of the guidelines suggest that the age limit to stop screening should be determined based on the women's health status and life expectancy. For women at high-risk, most guidelines recommend the use of annual mammography or magnetic resonance imaging, while the starting age should be earlier than the average-risk group, depending on the risk factor. There is discrepancy among the recommendations regarding the age at onset of screening in the various high-risk categories. The development of consistent international practice protocols for the most appropriate breast cancer screening programs seems of major importance to reduce mortality rates and safely guide everyday clinical practice.
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Affiliation(s)
- Laskarina Katsika
- Laboratory of Hygiene, Social & Preventive Medicine and Medical Statistics, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece; (L.K.); (I.T.); (Z.T.)
| | - Eirini Boureka
- Third Department of Obstetrics and Gynecology, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece; (E.B.); (I.K.); (T.D.)
| | - Ioannis Kalogiannidis
- Third Department of Obstetrics and Gynecology, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece; (E.B.); (I.K.); (T.D.)
| | - Ioannis Tsakiridis
- Third Department of Obstetrics and Gynecology, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece; (E.B.); (I.K.); (T.D.)
| | - Ilias Tirodimos
- Laboratory of Hygiene, Social & Preventive Medicine and Medical Statistics, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece; (L.K.); (I.T.); (Z.T.)
| | - Konstantinos Lallas
- Department of Medical Oncology, School of Medicine, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece;
| | - Zoi Tsimtsiou
- Laboratory of Hygiene, Social & Preventive Medicine and Medical Statistics, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece; (L.K.); (I.T.); (Z.T.)
| | - Themistoklis Dagklis
- Third Department of Obstetrics and Gynecology, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece; (E.B.); (I.K.); (T.D.)
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16
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Franklin J, Hayes J, Knippa E, Dogan B. False negative breast cancers on imaging and associated risk factors: a single institution six-year analysis. Breast Cancer Res Treat 2024; 205:507-520. [PMID: 38483757 DOI: 10.1007/s10549-024-07259-0] [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: 05/09/2023] [Accepted: 01/18/2024] [Indexed: 05/19/2024]
Abstract
PURPOSE Mitigating false negative imaging studies remains an important issue given its association with worse morbidity and mortality in patients with breast cancer. We aimed to identify risk factors that predispose to false negative breast imaging exams. METHODS In an IRB-approved, HIPAA compliant retrospective study, we identified all patients who were diagnosed with breast cancer within 365 days of a negative imaging study assessed as BI-RADS 1-3 between January 1, 2014 and January 31, 2020. A matched cohort based on mammographic breast density was created from randomly selected studies with BI-RADS 4-5 designation that yielded breast cancer at pathology within the same time frame. Patient and cancer characteristics, prior personal history of breast cancer and gene mutation status were collected from patient charts. Pearson chi-squared and Student's t-test on two independent groups with significance at < 0.05 was used for statistical analysis. RESULTS We identified 155 false negative studies of 129 missed cancers and 128 breast density matched true positive cancers. False negative studies were screening mammograms in 57.42% (89/155), diagnostic mammograms in 29.68% (46/155), ultrasounds in 6.45% (10/155) and MRIs in 6.45% (10/155). Rates of personal (41.09% vs. 18.75%, p < 0.001) and family history of breast cancer (68.22% vs. 49.21%, p = 0.002) were higher in the false negative cohort and remained significant when asymptomatic MRI-detected cancers were removed. CONCLUSION Our findings suggest that supplemental screening may be useful in breast cancer survivors.
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Affiliation(s)
- Jordan Franklin
- The University of Texas Southwestern Medical Center Medical School, Dallas, TX, USA.
- Department of Medicine, Duke University Medical Center, Durham, North Carolina, USA.
| | - Jody Hayes
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Emily Knippa
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Başak Dogan
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, USA
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17
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Niell BL, Jochelson MS, Amir T, Brown A, Adamson M, Baron P, Bennett DL, Chetlen A, Dayaratna S, Freer PE, Ivansco LK, Klein KA, Malak SF, Mehta TS, Moy L, Neal CH, Newell MS, Richman IB, Schonberg M, Small W, Ulaner GA, Slanetz PJ. ACR Appropriateness Criteria® Female Breast Cancer Screening: 2023 Update. J Am Coll Radiol 2024; 21:S126-S143. [PMID: 38823941 DOI: 10.1016/j.jacr.2024.02.019] [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: 02/20/2024] [Accepted: 02/28/2024] [Indexed: 06/03/2024]
Abstract
Early detection of breast cancer from regular screening substantially reduces breast cancer mortality and morbidity. Multiple different imaging modalities may be used to screen for breast cancer. Screening recommendations differ based on an individual's risk of developing breast cancer. Numerous factors contribute to breast cancer risk, which is frequently divided into three major categories: average, intermediate, and high risk. For patients assigned female at birth with native breast tissue, mammography and digital breast tomosynthesis are the recommended method for breast cancer screening in all risk categories. In addition to the recommendation of mammography and digital breast tomosynthesis in high-risk patients, screening with breast MRI is recommended. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.
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Affiliation(s)
- Bethany L Niell
- Panel Chair, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida.
| | | | - Tali Amir
- Memorial Sloan Kettering Cancer Center, New York, New York
| | - Ann Brown
- Panel Vice Chair, University of Cincinnati, Cincinnati, Ohio
| | - Megan Adamson
- Clinica Family Health, Lafayette, Colorado; American Academy of Family Physicians
| | - Paul Baron
- Lenox Hill Hospital, Northwell Health, New York, New York; American College of Surgeons
| | | | - Alison Chetlen
- Penn State Health Hershey Medical Center, Hershey, Pennsylvania
| | - Sandra Dayaratna
- Thomas Jefferson University Hospital, Philadelphia, Pennsylvania; American College of Obstetricians and Gynecologists
| | | | | | | | | | - Tejas S Mehta
- UMass Memorial Medical Center/UMass Chan Medical School, Worcester, Massachusetts
| | - Linda Moy
- NYU Clinical Cancer Center, New York, New York
| | | | - Mary S Newell
- Emory University Hospital, Atlanta, Georgia; RADS Committee
| | - Ilana B Richman
- Yale School of Medicine, New Haven, Connecticut; Society of General Internal Medicine
| | - Mara Schonberg
- Harvard Medical School, Boston, Massachusetts; American Geriatrics Society
| | - William Small
- Loyola University Chicago, Stritch School of Medicine, Department of Radiation Oncology, Cardinal Bernardin Cancer Center, Maywood, Illinois; Commission on Radiation Oncology
| | - Gary A Ulaner
- Hoag Family Cancer Institute, Newport Beach, California; University of Southern California, Los Angeles, California; Commission on Nuclear Medicine and Molecular Imaging
| | - Priscilla J Slanetz
- Specialty Chair, Boston University School of Medicine, Boston, Massachusetts
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18
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Washington I, Palm RF, White J, Rosenberg SA, Ataya D. The Role of MRI in Breast Cancer and Breast Conservation Therapy. Cancers (Basel) 2024; 16:2122. [PMID: 38893241 PMCID: PMC11171236 DOI: 10.3390/cancers16112122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Revised: 05/19/2024] [Accepted: 05/24/2024] [Indexed: 06/21/2024] Open
Abstract
Contrast-enhanced breast MRI has an established role in aiding in the detection, evaluation, and management of breast cancer. This article discusses MRI sequences, the clinical utility of MRI, and how MRI has been evaluated for use in breast radiotherapy treatment planning. We highlight the contribution of MRI in the decision-making regarding selecting appropriate candidates for breast conservation therapy and review the emerging role of MRI-guided breast radiotherapy.
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Affiliation(s)
- Iman Washington
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute, 12902 USF Magnolia Drive, Tampa, FL 33612, USA;
| | - Russell F. Palm
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute, 12902 USF Magnolia Drive, Tampa, FL 33612, USA;
| | - Julia White
- Department of Radiation Oncology, The University of Kansas Medical Center, 4001 Rainbow Blvd, Kansas City, KS 66160, USA;
| | - Stephen A. Rosenberg
- Department of Radiation Therapy, H. Lee Moffitt Cancer Center & Research Institute, 12902 USF Magnolia Drive, Tampa, FL 33612, USA;
| | - Dana Ataya
- Department of Diagnostic Imaging and Interventional Radiology, H. Lee Moffitt Cancer Center & Research Institute, 10920 N. McKinley Drive, Tampa, FL 33612, USA;
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19
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Müller-Franzes G, Huck L, Bode M, Nebelung S, Kuhl C, Truhn D, Lemainque T. Diffusion probabilistic versus generative adversarial models to reduce contrast agent dose in breast MRI. Eur Radiol Exp 2024; 8:53. [PMID: 38689178 PMCID: PMC11061055 DOI: 10.1186/s41747-024-00451-3] [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: 11/15/2023] [Accepted: 02/14/2024] [Indexed: 05/02/2024] Open
Abstract
BACKGROUND To compare denoising diffusion probabilistic models (DDPM) and generative adversarial networks (GAN) for recovering contrast-enhanced breast magnetic resonance imaging (MRI) subtraction images from virtual low-dose subtraction images. METHODS Retrospective, ethically approved study. DDPM- and GAN-reconstructed single-slice subtraction images of 50 breasts with enhancing lesions were compared to original ones at three dose levels (25%, 10%, 5%) using quantitative measures and radiologic evaluations. Two radiologists stated their preference based on the reconstruction quality and scored the lesion conspicuity as compared to the original, blinded to the model. Fifty lesion-free maximum intensity projections were evaluated for the presence of false-positives. Results were compared between models and dose levels, using generalized linear mixed models. RESULTS At 5% dose, both radiologists preferred the GAN-generated images, whereas at 25% dose, both radiologists preferred the DDPM-generated images. Median lesion conspicuity scores did not differ between GAN and DDPM at 25% dose (5 versus 5, p = 1.000) and 10% dose (4 versus 4, p = 1.000). At 5% dose, both readers assigned higher conspicuity to the GAN than to the DDPM (3 versus 2, p = 0.007). In the lesion-free examinations, DDPM and GAN showed no differences in the false-positive rate at 5% (15% versus 22%), 10% (10% versus 6%), and 25% (6% versus 4%) (p = 1.000). CONCLUSIONS Both GAN and DDPM yielded promising results in low-dose image reconstruction. However, neither of them showed superior results over the other model for all dose levels and evaluation metrics. Further development is needed to counteract false-positives. RELEVANCE STATEMENT For MRI-based breast cancer screening, reducing the contrast agent dose is desirable. Diffusion probabilistic models and generative adversarial networks were capable of retrospectively enhancing the signal of low-dose images. Hence, they may supplement imaging with reduced doses in the future. KEY POINTS • Deep learning may help recover signal in low-dose contrast-enhanced breast MRI. • Two models (DDPM and GAN) were trained at different dose levels. • Radiologists preferred DDPM at 25%, and GAN images at 5% dose. • Lesion conspicuity between DDPM and GAN was similar, except at 5% dose. • GAN and DDPM yield promising results in low-dose image reconstruction.
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Affiliation(s)
- Gustav Müller-Franzes
- Department of Diagnostic and Interventional Radiology, Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - Luisa Huck
- Department of Diagnostic and Interventional Radiology, Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - Maike Bode
- Department of Diagnostic and Interventional Radiology, Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - Sven Nebelung
- Department of Diagnostic and Interventional Radiology, Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - Christiane Kuhl
- Department of Diagnostic and Interventional Radiology, Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - Daniel Truhn
- Department of Diagnostic and Interventional Radiology, Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - Teresa Lemainque
- Department of Diagnostic and Interventional Radiology, Medical Faculty, RWTH Aachen University, Aachen, Germany.
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20
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Lo Gullo R, Brunekreef J, Marcus E, Han LK, Eskreis-Winkler S, Thakur SB, Mann R, Groot Lipman K, Teuwen J, Pinker K. AI Applications to Breast MRI: Today and Tomorrow. J Magn Reson Imaging 2024. [PMID: 38581127 DOI: 10.1002/jmri.29358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 03/07/2024] [Accepted: 03/09/2024] [Indexed: 04/08/2024] Open
Abstract
In breast imaging, there is an unrelenting increase in the demand for breast imaging services, partly explained by continuous expanding imaging indications in breast diagnosis and treatment. As the human workforce providing these services is not growing at the same rate, the implementation of artificial intelligence (AI) in breast imaging has gained significant momentum to maximize workflow efficiency and increase productivity while concurrently improving diagnostic accuracy and patient outcomes. Thus far, the implementation of AI in breast imaging is at the most advanced stage with mammography and digital breast tomosynthesis techniques, followed by ultrasound, whereas the implementation of AI in breast magnetic resonance imaging (MRI) is not moving along as rapidly due to the complexity of MRI examinations and fewer available dataset. Nevertheless, there is persisting interest in AI-enhanced breast MRI applications, even as the use of and indications of breast MRI continue to expand. This review presents an overview of the basic concepts of AI imaging analysis and subsequently reviews the use cases for AI-enhanced MRI interpretation, that is, breast MRI triaging and lesion detection, lesion classification, prediction of treatment response, risk assessment, and image quality. Finally, it provides an outlook on the barriers and facilitators for the adoption of AI in breast MRI. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 6.
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Affiliation(s)
- Roberto Lo Gullo
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, New York, USA
| | - Joren Brunekreef
- AI for Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Eric Marcus
- AI for Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Lynn K Han
- Weill Cornell Medical College, New York-Presbyterian Hospital, New York City, New York, USA
| | - Sarah Eskreis-Winkler
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, New York, USA
| | - Sunitha B Thakur
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, New York, USA
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York City, New York, USA
| | - Ritse Mann
- AI for Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Kevin Groot Lipman
- AI for Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Jonas Teuwen
- AI for Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Katja Pinker
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, New York, USA
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21
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Ramli Hamid MT, Ab Mumin N, Abdul Hamid S, Ahmad Saman MS, Rahmat K. Abbreviated breast magnetic resonance imaging (MRI) or digital breast tomosynthesis for breast cancer detection in dense breasts? A retrospective preliminary study with comparable results. Clin Radiol 2024; 79:e524-e531. [PMID: 38267349 DOI: 10.1016/j.crad.2023.12.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 11/08/2023] [Accepted: 12/19/2023] [Indexed: 01/26/2024]
Abstract
AIM To compare the diagnostic performance of abbreviated breast magnetic resonance (AB-MR) imaging (MRI) and digital breast tomosynthesis (DBT) for breast cancer detection in Malaysian women with dense breasts, using histopathology as the reference standard. MATERIALS AND METHODS This was a single-centre cross-sectional study of 115 women with American College of Radiology (ACR) Breast Imaging-Reporting and Data System (BIRADS) breast density C and D on DBT with breast lesions who underwent AB-MR from June 2018 to December 2021. AB-MR was performed on a 3 T MRI system with an imaging protocol consisting of three sequences: axial T1 fat-saturated unenhanced; axial first contrast-enhanced; and subtracted first contrast-enhanced with maximum intensity projection (MIP). DBT and AB-MR images were evaluated by two radiologists blinded to the histopathology and patient outcomes. Diagnostic accuracy (sensitivity, specificity, positive predictive value [PPV] and negative predictive value [NPV]) was assessed. RESULT Of the 115 women, the mean age was 50.6 years. There were 48 (41.7%) Malay, 54 (47%) Chinese, and 12 (10.4%) Indian women. The majority (n=87, 75.7%) were from the diagnostic population. Sixty-one (53.1%) were premenopausal and 54 (46.9%) postmenopausal. Seventy-eight (72.4%) had an increased risk of developing breast cancer. Ninety-one (79.1%) women had density C and 24 (20.9%) had density D. There were 164 histopathology-proven lesions; 69 (42.1%) were malignant and 95 (57.9%) were benign. There were 62.8% (n=103/164) lesions detected at DBT. All the malignant lesions 100% (n=69) and 35.7% (n=34) of benign lesions were detected. Of the 61 lesions that were not detected, 46 (75.4%) were in density C, and 15 (24.6%) were in density D. The sensitivity, specificity, PPV, and NPV for DBT were 98.5%, 34.6%, 66.3%, and 94.7%, respectively. There were 65.2% (n=107/164) lesions detected on AB-MR, with 98.6% (n=68) malignant and 41.1% (39) benign lesions detected. The sensitivity, specificity, PPV, and NPV for AB-MR were 98.5%, 43.9%, 67.2%, and 96.2%, respectively. One malignant lesion (0.6%), which was a low-grade ductal carcinoma in-situ (DCIS), was missed on AB-MR. CONCLUSION The present findings suggest that both DBT and AB-MR have comparable effectiveness as an imaging method for detecting breast cancer and have high NPV for low-risk lesions in women with dense breasts.
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Affiliation(s)
- M T Ramli Hamid
- Department of Radiology, Faculty of Medicine University Teknologi MARA, Sungai Buloh, Selangor, Malaysia.
| | - N Ab Mumin
- Department of Radiology, Faculty of Medicine University Teknologi MARA, Sungai Buloh, Selangor, Malaysia
| | - S Abdul Hamid
- Department of Radiology, Faculty of Medicine University Teknologi MARA, Sungai Buloh, Selangor, Malaysia.
| | - M S Ahmad Saman
- Department of Public Health, Faculty of Medicine, University Teknologi MARA, Sungai Buloh, Selangor, Malaysia
| | - K Rahmat
- Department of Biomedical Imaging, University Malaya Research Imaging Centre, Kuala Lumpur, Malaysia
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22
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Abu Abeelh E, AbuAbeileh Z. Comparative Effectiveness of Mammography, Ultrasound, and MRI in the Detection of Breast Carcinoma in Dense Breast Tissue: A Systematic Review. Cureus 2024; 16:e59054. [PMID: 38800325 PMCID: PMC11128098 DOI: 10.7759/cureus.59054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/25/2024] [Indexed: 05/29/2024] Open
Abstract
This systematic review aimed to critically assess the effectiveness of mammography, ultrasound, and magnetic resonance imaging (MRI) in the detection of breast carcinoma within dense breast tissue. An exhaustive search of contemporary literature was undertaken, focusing on the diagnostic accuracy, false positive and negative rates, and clinical implications of the aforementioned imaging modalities. Each modality was assessed in isolation and side by side against the others to draw comparative inferences. While mammography remains a foundational imaging modality, its effectiveness waned within the context of dense breast tissue. Ultrasound demonstrated a strong differentiation prowess, especially among specific demographic cohorts. MRI, despite its exceptional precision and differentiation capabilities, exhibited a tendency for slightly elevated false positive rates. No single modality emerged as singularly superior for all cases. Instead, an integrated approach, combining the strengths of each modality based on individual patient profiles and clinical scenarios, is recommended. This tailored approach ensures optimized detection rates and minimizes diagnostic ambiguities, underscoring the significance of individualized patient care in the field of diagnostic radiology.
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23
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Aribal E, Guldogan N, Seker ME, Yilmaz E, Turk EB. MRI only detected lesions: Can contrast enhanced mammography guided biopsy be an alternative method: Initial clinical findings. Eur J Radiol 2024; 173:111373. [PMID: 38364588 DOI: 10.1016/j.ejrad.2024.111373] [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: 10/28/2023] [Revised: 01/22/2024] [Accepted: 02/13/2024] [Indexed: 02/18/2024]
Abstract
OBJECTIVE This study aims to analyze our initial findings regarding CEM-guided stereotactic vacuum-assisted biopsy for MRI-only detected lesions and compare biopsy times by MRI-guided biopsy. MATERIALS AND METHODS In this retrospective analysis, CEM-guided biopsies of MRI-only detected breast lesions from December 2021 to June 2023were included. Patient demographics, breast density, lesion size, background parenchymal enhancement on CEM, lesion positioning, procedure duration, and number of scout views were documented. Initially, seven patients had CEM imaging before biopsy; for later cases, CEM scout views were used for simultaneous lesion depiction and targeting. RESULTS Two cases were excluded from the initial 28 patients with 29 lesions resulting in a total of 27 lesions in 26 women (mean age:44.96 years). Lesion sizes ranged from 4.5 to 41 mm, with two as masses and the remaining as non-mass enhancements. Histopathological results identified nine malignancies (33.3 %, 9/27), including invasive cancers (55.6 %, 5/9) and DCIS (44.4 %, 4/9). The biopsy PPV rate was 33.3 %. Benign lesions comprised 66.7 %, with 22.2 % high-risk lesions. The biopsy success rate was 93.1 % (27/29), and minor complications occurred in seven cases (25.9 %, 7/27), mainly small hematomas and one vasovagal reaction (3.7 %, 1/27). Median number of scout views required was 2, with no significant differences between cases with or without prior CEM (P = 0.8). Median duration time for biopsy was 14 min, significantly shorter than MRI-guided bx at the same institution (P < 0.001) by 24 min with predominantly upright positioning of the patient (88.9 %) and horizontal approach of the needle (92.6 %). CONCLUSION This study showed that CEM-guided biopsy is a feasible and safe alternative method and a faster solution for MRI-only detected enhancing lesions and can be accurately performed without the need for prior CEM imaging.
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Affiliation(s)
- Erkin Aribal
- Acibadem University, School of Medicine, Istanbul, Turkey; Acibadem Altunizade Hospital, Istanbul, Turkey.
| | | | | | - Ebru Yilmaz
- Acibadem Altunizade Hospital, Istanbul, Turkey.
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24
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Jing X, Dorrius MD, Zheng S, Wielema M, Oudkerk M, Sijens PE, van Ooijen PMA. Localization of contrast-enhanced breast lesions in ultrafast screening MRI using deep convolutional neural networks. Eur Radiol 2024; 34:2084-2092. [PMID: 37658141 PMCID: PMC10873226 DOI: 10.1007/s00330-023-10184-3] [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: 05/10/2023] [Revised: 06/20/2023] [Accepted: 07/21/2023] [Indexed: 09/03/2023]
Abstract
OBJECTIVES To develop a deep learning-based method for contrast-enhanced breast lesion detection in ultrafast screening MRI. MATERIALS AND METHODS A total of 837 breast MRI exams of 488 consecutive patients were included. Lesion's location was independently annotated in the maximum intensity projection (MIP) image of the last time-resolved angiography with stochastic trajectories (TWIST) sequence for each individual breast, resulting in 265 lesions (190 benign, 75 malignant) in 163 breasts (133 women). YOLOv5 models were fine-tuned using training sets containing the same number of MIP images with and without lesions. A long short-term memory (LSTM) network was employed to help reduce false positive predictions. The integrated system was then evaluated on test sets containing enriched uninvolved breasts during cross-validation to mimic the performance in a screening scenario. RESULTS In five-fold cross-validation, the YOLOv5x model showed a sensitivity of 0.95, 0.97, 0.98, and 0.99, with 0.125, 0.25, 0.5, and 1 false positive per breast, respectively. The LSTM network reduced 15.5% of the false positive prediction from the YOLO model, and the positive predictive value was increased from 0.22 to 0.25. CONCLUSIONS A fine-tuned YOLOv5x model can detect breast lesions on ultrafast MRI with high sensitivity in a screening population, and the output of the model could be further refined by an LSTM network to reduce the amount of false positive predictions. CLINICAL RELEVANCE STATEMENT The proposed integrated system would make the ultrafast MRI screening process more effective by assisting radiologists in prioritizing suspicious examinations and supporting the diagnostic workup. KEY POINTS • Deep convolutional neural networks could be utilized to automatically pinpoint breast lesions in screening MRI with high sensitivity. • False positive predictions significantly increased when the detection models were tested on highly unbalanced test sets with more normal scans. • Dynamic enhancement patterns of breast lesions during contrast inflow learned by the long short-term memory networks helped to reduce false positive predictions.
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Affiliation(s)
- Xueping Jing
- Department of Radiation Oncology, and Data Science Center in Health (DASH), Machine Learning Lab, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands.
| | - Monique D Dorrius
- Department of Radiology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands
| | - Sunyi Zheng
- School of Engineering, Artificial Intelligence and Biomedical Image Analysis Lab, Westlake University, No.18 Shilongshan, Road Cloud Town, Xihu District, Hangzhou, 310024, Zhejiang, China
| | - Mirjam Wielema
- Department of Radiology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands
| | - Matthijs Oudkerk
- Faculty of Medical Sciences, University of Groningen, and Institute of Diagnostic Accuracy, Wiersmastraat 5, 9713 GH, Groningen, The Netherlands
| | - Paul E Sijens
- Department of Radiology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands
| | - Peter M A van Ooijen
- Department of Radiation Oncology, and Data Science Center in Health (DASH), Machine Learning Lab, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands
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25
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Freitas V, Li X, Scaranelo A, Au F, Kulkarni S, Ghai S, Taeb S, Bubon O, Baldassi B, Komarov B, Parker S, Macsemchuk CA, Waterston M, Olsen KO, Reznik A. Breast Cancer Detection Using a Low-Dose Positron Emission Digital Mammography System. Radiol Imaging Cancer 2024; 6:e230020. [PMID: 38334470 PMCID: PMC10988332 DOI: 10.1148/rycan.230020] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 10/16/2023] [Accepted: 10/17/2023] [Indexed: 02/10/2024]
Abstract
Purpose To investigate the feasibility of low-dose positron emission mammography (PEM) concurrently to MRI to identify breast cancer and determine its local extent. Materials and Methods In this research ethics board-approved prospective study, participants newly diagnosed with breast cancer with concurrent breast MRI acquisitions were assigned independently of breast density, tumor size, and histopathologic cancer subtype to undergo low-dose PEM with up to 185 MBq of fluorine 18-labeled fluorodeoxyglucose (18F-FDG). Two breast radiologists, unaware of the cancer location, reviewed PEM images taken 1 and 4 hours following 18F-FDG injection. Findings were correlated with histopathologic results. Detection accuracy and participant details were examined using logistic regression and summary statistics, and a comparative analysis assessed the efficacy of PEM and MRI additional lesions detection (ClinicalTrials.gov: NCT03520218). Results Twenty-five female participants (median age, 52 years; range, 32-85 years) comprised the cohort. Twenty-four of 25 (96%) cancers (19 invasive cancers and five in situ diseases) were identified with PEM from 100 sets of bilateral images, showcasing comparable performance even after 3 hours of radiotracer uptake. The median invasive cancer size was 31 mm (range, 10-120). Three additional in situ grade 2 lesions were missed at PEM. While not significant, PEM detected fewer false-positive additional lesions compared with MRI (one of six [16%] vs eight of 13 [62%]; P = .14). Conclusion This study suggests the feasibility of a low-dose PEM system in helping to detect invasive breast cancer. Though large-scale clinical trials are essential to confirm these preliminary results, this study underscores the potential of this low-dose PEM system as a promising imaging tool in breast cancer diagnosis. ClinicalTrials.gov registration no. NCT03520218 Keywords: Positron Emission Digital Mammography, Invasive Breast Cancer, Oncology, MRI Supplemental material is available for this article. © RSNA, 2024 See also commentary by Barreto and Rapelyea in this issue.
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Affiliation(s)
- Vivianne Freitas
- From the Temerty Faculty of Medicine, Joint Department of Medical
Imaging, University Health Network, Sinai Health System, Women's College
Hospital, University of Toronto, 610 University Ave, Toronto, ON, Canada M5G 2M9
(V.F., A.S., F.A., S.K., S.G.); Department of Biostatistics, Princess Margaret
Cancer Centre, University Health Network, Toronto, Canada (X.L.); Thunder Bay
Regional Health Research Institute, Thunder Bay, Canada (S.T., O.B., A.R.);
Lakehead University, Thunder Bay, Canada (O.B., B.B., A.R.); Radialis Inc,
Thunder Bay, Canada (O.B., B.B., B.K., S.P., C.A.M., M.W., K.O.O.); Institute of
Biomedical Engineering, University of Toronto, Toronto, Canada (C.A.M.); and
Posluns Centre for Image-Guided Innovation and Therapeutic Intervention, The
Hospital for Sick Children, Toronto, Canada (C.A.M.)
| | - Xuan Li
- From the Temerty Faculty of Medicine, Joint Department of Medical
Imaging, University Health Network, Sinai Health System, Women's College
Hospital, University of Toronto, 610 University Ave, Toronto, ON, Canada M5G 2M9
(V.F., A.S., F.A., S.K., S.G.); Department of Biostatistics, Princess Margaret
Cancer Centre, University Health Network, Toronto, Canada (X.L.); Thunder Bay
Regional Health Research Institute, Thunder Bay, Canada (S.T., O.B., A.R.);
Lakehead University, Thunder Bay, Canada (O.B., B.B., A.R.); Radialis Inc,
Thunder Bay, Canada (O.B., B.B., B.K., S.P., C.A.M., M.W., K.O.O.); Institute of
Biomedical Engineering, University of Toronto, Toronto, Canada (C.A.M.); and
Posluns Centre for Image-Guided Innovation and Therapeutic Intervention, The
Hospital for Sick Children, Toronto, Canada (C.A.M.)
| | - Anabel Scaranelo
- From the Temerty Faculty of Medicine, Joint Department of Medical
Imaging, University Health Network, Sinai Health System, Women's College
Hospital, University of Toronto, 610 University Ave, Toronto, ON, Canada M5G 2M9
(V.F., A.S., F.A., S.K., S.G.); Department of Biostatistics, Princess Margaret
Cancer Centre, University Health Network, Toronto, Canada (X.L.); Thunder Bay
Regional Health Research Institute, Thunder Bay, Canada (S.T., O.B., A.R.);
Lakehead University, Thunder Bay, Canada (O.B., B.B., A.R.); Radialis Inc,
Thunder Bay, Canada (O.B., B.B., B.K., S.P., C.A.M., M.W., K.O.O.); Institute of
Biomedical Engineering, University of Toronto, Toronto, Canada (C.A.M.); and
Posluns Centre for Image-Guided Innovation and Therapeutic Intervention, The
Hospital for Sick Children, Toronto, Canada (C.A.M.)
| | - Frederick Au
- From the Temerty Faculty of Medicine, Joint Department of Medical
Imaging, University Health Network, Sinai Health System, Women's College
Hospital, University of Toronto, 610 University Ave, Toronto, ON, Canada M5G 2M9
(V.F., A.S., F.A., S.K., S.G.); Department of Biostatistics, Princess Margaret
Cancer Centre, University Health Network, Toronto, Canada (X.L.); Thunder Bay
Regional Health Research Institute, Thunder Bay, Canada (S.T., O.B., A.R.);
Lakehead University, Thunder Bay, Canada (O.B., B.B., A.R.); Radialis Inc,
Thunder Bay, Canada (O.B., B.B., B.K., S.P., C.A.M., M.W., K.O.O.); Institute of
Biomedical Engineering, University of Toronto, Toronto, Canada (C.A.M.); and
Posluns Centre for Image-Guided Innovation and Therapeutic Intervention, The
Hospital for Sick Children, Toronto, Canada (C.A.M.)
| | - Supriya Kulkarni
- From the Temerty Faculty of Medicine, Joint Department of Medical
Imaging, University Health Network, Sinai Health System, Women's College
Hospital, University of Toronto, 610 University Ave, Toronto, ON, Canada M5G 2M9
(V.F., A.S., F.A., S.K., S.G.); Department of Biostatistics, Princess Margaret
Cancer Centre, University Health Network, Toronto, Canada (X.L.); Thunder Bay
Regional Health Research Institute, Thunder Bay, Canada (S.T., O.B., A.R.);
Lakehead University, Thunder Bay, Canada (O.B., B.B., A.R.); Radialis Inc,
Thunder Bay, Canada (O.B., B.B., B.K., S.P., C.A.M., M.W., K.O.O.); Institute of
Biomedical Engineering, University of Toronto, Toronto, Canada (C.A.M.); and
Posluns Centre for Image-Guided Innovation and Therapeutic Intervention, The
Hospital for Sick Children, Toronto, Canada (C.A.M.)
| | - Sandeep Ghai
- From the Temerty Faculty of Medicine, Joint Department of Medical
Imaging, University Health Network, Sinai Health System, Women's College
Hospital, University of Toronto, 610 University Ave, Toronto, ON, Canada M5G 2M9
(V.F., A.S., F.A., S.K., S.G.); Department of Biostatistics, Princess Margaret
Cancer Centre, University Health Network, Toronto, Canada (X.L.); Thunder Bay
Regional Health Research Institute, Thunder Bay, Canada (S.T., O.B., A.R.);
Lakehead University, Thunder Bay, Canada (O.B., B.B., A.R.); Radialis Inc,
Thunder Bay, Canada (O.B., B.B., B.K., S.P., C.A.M., M.W., K.O.O.); Institute of
Biomedical Engineering, University of Toronto, Toronto, Canada (C.A.M.); and
Posluns Centre for Image-Guided Innovation and Therapeutic Intervention, The
Hospital for Sick Children, Toronto, Canada (C.A.M.)
| | - Samira Taeb
- From the Temerty Faculty of Medicine, Joint Department of Medical
Imaging, University Health Network, Sinai Health System, Women's College
Hospital, University of Toronto, 610 University Ave, Toronto, ON, Canada M5G 2M9
(V.F., A.S., F.A., S.K., S.G.); Department of Biostatistics, Princess Margaret
Cancer Centre, University Health Network, Toronto, Canada (X.L.); Thunder Bay
Regional Health Research Institute, Thunder Bay, Canada (S.T., O.B., A.R.);
Lakehead University, Thunder Bay, Canada (O.B., B.B., A.R.); Radialis Inc,
Thunder Bay, Canada (O.B., B.B., B.K., S.P., C.A.M., M.W., K.O.O.); Institute of
Biomedical Engineering, University of Toronto, Toronto, Canada (C.A.M.); and
Posluns Centre for Image-Guided Innovation and Therapeutic Intervention, The
Hospital for Sick Children, Toronto, Canada (C.A.M.)
| | - Oleksandr Bubon
- From the Temerty Faculty of Medicine, Joint Department of Medical
Imaging, University Health Network, Sinai Health System, Women's College
Hospital, University of Toronto, 610 University Ave, Toronto, ON, Canada M5G 2M9
(V.F., A.S., F.A., S.K., S.G.); Department of Biostatistics, Princess Margaret
Cancer Centre, University Health Network, Toronto, Canada (X.L.); Thunder Bay
Regional Health Research Institute, Thunder Bay, Canada (S.T., O.B., A.R.);
Lakehead University, Thunder Bay, Canada (O.B., B.B., A.R.); Radialis Inc,
Thunder Bay, Canada (O.B., B.B., B.K., S.P., C.A.M., M.W., K.O.O.); Institute of
Biomedical Engineering, University of Toronto, Toronto, Canada (C.A.M.); and
Posluns Centre for Image-Guided Innovation and Therapeutic Intervention, The
Hospital for Sick Children, Toronto, Canada (C.A.M.)
| | - Brandon Baldassi
- From the Temerty Faculty of Medicine, Joint Department of Medical
Imaging, University Health Network, Sinai Health System, Women's College
Hospital, University of Toronto, 610 University Ave, Toronto, ON, Canada M5G 2M9
(V.F., A.S., F.A., S.K., S.G.); Department of Biostatistics, Princess Margaret
Cancer Centre, University Health Network, Toronto, Canada (X.L.); Thunder Bay
Regional Health Research Institute, Thunder Bay, Canada (S.T., O.B., A.R.);
Lakehead University, Thunder Bay, Canada (O.B., B.B., A.R.); Radialis Inc,
Thunder Bay, Canada (O.B., B.B., B.K., S.P., C.A.M., M.W., K.O.O.); Institute of
Biomedical Engineering, University of Toronto, Toronto, Canada (C.A.M.); and
Posluns Centre for Image-Guided Innovation and Therapeutic Intervention, The
Hospital for Sick Children, Toronto, Canada (C.A.M.)
| | - Borys Komarov
- From the Temerty Faculty of Medicine, Joint Department of Medical
Imaging, University Health Network, Sinai Health System, Women's College
Hospital, University of Toronto, 610 University Ave, Toronto, ON, Canada M5G 2M9
(V.F., A.S., F.A., S.K., S.G.); Department of Biostatistics, Princess Margaret
Cancer Centre, University Health Network, Toronto, Canada (X.L.); Thunder Bay
Regional Health Research Institute, Thunder Bay, Canada (S.T., O.B., A.R.);
Lakehead University, Thunder Bay, Canada (O.B., B.B., A.R.); Radialis Inc,
Thunder Bay, Canada (O.B., B.B., B.K., S.P., C.A.M., M.W., K.O.O.); Institute of
Biomedical Engineering, University of Toronto, Toronto, Canada (C.A.M.); and
Posluns Centre for Image-Guided Innovation and Therapeutic Intervention, The
Hospital for Sick Children, Toronto, Canada (C.A.M.)
| | - Shayna Parker
- From the Temerty Faculty of Medicine, Joint Department of Medical
Imaging, University Health Network, Sinai Health System, Women's College
Hospital, University of Toronto, 610 University Ave, Toronto, ON, Canada M5G 2M9
(V.F., A.S., F.A., S.K., S.G.); Department of Biostatistics, Princess Margaret
Cancer Centre, University Health Network, Toronto, Canada (X.L.); Thunder Bay
Regional Health Research Institute, Thunder Bay, Canada (S.T., O.B., A.R.);
Lakehead University, Thunder Bay, Canada (O.B., B.B., A.R.); Radialis Inc,
Thunder Bay, Canada (O.B., B.B., B.K., S.P., C.A.M., M.W., K.O.O.); Institute of
Biomedical Engineering, University of Toronto, Toronto, Canada (C.A.M.); and
Posluns Centre for Image-Guided Innovation and Therapeutic Intervention, The
Hospital for Sick Children, Toronto, Canada (C.A.M.)
| | - Craig A. Macsemchuk
- From the Temerty Faculty of Medicine, Joint Department of Medical
Imaging, University Health Network, Sinai Health System, Women's College
Hospital, University of Toronto, 610 University Ave, Toronto, ON, Canada M5G 2M9
(V.F., A.S., F.A., S.K., S.G.); Department of Biostatistics, Princess Margaret
Cancer Centre, University Health Network, Toronto, Canada (X.L.); Thunder Bay
Regional Health Research Institute, Thunder Bay, Canada (S.T., O.B., A.R.);
Lakehead University, Thunder Bay, Canada (O.B., B.B., A.R.); Radialis Inc,
Thunder Bay, Canada (O.B., B.B., B.K., S.P., C.A.M., M.W., K.O.O.); Institute of
Biomedical Engineering, University of Toronto, Toronto, Canada (C.A.M.); and
Posluns Centre for Image-Guided Innovation and Therapeutic Intervention, The
Hospital for Sick Children, Toronto, Canada (C.A.M.)
| | - Michael Waterston
- From the Temerty Faculty of Medicine, Joint Department of Medical
Imaging, University Health Network, Sinai Health System, Women's College
Hospital, University of Toronto, 610 University Ave, Toronto, ON, Canada M5G 2M9
(V.F., A.S., F.A., S.K., S.G.); Department of Biostatistics, Princess Margaret
Cancer Centre, University Health Network, Toronto, Canada (X.L.); Thunder Bay
Regional Health Research Institute, Thunder Bay, Canada (S.T., O.B., A.R.);
Lakehead University, Thunder Bay, Canada (O.B., B.B., A.R.); Radialis Inc,
Thunder Bay, Canada (O.B., B.B., B.K., S.P., C.A.M., M.W., K.O.O.); Institute of
Biomedical Engineering, University of Toronto, Toronto, Canada (C.A.M.); and
Posluns Centre for Image-Guided Innovation and Therapeutic Intervention, The
Hospital for Sick Children, Toronto, Canada (C.A.M.)
| | - Kenneth O. Olsen
- From the Temerty Faculty of Medicine, Joint Department of Medical
Imaging, University Health Network, Sinai Health System, Women's College
Hospital, University of Toronto, 610 University Ave, Toronto, ON, Canada M5G 2M9
(V.F., A.S., F.A., S.K., S.G.); Department of Biostatistics, Princess Margaret
Cancer Centre, University Health Network, Toronto, Canada (X.L.); Thunder Bay
Regional Health Research Institute, Thunder Bay, Canada (S.T., O.B., A.R.);
Lakehead University, Thunder Bay, Canada (O.B., B.B., A.R.); Radialis Inc,
Thunder Bay, Canada (O.B., B.B., B.K., S.P., C.A.M., M.W., K.O.O.); Institute of
Biomedical Engineering, University of Toronto, Toronto, Canada (C.A.M.); and
Posluns Centre for Image-Guided Innovation and Therapeutic Intervention, The
Hospital for Sick Children, Toronto, Canada (C.A.M.)
| | - Alla Reznik
- From the Temerty Faculty of Medicine, Joint Department of Medical
Imaging, University Health Network, Sinai Health System, Women's College
Hospital, University of Toronto, 610 University Ave, Toronto, ON, Canada M5G 2M9
(V.F., A.S., F.A., S.K., S.G.); Department of Biostatistics, Princess Margaret
Cancer Centre, University Health Network, Toronto, Canada (X.L.); Thunder Bay
Regional Health Research Institute, Thunder Bay, Canada (S.T., O.B., A.R.);
Lakehead University, Thunder Bay, Canada (O.B., B.B., A.R.); Radialis Inc,
Thunder Bay, Canada (O.B., B.B., B.K., S.P., C.A.M., M.W., K.O.O.); Institute of
Biomedical Engineering, University of Toronto, Toronto, Canada (C.A.M.); and
Posluns Centre for Image-Guided Innovation and Therapeutic Intervention, The
Hospital for Sick Children, Toronto, Canada (C.A.M.)
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26
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Kerlikowske K, Zhu W, Su YR, Sprague BL, Stout NK, Onega T, O’Meara ES, Henderson LM, Tosteson ANA, Wernli K, Miglioretti DL. Supplemental magnetic resonance imaging plus mammography compared with magnetic resonance imaging or mammography by extent of breast density. J Natl Cancer Inst 2024; 116:249-257. [PMID: 37897090 PMCID: PMC10852604 DOI: 10.1093/jnci/djad201] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 09/13/2023] [Accepted: 09/18/2023] [Indexed: 10/29/2023] Open
Abstract
BACKGROUND Examining screening outcomes by breast density for breast magnetic resonance imaging (MRI) with or without mammography could inform discussions about supplemental MRI in women with dense breasts. METHODS We evaluated 52 237 women aged 40-79 years who underwent 2611 screening MRIs alone and 6518 supplemental MRI plus mammography pairs propensity score-matched to 65 810 screening mammograms. Rates per 1000 examinations of interval, advanced, and screen-detected early stage invasive cancers and false-positive recall and biopsy recommendation were estimated by breast density (nondense = almost entirely fatty or scattered fibroglandular densities; dense = heterogeneously/extremely dense) adjusting for registry, examination year, age, race and ethnicity, family history of breast cancer, and prior breast biopsy. RESULTS Screen-detected early stage cancer rates were statistically higher for MRI plus mammography vs mammography for nondense (9.3 vs 2.9; difference = 6.4, 95% confidence interval [CI] = 2.5 to 10.3) and dense (7.5 vs 3.5; difference = 4.0, 95% CI = 1.4 to 6.7) breasts and for MRI vs MRI plus mammography for dense breasts (19.2 vs 7.5; difference = 11.7, 95% CI = 4.6 to 18.8). Interval rates were not statistically different for MRI plus mammography vs mammography for nondense (0.8 vs 0.5; difference = 0.4, 95% CI = -0.8 to 1.6) or dense breasts (1.5 vs 1.4; difference = 0.0, 95% CI = -1.2 to 1.3), nor were advanced cancer rates. Interval rates were not statistically different for MRI vs MRI plus mammography for nondense (2.6 vs 0.8; difference = 1.8 (95% CI = -2.0 to 5.5) or dense breasts (0.6 vs 1.5; difference = -0.9, 95% CI = -2.5 to 0.7), nor were advanced cancer rates. False-positive recall and biopsy recommendation rates were statistically higher for MRI groups than mammography alone. CONCLUSION MRI screening with or without mammography increased rates of screen-detected early stage cancer and false-positives for women with dense breasts without a concomitant decrease in advanced or interval cancers.
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Affiliation(s)
- Karla Kerlikowske
- Departments of Medicine and Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
- General Internal Medicine Section, Department of Veterans Affairs, University of California, San Francisco, CA, USA
| | - Weiwei Zhu
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, WA, USA
| | - Yu-Ru Su
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, WA, USA
| | - Brian L Sprague
- Departments of Surgery and Radiology, University of Vermont, Burlington, VT, USA
| | - Natasha K Stout
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA
| | - Tracy Onega
- Department of Population Health Sciences, University of Utah, Huntsman Cancer Institute, Salt Lake City, UT, USA
| | - Ellen S O’Meara
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, WA, USA
| | - Louise M Henderson
- Department of Radiology, University of North Carolina, Chapel Hill, NC, USA
| | - Anna N A Tosteson
- The Dartmouth Institute for Health Policy and Clinical Practice and Dartmouth Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
| | - Karen Wernli
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, WA, USA
| | - Diana L Miglioretti
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, WA, USA
- Department of Public Health Sciences, University of California, Davis, CA, USA
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27
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Eriksson M, Román M, Gräwingholt A, Castells X, Nitrosi A, Pattacini P, Heywang-Köbrunner S, Rossi PG. European validation of an image-derived AI-based short-term risk model for individualized breast cancer screening-a nested case-control study. THE LANCET REGIONAL HEALTH. EUROPE 2024; 37:100798. [PMID: 38362558 PMCID: PMC10866984 DOI: 10.1016/j.lanepe.2023.100798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 11/14/2023] [Accepted: 11/15/2023] [Indexed: 02/17/2024]
Abstract
Background Image-derived artificial intelligence (AI)-based risk models for breast cancer have shown high discriminatory performances compared with clinical risk models based on family history and lifestyle factors. However, little is known about their generalizability across European screening settings. We therefore investigated the discriminatory performances of an AI-based risk model in European screening settings. Methods Using four European screening populations in three countries (Italy, Spain, Germany) screened between 2009 and 2020 for women aged 45-69, we performed a nested case-control study to assess the predictive performance of an AI-based risk model. In total, 739 women with incident breast cancers were included together with 7812 controls matched on year of study-entry. Mammographic features (density, microcalcifications, masses, left-right breast asymmetries of these features) were extracted using AI from negative digital mammograms at study-entry. Two-year absolute risks of breast cancer were predicted and assessed after two years of follow-up. Adjusted risk stratification performance metrics were reported per clinical guidelines. Findings The overall adjusted Area Under the receiver operating characteristic Curve (aAUC) of the AI risk model was 0.72 (95% CI 0.70-0.75) for breast cancers developed in four screening populations. In the 6.2% [529/8551] of women at high risk using the National Institute of Health and Care Excellence (NICE) guidelines thresholds, cancers were more likely diagnosed after 2 years follow-up, risk-ratio (RR) 6.7 (95% CI 5.6-8.0), compared with the 69% [5907/8551] of women classified at general risk by the model. Similar risk-ratios were observed across levels of mammographic density. Interpretation The AI risk model showed generalizable discriminatory performances across European populations and, predicted ∼30% of clinically relevant stage 2 and higher breast cancers in ∼6% of high-risk women who were sent home with a negative mammogram. Similar results were seen in women with fatty and dense breasts. Funding Swedish Research Council.
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Affiliation(s)
- Mikael Eriksson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Public Health and Primary Care, University of Cambridge, UK
| | - Marta Román
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
| | | | - Xavier Castells
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
| | - Andrea Nitrosi
- Azienda Unitá Sanitaria Locale-IRCCS di Reggio Emilia, Reggia Emilia, Italy
| | | | | | - Paolo G. Rossi
- Azienda Unitá Sanitaria Locale-IRCCS di Reggio Emilia, Reggia Emilia, Italy
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28
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Zoghbi KK, Felipe VC, Graziano L, Guatelli CS, de Souza JA, Bitencourt AGV. Analysis of the indications for and results of breast cancer screening by magnetic resonance imaging at a cancer center in Brazil. Radiol Bras 2024; 57:e20230111en. [PMID: 38993971 PMCID: PMC11235068 DOI: 10.1590/0100-3984.2023.0111-en] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 11/01/2023] [Accepted: 11/17/2023] [Indexed: 07/13/2024] Open
Abstract
Objective To evaluate the indications for and results of magnetic resonance imaging (MRI) examinations for breast cancer screening at a cancer center in Brazil. Materials and Methods This was a retrospective observational study, based on electronic medical records, of patients undergoing MRI for breast cancer screening at a cancer center in Brazil. Results We included 597 patients between 19 and 82 years of age. The main indications for MRI screening were a personal history of breast cancer, in 354 patients (59.3%), a family history of breast cancer, in 102 (17.1%), and a confirmed genetic mutation, in 67 (11.2%). The MRI result was classified, in accordance with the categories defined in the Breast Imaging Reporting and Data System, as benign (category 1 or 2), in 425 patients (71.2%), probably benign (category 3), in 143 (24.0%), or suspicious (category 4 or 5), in 29 (4.9%). On MRI, 11 malignant tumors were identified, all of which were invasive carcinomas. Among those 11 carcinomas, six (54.5%) were categorized as minimal cancers (< 1 cm), and the axillary lymph nodes were negative in 10 (90.9%). The cancer detection rate was 18.4/1,000 examinations, and the positive predictive value for suspicious lesions submitted to biopsy was 37.9%. Conclusion In our sample, the main indication for breast MRI screening was a personal history of breast cancer. The results indicate that MRI is a highly accurate method for the early detection of breast neoplasms in this population.
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Affiliation(s)
- Karina Kuhl Zoghbi
- Graduate Program, A.C.Camargo Cancer Center, São Paulo, SP,
Brazil
- Hospital Saúde da Mulher, Belém, PA, Brazil
| | | | - Luciana Graziano
- Department of Imaging, A.C.Camargo Cancer Center, São Paulo,
SP, Brazil
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29
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Ghuman N, Ambinder EB, Oluyemi ET, Sutton E, Myers KS. Clinical and Imaging Features of MRI Screen-Detected Breast Cancer. Clin Breast Cancer 2024; 24:45-52. [PMID: 37821332 PMCID: PMC11328159 DOI: 10.1016/j.clbc.2023.09.012] [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: 07/13/2023] [Revised: 08/28/2023] [Accepted: 09/19/2023] [Indexed: 10/13/2023]
Abstract
BACKGROUND Supplemental screening with breast MRI is recommended annually for patients who have greater than 20% lifetime risk for breast cancer. While there is robust data regarding features of mammographic screen-detected breast cancers, there is limited data regarding MRI-screen-detected cancers. PATIENTS AND METHODS Screening breast MRIs performed between August 1, 2016 and July 30, 2022 identified 50 screen-detected breast cancers in 47 patients. Clinical and imaging features of all eligible cancers were recorded. RESULTS During the study period, 50 MRI-screen detected cancers were identified in 47 patients. The majority of MRI-screen detected cancers (32/50, 64%) were invasive. Pathology revealed ductal carcinoma in situ (DCIS) in 36% (18/50), invasive ductal carcinoma (IDC) in 52% (26/50), invasive lobular carcinoma in 10% (5/50), and angiosarcoma in 2% (1/50). The majority of patients (43/47, 91%) were stage 0 or 1 at diagnosis and there were no breast cancer-related deaths during the follow-up periods. Cancers presented as masses in 50% (25/50), nonmass enhancement in 48% (25/50), and a focus in 2% (1/50). DCIS was more likely to present as nonmass enhancement (94.4%, 17/18), whereas invasive cancers were more likely to present as masses (75%, 24/32) (P < .001). All cancers that were stage 2 at diagnosis were detected either on a baseline exam or more than 4 years since the prior MRI exam. CONCLUSION MRI screen-detected breast cancers were most often invasive cancers. Cancers detected by MRI screening had an excellent prognosis in our study population. Invasive cancers most commonly presented as a mass.
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Affiliation(s)
- Naveen Ghuman
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD.
| | - Emily B Ambinder
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Eniola T Oluyemi
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD
| | | | - Kelly S Myers
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD
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30
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Tollens F, Baltzer PA, Froelich MF, Kaiser CG. Economic evaluation of breast MRI in screening - a systematic review and basic approach to cost-effectiveness analyses. Front Oncol 2023; 13:1292268. [PMID: 38130995 PMCID: PMC10733447 DOI: 10.3389/fonc.2023.1292268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 11/20/2023] [Indexed: 12/23/2023] Open
Abstract
Background Economic evaluations have become an accepted methodology for decision makers to allocate resources in healthcare systems. Particularly in screening, where short-term costs are associated with long-term benefits, and adverse effects of screening intermingle, cost-effectiveness analyses provide a means to estimate the economic value of screening. Purpose To introduce the methodology of economic evaluations and to review the existing evidence on cost-effectiveness of MR-based breast cancer screening. Materials and methods The various concepts and techniques of economic evaluations critical to the interpretation of cost-effectiveness analyses are briefly introduced. In a systematic review of the literature, economic evaluations from the years 2000-2022 are reviewed. Results Despite a considerable heterogeneity in the reported input variables, outcome categories and methodological approaches, cost-effectiveness analyses report favorably on the economic value of breast MRI screening for different risk groups, including both short- and long-term costs and outcomes. Conclusion Economic evaluations indicate a strongly favorable economic value of breast MRI screening for women at high risk and for women with dense breast tissue.
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Affiliation(s)
- Fabian Tollens
- Department of Radiology and Nuclear Medicine, University Medical Centre Mannheim, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Pascal A.T. Baltzer
- Department of Biomedical Imaging and Image-Guided Therapy, Vienna General Hospital, Medical University of Vienna, Vienna, Austria
| | - Matthias F. Froelich
- Department of Radiology and Nuclear Medicine, University Medical Centre Mannheim, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Clemens G. Kaiser
- Department of Radiology and Nuclear Medicine, University Medical Centre Mannheim, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
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31
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Coffey K, Mango V. Revisiting Screening in Women With a Family History of Breast Cancer. JOURNAL OF BREAST IMAGING 2023; 5:635-645. [PMID: 38141237 DOI: 10.1093/jbi/wbad069] [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: 05/31/2023] [Indexed: 12/25/2023]
Abstract
Women with a family history (FH) of breast cancer and without known genetic susceptibility represent a unique population whose lifetime probability of developing breast cancer varies widely depending on familial factors, breast density, and the risk assessment tool used. Recently updated guidelines from the American College of Radiology recommend supplemental annual screening with contrast-enhanced MRI or contrast-enhanced mammography for women with an FH who are high risk (≥20% lifetime risk) or have dense breasts. To date, most screening studies addressing outcomes in women with FH have largely included those also with confirmed or suspected gene mutations, in whom the lifetime risk is highest, with limited data for women at average to intermediate risk who are not known to be genetically susceptible and may not benefit as much from the same screening approaches. Further research focusing specifically on women with FH as the only breast cancer risk factor is warranted to refine risk assessment and optimize a multimodality personalized screening approach.
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Affiliation(s)
- Kristen Coffey
- Memorial Sloan Kettering Cancer Center, Department of Radiology, New York, NY, USA
| | - Victoria Mango
- Memorial Sloan Kettering Cancer Center, Department of Radiology, New York, NY, USA
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Berg WA, Seitzman RL, Pushkin J. Implementing the National Dense Breast Reporting Standard, Expanding Supplemental Screening Using Current Guidelines, and the Proposed Find It Early Act. JOURNAL OF BREAST IMAGING 2023; 5:712-723. [PMID: 38141231 DOI: 10.1093/jbi/wbad034] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Indexed: 12/25/2023]
Abstract
Thirty-eight states and the District of Columbia (DC) have dense breast notification laws that mandate varying levels of patient notification about breast density after a mammogram, and these cover over 90% of American women. On March 10, 2023, the Food and Drug Administration issued a final rule amending regulations under the Mammography Quality Standards Act for a national dense breast reporting standard for both patient results letters and mammogram reports. Effective September 10, 2024, letters will be required to tell a woman her breasts are "dense" or "not dense," that dense tissue makes it harder to find cancers on a mammogram, and that it increases the risk of developing cancer. Women with dense breasts will also be told that other imaging tests in addition to a mammogram may help find cancers. The specific density category can be added (eg, if mandated by a state "inform" law). Reports to providers must include the Breast Imaging Reporting and Data System density category. Implementing appropriate supplemental screening should be based on patient risk for missed breast cancer on mammography; such assessment should include consideration of breast density and other risk factors. This article discusses strategies for implementation. Currently 21 states and DC have varying insurance laws for supplemental breast imaging; in addition, Oklahoma requires coverage for diagnostic breast imaging. A federal insurance bill, the Find It Early Act, has been introduced that would ensure no-cost screening and diagnostic imaging for women with dense breasts or at increased risk and close loopholes in state laws.
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Affiliation(s)
- Wendie A Berg
- University of Pittsburgh School of Medicine, Magee-Womens Hospital of UPMC, Department of Radiology, Pittsburgh, PA, USA
| | - Robin L Seitzman
- Seitzman Epidemiology, LLC, San Diego, CA, USA
- DenseBreast-info, Inc, Deer Park, NY, USA
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McCarthy AM, Fernandez Perez C, Beidas RS, Bekelman JE, Blumenthal D, Mack E, Bauer AM, Ehsan S, Conant EF, Wheeler BC, Guerra CE, Nunes LW, Gabriel P, Doucette A, Wileyto EP, Buttenheim AM, Asch DA, Rendle KA, Shelton RC, Fayanju OM, Ware S, Plag M, Hyland S, Gionta T, Shulman LN, Schnoll R. Protocol for a pragmatic stepped wedge cluster randomized clinical trial testing behavioral economic implementation strategies to increase supplemental breast MRI screening among patients with extremely dense breasts. Implement Sci 2023; 18:65. [PMID: 38001506 PMCID: PMC10668465 DOI: 10.1186/s13012-023-01323-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: 09/19/2023] [Accepted: 11/14/2023] [Indexed: 11/26/2023] Open
Abstract
BACKGROUND Increased breast density augments breast cancer risk and reduces mammography sensitivity. Supplemental breast MRI screening can significantly increase cancer detection among women with dense breasts. However, few women undergo this exam, and screening is consistently lower among racially minoritized populations. Implementation strategies informed by behavioral economics ("nudges") can promote evidence-based practices by improving clinician decision-making under conditions of uncertainty. Nudges directed toward clinicians and patients may facilitate the implementation of supplemental breast MRI. METHODS Approximately 1600 patients identified as having extremely dense breasts after non-actionable mammograms, along with about 1100 clinicians involved with their care at 32 primary care or OB/GYN clinics across a racially diverse academically based health system, will be enrolled. A 2 × 2 randomized pragmatic trial will test nudges to patients, clinicians, both, or neither to promote supplemental breast MRI screening. Before implementation, rapid cycle approaches informed by clinician and patient experiences and behavioral economics and health equity frameworks guided nudge design. Clinicians will be clustered into clinic groups based on existing administrative departments and care patterns, and these clinic groups will be randomized to have the nudge activated at different times per a stepped wedge design. Clinicians will receive nudges integrated into the routine mammographic report or sent through electronic health record (EHR) in-basket messaging once their clinic group (i.e., wedge) is randomized to receive the intervention. Independently, patients will be randomized to receive text message nudges or not. The primary outcome will be defined as ordering or scheduling supplemental breast MRI. Secondary outcomes include MRI completion, cancer detection rates, and false-positive rates. Patient sociodemographic information and clinic-level variables will be examined as moderators of nudge effectiveness. Qualitative interviews conducted at the trial's conclusion will examine barriers and facilitators to implementation. DISCUSSION This study will add to the growing literature on the effectiveness of behavioral economics-informed implementation strategies to promote evidence-based interventions. The design will facilitate testing the relative effects of nudges to patients and clinicians and the effects of moderators of nudge effectiveness, including key indicators of health disparities. The results may inform the introduction of low-cost, scalable implementation strategies to promote early breast cancer detection. TRIAL REGISTRATION ClinicalTrials.gov NCT05787249. Registered on March 28, 2023.
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Affiliation(s)
- Anne Marie McCarthy
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Penn Center for Cancer Care Innovation, Abramson Cancer Center, Penn Medicine, Philadelphia, PA, USA.
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Blockley Hall, 423 Guardian Drive, Philadelphia, PA, 19104, USA.
| | | | - Rinad S Beidas
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Justin E Bekelman
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Center for Cancer Care Innovation, Abramson Cancer Center, Penn Medicine, Philadelphia, PA, USA
| | - Daniel Blumenthal
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Interdisciplinary Research On Nicotine Addiction, University of Pennsylvania, Philadelphia, PA, USA
| | - Elizabeth Mack
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Anna-Marika Bauer
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Interdisciplinary Research On Nicotine Addiction, University of Pennsylvania, Philadelphia, PA, USA
| | - Sarah Ehsan
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Emily F Conant
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Carmen E Guerra
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Center for Cancer Care Innovation, Abramson Cancer Center, Penn Medicine, Philadelphia, PA, USA
| | - Linda W Nunes
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Peter Gabriel
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Center for Cancer Care Innovation, Abramson Cancer Center, Penn Medicine, Philadelphia, PA, USA
| | - Abigail Doucette
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - E Paul Wileyto
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Blockley Hall, 423 Guardian Drive, Philadelphia, PA, 19104, USA
- Center for Interdisciplinary Research On Nicotine Addiction, University of Pennsylvania, Philadelphia, PA, USA
| | - Alison M Buttenheim
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Center for Cancer Care Innovation, Abramson Cancer Center, Penn Medicine, Philadelphia, PA, USA
- School of Nursing, University of Pennsylvania, Philadelphia, PA, USA
| | - David A Asch
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Center for Cancer Care Innovation, Abramson Cancer Center, Penn Medicine, Philadelphia, PA, USA
| | - Katharine A Rendle
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Center for Cancer Care Innovation, Abramson Cancer Center, Penn Medicine, Philadelphia, PA, USA
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Blockley Hall, 423 Guardian Drive, Philadelphia, PA, 19104, USA
| | - Rachel C Shelton
- Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Oluwadamilola M Fayanju
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Center for Cancer Care Innovation, Abramson Cancer Center, Penn Medicine, Philadelphia, PA, USA
| | - Sue Ware
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Interdisciplinary Research On Nicotine Addiction, University of Pennsylvania, Philadelphia, PA, USA
| | - Martina Plag
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Steven Hyland
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Tracy Gionta
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Lawrence N Shulman
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Center for Cancer Care Innovation, Abramson Cancer Center, Penn Medicine, Philadelphia, PA, USA
| | - Robert Schnoll
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Center for Cancer Care Innovation, Abramson Cancer Center, Penn Medicine, Philadelphia, PA, USA
- Center for Interdisciplinary Research On Nicotine Addiction, University of Pennsylvania, Philadelphia, PA, USA
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Phillips J, Mehta TS, Portnow LH, Fishman MDC, Zhang Z, Pisano ED. Comparison of Contrast-enhanced Mammography with MRI Utilizing an Enriched Reader Study: A Breast Cancer Study (CONTRRAST Trial). Radiology 2023; 309:e230530. [PMID: 37962503 DOI: 10.1148/radiol.230530] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Background Despite growing interest in using contrast-enhanced mammography (CEM) for breast cancer screening as an alternative to breast MRI, limited literature is available. Purpose To determine whether CEM is noninferior to breast MRI or abbreviated breast MRI (AB MRI) and superior to two-dimensional mammography in an asymptomatic population simulating those who would present for screening and then undergo diagnostic work-up. Materials and Methods This enriched reader study used CEM and MRI data prospectively collected from asymptomatic individuals at a single institution from December 2014 to March 2020. Case sets were obtained at screening, as part of work-up for a screening-detected finding, or before biopsy of a screening-detected abnormality. All images were anonymized and randomized, and all 12 radiologists interpreted them. For CEM interpretation, readers were first shown low-energy images as a surrogate for digital mammography and asked to give a forced Breast Imaging Reporting and Data System score for up to three abnormalities. The highest score was used as the case score. Readers then reviewed the full CEM examination and scored it similarly. After a minimum 1-month washout, the readers similarly interpreted AB MRI and full MRI examinations. Receiver operating characteristic analysis, powered to test CEM noninferiority to full MRI, was performed. Results The study included 132 case sets (14 negative, 74 benign, and 44 malignant; all female participants; mean age, 54 years ± 12 [SD]). The mean areas under the receiver operating characteristic curve (AUCs) for digital mammography, CEM, AB MRI, and full MRI were 0.79, 0.91, 0.89, and 0.91, respectively. CEM was superior to digital mammography (P < .001). No evidence of a difference in AUC was found between CEM and AB MRI and MRI. Conclusion In an asymptomatic study sample, CEM was noninferior to full MRI and AB MRI and was superior to digital mammography. Clinical trial registration no. NCT03482557 and NCT02275871 © RSNA, 2023 Supplemental material is available for this article.
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Affiliation(s)
- Jordana Phillips
- From the Department of Radiology, Beth Israel Deaconess Medical Center, 330 Brookline Ave, TCC 4th Floor, Boston, MA 02215 (J.P.); Department of Radiology, UMass Memorial Medical Center, Worcester, Mass (T.S.M.); Department of Radiology, Brigham and Women's Hospital, Boston, Mass (L.H.P.); Department of Radiology, Boston University Medical Center, Boston, Mass (J.P., M.D.C.F.); Takeda Pharmaceuticals, Cambridge, Mass (Z.Z.); and Department of Radiology, Penn Medicine, Philadelphia, Pa (E.D.P.)
| | - Tejas S Mehta
- From the Department of Radiology, Beth Israel Deaconess Medical Center, 330 Brookline Ave, TCC 4th Floor, Boston, MA 02215 (J.P.); Department of Radiology, UMass Memorial Medical Center, Worcester, Mass (T.S.M.); Department of Radiology, Brigham and Women's Hospital, Boston, Mass (L.H.P.); Department of Radiology, Boston University Medical Center, Boston, Mass (J.P., M.D.C.F.); Takeda Pharmaceuticals, Cambridge, Mass (Z.Z.); and Department of Radiology, Penn Medicine, Philadelphia, Pa (E.D.P.)
| | - Leah H Portnow
- From the Department of Radiology, Beth Israel Deaconess Medical Center, 330 Brookline Ave, TCC 4th Floor, Boston, MA 02215 (J.P.); Department of Radiology, UMass Memorial Medical Center, Worcester, Mass (T.S.M.); Department of Radiology, Brigham and Women's Hospital, Boston, Mass (L.H.P.); Department of Radiology, Boston University Medical Center, Boston, Mass (J.P., M.D.C.F.); Takeda Pharmaceuticals, Cambridge, Mass (Z.Z.); and Department of Radiology, Penn Medicine, Philadelphia, Pa (E.D.P.)
| | - Michael D C Fishman
- From the Department of Radiology, Beth Israel Deaconess Medical Center, 330 Brookline Ave, TCC 4th Floor, Boston, MA 02215 (J.P.); Department of Radiology, UMass Memorial Medical Center, Worcester, Mass (T.S.M.); Department of Radiology, Brigham and Women's Hospital, Boston, Mass (L.H.P.); Department of Radiology, Boston University Medical Center, Boston, Mass (J.P., M.D.C.F.); Takeda Pharmaceuticals, Cambridge, Mass (Z.Z.); and Department of Radiology, Penn Medicine, Philadelphia, Pa (E.D.P.)
| | - Zheng Zhang
- From the Department of Radiology, Beth Israel Deaconess Medical Center, 330 Brookline Ave, TCC 4th Floor, Boston, MA 02215 (J.P.); Department of Radiology, UMass Memorial Medical Center, Worcester, Mass (T.S.M.); Department of Radiology, Brigham and Women's Hospital, Boston, Mass (L.H.P.); Department of Radiology, Boston University Medical Center, Boston, Mass (J.P., M.D.C.F.); Takeda Pharmaceuticals, Cambridge, Mass (Z.Z.); and Department of Radiology, Penn Medicine, Philadelphia, Pa (E.D.P.)
| | - Etta D Pisano
- From the Department of Radiology, Beth Israel Deaconess Medical Center, 330 Brookline Ave, TCC 4th Floor, Boston, MA 02215 (J.P.); Department of Radiology, UMass Memorial Medical Center, Worcester, Mass (T.S.M.); Department of Radiology, Brigham and Women's Hospital, Boston, Mass (L.H.P.); Department of Radiology, Boston University Medical Center, Boston, Mass (J.P., M.D.C.F.); Takeda Pharmaceuticals, Cambridge, Mass (Z.Z.); and Department of Radiology, Penn Medicine, Philadelphia, Pa (E.D.P.)
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Tsarouchi MI, Hoxhaj A, Mann RM. New Approaches and Recommendations for Risk-Adapted Breast Cancer Screening. J Magn Reson Imaging 2023; 58:987-1010. [PMID: 37040474 DOI: 10.1002/jmri.28731] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 03/23/2023] [Accepted: 03/24/2023] [Indexed: 04/13/2023] Open
Abstract
Population-based breast cancer screening using mammography as the gold standard imaging modality has been in clinical practice for over 40 years. However, the limitations of mammography in terms of sensitivity and high false-positive rates, particularly in high-risk women, challenge the indiscriminate nature of population-based screening. Additionally, in light of expanding research on new breast cancer risk factors, there is a growing consensus that breast cancer screening should move toward a risk-adapted approach. Recent advancements in breast imaging technology, including contrast material-enhanced mammography (CEM), ultrasound (US) (automated-breast US, Doppler, elastography US), and especially magnetic resonance imaging (MRI) (abbreviated, ultrafast, and contrast-agent free), may provide new opportunities for risk-adapted personalized screening strategies. Moreover, the integration of artificial intelligence and radiomics techniques has the potential to enhance the performance of risk-adapted screening. This review article summarizes the current evidence and challenges in breast cancer screening and highlights potential future perspectives for various imaging techniques in a risk-adapted breast cancer screening approach. EVIDENCE LEVEL: 1. TECHNICAL EFFICACY: Stage 5.
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Affiliation(s)
- Marialena I Tsarouchi
- Department of Radiology, Nuclear Medicine and Anatomy, Radboud University Medical Center, Nijmegen, the Netherlands
- Department of Radiology, the Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Alma Hoxhaj
- Department of Radiology, Nuclear Medicine and Anatomy, Radboud University Medical Center, Nijmegen, the Netherlands
- Department of Radiology, the Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Ritse M Mann
- Department of Radiology, Nuclear Medicine and Anatomy, Radboud University Medical Center, Nijmegen, the Netherlands
- Department of Radiology, the Netherlands Cancer Institute, Amsterdam, the Netherlands
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Reig B, Kim E, Chhor CM, Moy L, Lewin AA, Heacock L. Problem-solving Breast MRI. Radiographics 2023; 43:e230026. [PMID: 37733618 DOI: 10.1148/rg.230026] [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: 09/23/2023]
Abstract
Breast MRI has high sensitivity and negative predictive value, making it well suited to problem solving when other imaging modalities or physical examinations yield results that are inconclusive for the presence of breast cancer. Indications for problem-solving MRI include equivocal or uncertain imaging findings at mammography and/or US; suspicious nipple discharge or skin changes suspected to represent an abnormality when conventional imaging results are negative for cancer; lesions categorized as Breast Imaging Reporting and Data System 4, which are not amenable to biopsy; and discordant radiologic-pathologic findings after biopsy. MRI should not precede or replace careful diagnostic workup with mammography and US and should not be used when a biopsy can be safely performed. The role of MRI in characterizing calcifications is controversial, and management of calcifications should depend on their mammographic appearance because ductal carcinoma in situ may not appear enhancing on MR images. In addition, ductal carcinoma in situ detected solely with MRI is not associated with a higher likelihood of an upgrade to invasive cancer compared with ductal carcinoma in situ detected with other modalities. MRI for triage of high-risk lesions is a subject of ongoing investigation, with a possible future role for MRI in decreasing excisional biopsies. The accuracy of MRI is likely to increase with the use of advanced techniques such as deep learning, which will likely expand the indications for problem-solving MRI. ©RSNA, 2023 Quiz questions for this article are available in the supplemental material.
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Affiliation(s)
- Beatriu Reig
- From the Department of Radiology, NYU Langone Health, 660 1st Ave, New York, NY 10016
| | - Eric Kim
- From the Department of Radiology, NYU Langone Health, 660 1st Ave, New York, NY 10016
| | - Chloe M Chhor
- From the Department of Radiology, NYU Langone Health, 660 1st Ave, New York, NY 10016
| | - Linda Moy
- From the Department of Radiology, NYU Langone Health, 660 1st Ave, New York, NY 10016
| | - Alana A Lewin
- From the Department of Radiology, NYU Langone Health, 660 1st Ave, New York, NY 10016
| | - Laura Heacock
- From the Department of Radiology, NYU Langone Health, 660 1st Ave, New York, NY 10016
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Brown AL, Vijapura C, Patel M, De La Cruz A, Wahab R. Breast Cancer in Dense Breasts: Detection Challenges and Supplemental Screening Opportunities. Radiographics 2023; 43:e230024. [PMID: 37792590 DOI: 10.1148/rg.230024] [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: 10/06/2023]
Abstract
Dense breast tissue at mammography is associated with higher breast cancer incidence and mortality rates, which have prompted new considerations for breast cancer screening in women with dense breasts. The authors review the definition and classification of breast density, density assessment methods, breast cancer risk, current legislation, and future efforts and summarize trials and key studies that have affected the existing guidelines for supplemental screening. Cases of breast cancer in dense breasts are presented, highlighting a variety of modalities and specific imaging findings that can aid in cancer detection and staging. Understanding the current state of breast cancer screening in patients with dense breasts and its challenges is important to shape future considerations for care. Shifting the paradigm of breast cancer detection toward early diagnosis for women with dense breasts may be the answer to reducing the number of deaths from this common disease. ©RSNA, 2023 Online supplemental material is available for this article. Quiz questions for this article are available through the Online Learning Center. See the invited commentary by Yeh in this issue.
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Affiliation(s)
- Ann L Brown
- From the Department of Radiology, University of Cincinnati Medical Center, 3188 Bellevue Ave, Cincinnati, OH 45219-0772 (A.L.B., C.V., A.D.L.C., R.W.); and Department of Radiology, Ohio State University Medical Center, Columbus, Ohio (M.P.)
| | - Charmi Vijapura
- From the Department of Radiology, University of Cincinnati Medical Center, 3188 Bellevue Ave, Cincinnati, OH 45219-0772 (A.L.B., C.V., A.D.L.C., R.W.); and Department of Radiology, Ohio State University Medical Center, Columbus, Ohio (M.P.)
| | - Mitva Patel
- From the Department of Radiology, University of Cincinnati Medical Center, 3188 Bellevue Ave, Cincinnati, OH 45219-0772 (A.L.B., C.V., A.D.L.C., R.W.); and Department of Radiology, Ohio State University Medical Center, Columbus, Ohio (M.P.)
| | - Alexis De La Cruz
- From the Department of Radiology, University of Cincinnati Medical Center, 3188 Bellevue Ave, Cincinnati, OH 45219-0772 (A.L.B., C.V., A.D.L.C., R.W.); and Department of Radiology, Ohio State University Medical Center, Columbus, Ohio (M.P.)
| | - Rifat Wahab
- From the Department of Radiology, University of Cincinnati Medical Center, 3188 Bellevue Ave, Cincinnati, OH 45219-0772 (A.L.B., C.V., A.D.L.C., R.W.); and Department of Radiology, Ohio State University Medical Center, Columbus, Ohio (M.P.)
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Moran CJ, Middione MJ, Mazzoli V, McKay-Nault JA, Guidon A, Waheed U, Rosen EL, Poplack SP, Rosenberg J, Ennis DB, Hargreaves BA, Daniel BL. Multishot Diffusion-Weighted MRI of the Breasts in the Supine vs. Prone Position. J Magn Reson Imaging 2023; 58:951-962. [PMID: 36583628 PMCID: PMC10310889 DOI: 10.1002/jmri.28582] [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: 12/30/2021] [Revised: 12/12/2022] [Accepted: 12/12/2022] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Diffusion-weighted imaging (DWI) may allow for breast cancer screening MRI without a contrast injection. Multishot methods improve prone DWI of the breasts but face different challenges in the supine position. PURPOSE To establish a multishot DWI (msDWI) protocol for supine breast MRI and to evaluate the performance of supine vs. prone msDWI. STUDY TYPE Prospective. POPULATION Protocol optimization: 10 healthy women (ages 22-56), supine vs. prone: 24 healthy women (ages 22-62) and five women (ages 29-61) with breast tumors. FIELD STRENGTH/SEQUENCE 3-T, protocol optimization msDWI: free-breathing (FB) 2-shots, FB 4-shots, respiratory-triggered (RT) 2-shots, RT 4-shots, supine vs. prone: RT 4-shot msDWI, T2-weighted fast-spin echo. ASSESSMENT Protocol optimization and supine vs. prone: three observers performed an image quality assessment of sharpness, aliasing, distortion (vs. T2), perceived SNR, and overall image quality (scale of 1-5). Apparent diffusion coefficients (ADCs) in fibroglandular tissue (FGT) and breast tumors were measured. STATISTICAL TESTS Effect of study variables on dichotomized ratings (4/5 vs. 1/2/3) and FGT ADCs were assessed with mixed-effects logistic regression. Interobserver agreement utilized Gwet's agreement coefficient (AC). Lesion ADCs were assessed by Bland-Altman analysis and concordance correlation (ρc ). P value <0.05 was considered statistically significant. RESULTS Protocol optimization: 4-shots significantly improved sharpness and distortion; RT significantly improved sharpness, aliasing, perceived SNR, and overall image quality. FGT ADCs were not significantly different between shots (P = 0.812), FB vs. RT (P = 0.591), or side (P = 0.574). Supine vs. prone: supine images were rated significantly higher for sharpness, aliasing, and overall image quality. FGT ADCs were significantly higher supine; lesion ADCs were highly correlated (ρc = 0.92). DATA CONCLUSION Based on image quality, supine msDWI outperformed prone msDWI. Lesion ADCs were highly correlated between the two positions, while FGT ADCs were higher in the supine position. EVIDENCE LEVEL 2. TECHNICAL EFFICACY Stage 1.
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Affiliation(s)
| | | | - Valentina Mazzoli
- Department of Radiology, Stanford University, Stanford, California, USA
| | | | - Arnaud Guidon
- Global MR Application and Workflow, GE Healthcare, Boston, Massachusetts, USA
| | - Uzma Waheed
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Eric L. Rosen
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Steven P. Poplack
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Jarrett Rosenberg
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Daniel B. Ennis
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Brian A. Hargreaves
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Bruce L. Daniel
- Department of Radiology, Stanford University, Stanford, California, USA
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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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Endrikat J, Khater H, Boreham ADP, Fritze S, Schwenke C, Bhatti A, Trnkova ZJ, Seidensticker P. Iopromide for Contrast-Enhanced Mammography: A Systemic Review and Meta-Analysis of Pertinent Literature. Breast Cancer (Auckl) 2023; 17:11782234231189467. [PMID: 37600467 PMCID: PMC10433886 DOI: 10.1177/11782234231189467] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 07/06/2023] [Indexed: 08/22/2023] Open
Abstract
Background Contrast-enhanced mammography (CEM) is an emerging breast imaging modality. Clinical data is scarce. Objectives To summarize clinical evidence on the use of iopromide in CEM for the detection or by systematically analyzing the available literature on efficacy and safety. Design Systematic review and meta-analysis. Data sources and methods Iopromide-specific publications reporting its use in CEM were identified by a systematic search within Bayer's Product Literature Information (PLI) database and by levering a recent review publication. The literature search in PLI was performed up to January 2023. The confirmatory-supporting review publication was based on a MEDLINE/EMBASE + full text search for publications issued between September 2003 and January 2019. Relevant literature was selected based on pre-defined criteria by 2 reviewers. The comparison of CEM vs traditional mammography (XRM) was performed on published results of sensitivity and specificity. Differences in diagnostic parameters were assessed within a meta-analysis. Results Literature search: A total of 31 studies were identified reporting data on 5194 patients. Thereof, 19 studies on efficacy and 3 studies on safety. Efficacy: in 11 studies comparing iopromide CEM vs XRM, sensitivity was up to 43% higher (range 1%-43%) for CEM. Differences in specificity were found to be in a range of -4% to 46% for CEM compared with XRM. The overall gain in sensitivity for CEM vs XRM was 7% (95% CI [4%, 11%]) with no statistically significant loss in specificity in any study assessed. In most studies, accuracy, positive predictive value, and negative predictive value were found to be in favor of CEM. In 2 studies comparing CEM with breast magnetic resonance imaging (bMRI), both imaging modalities performed either equally well or CEM tended to show better results with respect to sensitivity and specificity. Safety: eight cases of iopromide-related adverse drug reactions were reported in 1022 patients (0.8%). Conclusions Pertinent literature provides evidence for clinical utility of iopromide in CEM for the detection or confirmation of breast cancer. The overall gain in sensitivity for iopromide CEM vs XRM was 7% with no statistically significant loss in specificity.
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Affiliation(s)
- Jan Endrikat
- Radiology R&D, Bayer AG, Berlin, Germany
- Department of Gynecology, Obstetrics and Reproductive Medicine, University Medical School of Saarland, Homburg, Germany
| | | | | | - Sabine Fritze
- Medical Affairs & Pharmacovigilance, Pharmaceuticals, Product Information, Bayer AG, Berlin, Germany
| | | | - Aasia Bhatti
- Benefit Risk Management Pharmacovigilance, Bayer US LLC, Whippany, NJ, USA
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41
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Berg WA, Seitzman RL, Pushkin J. Opinion: USPSTF Guideline Fails to Address Dense Breasts. JOURNAL OF BREAST IMAGING 2023; 5:393-395. [PMID: 38416902 DOI: 10.1093/jbi/wbad043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Indexed: 03/01/2024]
Affiliation(s)
- Wendie A Berg
- University of Pittsburgh School of Medicine, Magee-Womens Hospital of UPMC, Department of Radiology, Pittsburgh, PA, USA
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42
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Ecanow JS, Ecanow DB, Hack B, Leloudas N, Prasad PV. Feasibility of Diffusion Tensor Imaging for Decreasing Biopsy Rates in Breast Imaging: Interim Analysis of a Prospective Study. Diagnostics (Basel) 2023; 13:2226. [PMID: 37443620 DOI: 10.3390/diagnostics13132226] [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: 05/22/2023] [Revised: 06/28/2023] [Accepted: 06/28/2023] [Indexed: 07/15/2023] Open
Abstract
Because of the limited specificity of diagnostic imaging, many breast lesions referred for biopsy turn out to be benign. The objective of this study was to evaluate whether diffusion tensor MRI (DTI) parametric maps can be used to safely avoid biopsy of breast lesions. Individuals referred for breast biopsy based on mammogram (MG), ultrasound (US), and/or contrast enhanced (CE)-MRI were recruited. Scans consisting of T2-weighted and DTI sequences were performed. Multiple DTI-derived parametric color maps were evaluated semi-quantitatively to characterize lesions as "definitely benign," "not definitely benign," or "suspicious." All patients subsequently underwent biopsy. In this moderately-sized prospective study, 21 out of 47 pathologically proven benign lesions were characterized by both readers as "definitely benign," which would have precluded the need for biopsy. Biopsy was recommended for 11 out of 13 cancers that were characterized as "suspicious." In the remaining two cancers and 26 of 47 benign lesions, the scans were characterized as "not definitely benign" and hence required biopsy. The main causes for "not definitely benign" scans were small lesion sizes and noise. The results suggest that in appropriately selected patients, DTI may be used to safely reduce the number of unnecessary breast biopsies.
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Affiliation(s)
- Jacob S Ecanow
- Department of Radiology, NorthShore University HealthSystem, Evanston, IL 60201, USA
| | - David B Ecanow
- Department of Radiology, NorthShore University HealthSystem, Evanston, IL 60201, USA
| | - Bradley Hack
- Department of Radiology, NorthShore University HealthSystem, Evanston, IL 60201, USA
| | - Nondas Leloudas
- Department of Radiology, NorthShore University HealthSystem, Evanston, IL 60201, USA
| | - Pottumarthi V Prasad
- Department of Radiology, NorthShore University HealthSystem, Evanston, IL 60201, USA
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43
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Marshall H, Burkard-Mandel L, Hsu J, Durieux J, Shikhman R, Plecha D. Abbreviated Breast MRI: Our Two-Year Initial Experience. JOURNAL OF BREAST IMAGING 2023; 5:318-328. [PMID: 38416894 DOI: 10.1093/jbi/wbad017] [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: 09/15/2022] [Indexed: 03/01/2024]
Abstract
OBJECTIVE The aim of this study was to retrospectively evaluate and present our two-year experience with abbreviated breast MRI at our academic institution as a screening tool to identify primary breast cancers. METHODS Employing eight specialty trained breast radiologists, studies were interpreted using the BI-RADS MRI lexicon in this IRB-approved retrospective study. The protocol utilized T1-weighted, fat-saturated, pre- and post-contrast, short T1 inversion recovery images, and was completed within 10 minutes. Abbreviated breast MRI was offered to asymptomatic women of all breast densities, whose ages ranged from 24 to 90 years. Statistical analysis was performed for comparative data utilizing estimated odds ratios. RESULTS Of 1338 patients that met inclusion criteria, 83% (1111/1338) were BI-RADS 1 or 2, 9.0% (121/1338) were BI-RADS 3, and 8% (106/1338) were categorized as either BI-RADS 4 or 5 with recommended biopsy. Biopsy of BI-RADS 4 and 5 categorized patients yielded 15 cancers for a positive predictive value (PPV) 2 of 14.2% and a PPV3 of 18.5%, with 76% (81/106) of patients undergoing the recommended biopsy. An additional cancer was detected in a BI-RADS 3 finding. All cancers detected were in women with heterogeneously dense or extremely dense breasts. Therefore, 16 cancers were detected, yielding a cancer detection rate of 12.0 per 1000. Over the next 12 to 24 months, no interval cancers were detected. CONCLUSION Abbreviated breast MRI demonstrates a higher cancer detection rate compared with mammography only and may provide a supplemental screening method to detect breast cancers in patients with varying risk factors.
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Affiliation(s)
- Holly Marshall
- University Hospitals Cleveland Medical Center, Department of Radiology, Cleveland, OH, USA
| | - Lauren Burkard-Mandel
- University Hospitals Cleveland Medical Center, Department of Radiology, Cleveland, OH, USA
| | - Jerry Hsu
- University Hospitals Cleveland Medical Center, Department of Radiology, Cleveland, OH, USA
| | - Jared Durieux
- University Hospitals Cleveland Medical Center, Department of Radiology, Cleveland, OH, USA
| | | | - Donna Plecha
- University Hospitals Cleveland Medical Center, Department of Radiology, Cleveland, OH, USA
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Judd D, Stucki B, Oldham J, Johnston D. Inflammatory Skin Change From a Small 0.9 cm Primary Breast Cancer Not Seen on Initial Imaging: A Case Report. Cureus 2023; 15:e39491. [PMID: 37362505 PMCID: PMC10290542 DOI: 10.7759/cureus.39491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/25/2023] [Indexed: 06/28/2023] Open
Abstract
Most inflammatory breast cancers are caused by invasive ductal cell carcinomas that arise from mammary epithelial cells lining the breast ducts. Typically, in these cancers, radiological signs are conspicuous, and a diagnosis is made after standard mammographic imaging or ultrasound. We report the case of a 54-year-old female who presented to a mammography clinic with right-sided breast pain and swelling. Upon physical examination, there was no palpable mass. Ultrasound and mammogram findings included mild skin thickening, normal-sized but irregularly shaped axillary lymph nodes, and no breast mass. Due to the presence of inflammatory changes (skin thickening) and abnormal lymph nodes but no obvious mass, an MRI was done to find the primary mass after a core needle biopsy of one lymph node showed metastatic ductal disease. In this patient, a 0.9 cm mass was found at the right 8:00 position on MRI. A second-look ultrasound was then performed and the mass was identified, followed by an ultrasound-guided core biopsy. The biopsy showed an invasive ductal cell carcinoma. In most cases, inflammatory breast cancer is associated with larger tumor sizes. However, a subset of patients with inflammatory breast cancer may present with a small primary breast tumor that causes inflammatory changes. Here, we present a rare case of inflammatory breast cancer associated with a small breast mass measuring less than 1 cm in size.
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Affiliation(s)
- Dallin Judd
- Texas College of Osteopathic Medicine, University of North Texas Health Science Center, Fort Worth, USA
| | - Brenton Stucki
- Texas College of Osteopathic Medicine, University of North Texas Health Science Center, Fort Worth, USA
| | - Jake Oldham
- Texas College of Osteopathic Medicine, University of North Texas Health Science Center, Fort Worth, USA
| | - David Johnston
- Radiology Partners, Rose Imaging Specialists, Fort Worth, USA
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Berg WA, Zuley ML, Chang TS, Gizienski TA, Chough DM, Böhm-Vélez M, Sharek DE, Straka MR, Hakim CM, Hartman JY, Harnist KS, Tyma CS, Kelly AE, Waheed U, Houshmand G, Nair BE, Shinde DD, Lu AH, Bandos AI, Berg JM, Lettiere NB, Ganott MA. Prospective Multicenter Diagnostic Performance of Technologist-Performed Screening Breast Ultrasound After Tomosynthesis in Women With Dense Breasts (the DBTUST). J Clin Oncol 2023; 41:2403-2415. [PMID: 36626696 PMCID: PMC10150890 DOI: 10.1200/jco.22.01445] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Revised: 10/25/2022] [Accepted: 11/19/2022] [Indexed: 01/11/2023] Open
Abstract
PURPOSE To assess diagnostic performance of digital breast tomosynthesis (DBT) alone or combined with technologist-performed handheld screening ultrasound (US) in women with dense breasts. METHODS In an institutional review board-approved, Health Insurance Portability and Accountability Act-compliant multicenter protocol in western Pennsylvania, 6,179 women consented to three rounds of annual screening, interpreted by two radiologist observers, and had appropriate follow-up. Primary analysis was based on first observer results. RESULTS Mean participant age was 54.8 years (range, 40-75 years). Across 17,552 screens, there were 126 cancer events in 125 women (7.2/1,000; 95% CI, 5.9 to 8.4). In year 1, DBT-alone cancer yield was 5.0/1,000, and of DBT+US, 6.3/1,000, difference 1.3/1,000 (95% CI, 0.3 to 2.1; P = .005). In years 2 + 3, DBT cancer yield was 4.9/1,000, and of DBT+US, 5.9/1,000, difference 1.0/1,000 (95% CI, 0.4 to 1.5; P < .001). False-positive rate increased from 7.0% for DBT in year 1 to 11.5% for DBT+US and from 5.9% for DBT in year 2 + 3 to 9.7% for DBT+US (P < .001 for both). Nine cancers were seen only by double reading DBT and one by double reading US. Ten interval cancers (0.6/1,000 [95% CI, 0.2 to 0.9]) were identified. Despite reduction in specificity, addition of US improved receiver operating characteristic curves, with area under receiver operating characteristic curve increasing from 0.83 for DBT alone to 0.92 for DBT+US in year 1 (P = .01), with smaller improvements in subsequent years. Of 6,179 women, across all 3 years, 172/6,179 (2.8%) unique women had a false-positive biopsy because of DBT as did another 230/6,179 (3.7%) women because of US (P < .001). CONCLUSION Overall added cancer detection rate of US screening after DBT was modest at 19/17,552 (1.1/1,000; CI, 0.5- to 1.6) screens but potentially overcomes substantial increases in false-positive recalls and benign biopsies.
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Affiliation(s)
- Wendie A. Berg
- Department of Radiology, University of Pittsburgh School of Medicine, Magee-Womens Hospital of UPMC, Pittsburgh, PA
| | - Margarita L. Zuley
- Department of Radiology, University of Pittsburgh School of Medicine, Magee-Womens Hospital of UPMC, Pittsburgh, PA
| | | | - Terri-Ann Gizienski
- Department of Radiology, University of Pittsburgh School of Medicine, Magee-Womens Hospital of UPMC, Pittsburgh, PA
| | - Denise M. Chough
- Department of Radiology, University of Pittsburgh School of Medicine, Magee-Womens Hospital of UPMC, Pittsburgh, PA
| | | | | | | | - Christiane M. Hakim
- Department of Radiology, University of Pittsburgh School of Medicine, Magee-Womens Hospital of UPMC, Pittsburgh, PA
| | - Jamie Y. Hartman
- Department of Radiology, University of Pittsburgh School of Medicine, Magee-Womens Hospital of UPMC, Pittsburgh, PA
| | - Kimberly S. Harnist
- Department of Radiology, University of Pittsburgh School of Medicine, Magee-Womens Hospital of UPMC, Pittsburgh, PA
| | - Cathy S. Tyma
- Department of Radiology, University of Pittsburgh School of Medicine, Magee-Womens Hospital of UPMC, Pittsburgh, PA
- Department of Radiology, New York University Grossman School of Medicine, New York, NY
| | - Amy E. Kelly
- Department of Radiology, University of Pittsburgh School of Medicine, Magee-Womens Hospital of UPMC, Pittsburgh, PA
| | - Uzma Waheed
- Department of Radiology, University of Pittsburgh School of Medicine, Magee-Womens Hospital of UPMC, Pittsburgh, PA
- Department of Radiology, Stanford University School of Medicine, Stanford, CA
| | - Golbahar Houshmand
- Department of Radiology, University of Pittsburgh School of Medicine, Magee-Womens Hospital of UPMC, Pittsburgh, PA
- Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Bronwyn E. Nair
- Department of Radiology, University of Pittsburgh School of Medicine, Magee-Womens Hospital of UPMC, Pittsburgh, PA
| | - Dilip D. Shinde
- Department of Radiology, University of Pittsburgh School of Medicine, Magee-Womens Hospital of UPMC, Pittsburgh, PA
| | - Amy H. Lu
- Department of Radiology, University of Pittsburgh School of Medicine, Magee-Womens Hospital of UPMC, Pittsburgh, PA
| | - Andriy I. Bandos
- Department of Biostatistics, University of Pittsburgh School of Public Health, Pittsburgh, PA
| | - Jeremy M. Berg
- Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA
| | - Nicole B. Lettiere
- Department of Radiology, University of Pittsburgh School of Medicine, Magee-Womens Hospital of UPMC, Pittsburgh, PA
- ICON-Amgen, Pittsburgh, PA
| | - Marie A. Ganott
- Department of Radiology, University of Pittsburgh School of Medicine, Magee-Womens Hospital of UPMC, Pittsburgh, PA
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Nicosia L, Bozzini AC, Pesapane F, Rotili A, Marinucci I, Signorelli G, Frassoni S, Bagnardi V, Origgi D, De Marco P, Abiuso I, Sangalli C, Balestreri N, Corso G, Cassano E. Breast Digital Tomosynthesis versus Contrast-Enhanced Mammography: Comparison of Diagnostic Application and Radiation Dose in a Screening Setting. Cancers (Basel) 2023; 15:cancers15092413. [PMID: 37173880 PMCID: PMC10177523 DOI: 10.3390/cancers15092413] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 04/15/2023] [Accepted: 04/20/2023] [Indexed: 05/15/2023] Open
Abstract
This study aims to evaluate the Average Glandular Dose (AGD) and diagnostic performance of CEM versus Digital Mammography (DM) as well as versus DM plus one-view Digital Breast Tomosynthesis (DBT), which were performed in the same patients at short intervals of time. A preventive screening examination in high-risk asymptomatic patients between 2020 and 2022 was performed with two-view Digital Mammography (DM) projections (Cranio Caudal and Medio Lateral) plus one Digital Breast Tomosynthesis (DBT) projection (mediolateral oblique, MLO) in a single session examination. For all patients in whom we found a suspicious lesion by using DM + DBT, we performed (within two weeks) a CEM examination. AGD and compression force were compared between the diagnostic methods. All lesions identified by DM + DBT were biopsied; then, we assessed whether lesions found by DBT were also highlighted by DM alone and/or by CEM. We enrolled 49 patients with 49 lesions in the study. The median AGD was lower for DM alone than for CEM (3.41 mGy vs. 4.24 mGy, p = 0.015). The AGD for CEM was significantly lower than for the DM plus one single projection DBT protocol (4.24 mGy vs. 5.55 mGy, p < 0.001). We did not find a statistically significant difference in the median compression force between the CEM and DM + DBT. DM + DBT allows the identification of one more invasive neoplasm one in situ lesion and two high-risk lesions, compared to DM alone. The CEM, compared to DM + DBT, failed to identify only one of the high-risk lesions. According to these results, CEM could be used in the screening of asymptomatic high-risk patients.
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Affiliation(s)
- Luca Nicosia
- Breast Imaging Division, Radiology Department, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Anna Carla Bozzini
- Breast Imaging Division, Radiology Department, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Filippo Pesapane
- Breast Imaging Division, Radiology Department, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Anna Rotili
- Breast Imaging Division, Radiology Department, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Irene Marinucci
- Breast Imaging Division, Radiology Department, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Giulia Signorelli
- Breast Imaging Division, Radiology Department, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Samuele Frassoni
- Department of Statistics and Quantitative Methods, University of Milan-Bicocca, 20126 Milan, Italy
| | - Vincenzo Bagnardi
- Department of Statistics and Quantitative Methods, University of Milan-Bicocca, 20126 Milan, Italy
| | - Daniela Origgi
- Medical Physics Unit, IEO European Institute of Oncology IRCCS, Via Ripamonti 435, 20141 Milan, Italy
| | - Paolo De Marco
- Medical Physics Unit, IEO European Institute of Oncology IRCCS, Via Ripamonti 435, 20141 Milan, Italy
| | - Ida Abiuso
- Radiology Department, Università Degli Studi di Torino, 10124 Turin, Italy
| | - Claudia Sangalli
- Data Management, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Nicola Balestreri
- Department of Radiology, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Giovanni Corso
- Division of Breast Surgery, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
- European Cancer Prevention Organization, 20122 Milan, Italy
| | - Enrico Cassano
- Breast Imaging Division, Radiology Department, IEO European Institute of Oncology IRCCS, 20141 Milan, Italy
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Alikhassi A, Li X, Au F, Kulkarni S, Ghai S, Allison G, Freitas V. False-positive incidental lesions detected on contrast-enhanced breast MRI: clinical and imaging features. Breast Cancer Res Treat 2023; 198:321-334. [PMID: 36740611 DOI: 10.1007/s10549-023-06861-y] [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: 04/27/2022] [Accepted: 01/08/2023] [Indexed: 02/07/2023]
Abstract
PURPOSE To identify demographic and imaging features of MRI-detected enhancing lesions without clinical, ultrasound, and mammographic correlation associated with false-positive outcomes, impacting patient care. MATERIALS AND METHODS A retrospective multi-institutional study of imaging studies and patient's chart review of consecutive women with MRI-detected enhancing lesions without clinical, mammogram, or ultrasound correlation between January and December 2018, who underwent MRI-guided biopsy. According to the BI-RADS lexicon, lesions' frequency and imaging features were recorded. The demographic and imaging characteristics variables were correlated with histopathology as the gold standard and an uneventful follow-up of at least one year. Univariate logistic regression analysis was used to explore the correlation between the baseline variables such as age, genetic mutation, family history of breast cancer, personal history of breast cancer, MRI indication, background parenchymal enhancement, and MRI characteristic of the lesion with the false-positive results in main data and subgroup analysis. RESULTS Two hundred nineteen women (median age 49 years; range 26-85 years) with 219 MRI-detected enhancing lesions that underwent MRI-guided vacuum-assisted biopsy during the study period fulfilled the study criteria and formed the study cohort. Out of 219, 180 lesions (82.2%) yielded benign pathology results, including 137 benign outcomes (76%) and 43 high-risk lesions (24%). Most demographic and imaging characteristics variables did not help to differentiate malignant from benign lesions. The variables that showed statistically significant association with true-positive results in univariate analyses were age (OR 1.05; 95% CI 1.02-1.08; p = 0.0015), irregular mass-lesion shape when compared with oval/round mass lesion (OR 11.2; 95% CI 1.6-78.4; p = 0.015), and clumped and clustered ring of enhancement when compared with homogeneous (OR 3.22, 95% CI 1.40-7.40; p = 0.0058). For participants with mass breast lesion, the hyperintense signal on the T2-weighted sequence (compared to the normal fibroglandular signal) was significantly related to the false-positive result (OR 0.13; 95% CI 0.02-0.76; p = 0.024). CONCLUSION Young patients, oval/round mass-lesion shape, and homogeneous pattern of non-mass enhancement showed the strongest association with false-positive results of enhancing lesions depicted by MRI. For participants with mass breast lesion, T2-bright mass lesion showed significant association with false-positive result. It may impact the patient's management with a suggestion of follow-up rather than interventional procedure when these demographic and imaging parameters are present, consequently decreasing the patient's anxiety and health care costs.
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Affiliation(s)
- Afsaneh Alikhassi
- Division of Breast Imaging, Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, 2075 Bayview Ave, Toronto, ON, M4N 3M5, Canada
| | - Xuan Li
- Department of Biostatistics-Princess Margaret Cancer Centre, University Health Network, 610 University Avenue, 10Th Floor, Room 10-509, Toronto, ON, M5G 2M9, Canada
| | - Frederick Au
- Joint Department of Medical Imaging-University Health Network, Sinai Health System, Women's College Hospital, University of Toronto, 610 University Avenue, Toronto, ON, M5G 2M9, Canada
| | - Supriya Kulkarni
- Joint Department of Medical Imaging-University Health Network, Sinai Health System, Women's College Hospital, University of Toronto, 610 University Avenue, Toronto, ON, M5G 2M9, Canada
| | - Sandeep Ghai
- Joint Department of Medical Imaging-University Health Network, Sinai Health System, Women's College Hospital, University of Toronto, 610 University Avenue, Toronto, ON, M5G 2M9, Canada
| | - Grant Allison
- Joint Department of Medical Imaging-University Health Network, Sinai Health System, Women's College Hospital, University of Toronto, 610 University Avenue, Toronto, ON, M5G 2M9, Canada
| | - Vivianne Freitas
- Joint Department of Medical Imaging-University Health Network, Sinai Health System, Women's College Hospital, University of Toronto, 610 University Avenue, Toronto, ON, M5G 2M9, Canada.
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48
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Elder K, Matheson J, Nickson C, Box G, Ellis J, Mou A, Shadbolt C, Park A, Tay J, Rose A, Mann GB. Contrast enhanced mammography in breast cancer surveillance. Breast Cancer Res Treat 2023; 199:221-230. [PMID: 36966271 PMCID: PMC10175447 DOI: 10.1007/s10549-023-06916-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 03/13/2023] [Indexed: 03/27/2023]
Abstract
PURPOSE Mammography (MG) is the standard imaging in surveillance of women with a personal history of breast cancer or DCIS (PHBC), supplemented with ultrasound. Contrast Enhanced Mammography (CEM) has higher sensitivity than MG and US. We report the performance of CEM compared with MG ± US. METHODS A retrospective study of patients undergoing their first surveillance CEM in an Australian hospital setting between June 2006 and October 2020. Cases where a patient was recalled for assessment were identified, recording radiology, pathology and treatment details. Blinded re-reading of recalled cases was performed to determine the contribution of contrast. Use of surveillance US across the board was assessed for the period. RESULTS 73/1191 (6.1%) patients were recalled. 35 (48%) were true positives (TP), with 26 invasive cancers and 9 cases of DCIS, while 38 (52%) were false positive (FP) with a positive predictive value (PPV) 47.9%. 32/73 were recalled due to MG findings, while 41/73 were only recalled due to Contrast. 14/73 had 'minimal signs' with a lesion identifiable on MG with knowledge of the contrast finding, while 27/73 were visible only with contrast. 41% (17/41) recalled due to contrast were TP. Contrast-only TPs were found with low and high mammographic density (MD). Screening breast US reduced by 55% in the year after CEM was implemented. CONCLUSION Compared to MG, CEM as a single surveillance modality for those with PHBC has higher sensitivity and comparable specificity, identifying additional malignant lesions that are clinically significant. Investigation of interval cancer and subsequent round outcomes is warranted.
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Affiliation(s)
- Kenneth Elder
- The Royal Melbourne Hospital, Grattan Street, Parkville, Melbourne, 3101, Australia.
| | - Julia Matheson
- The Royal Melbourne Hospital, Grattan Street, Parkville, Melbourne, 3101, 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
| | - Georgia Box
- The Royal Melbourne Hospital, Grattan Street, Parkville, Melbourne, 3101, Australia
| | - Jennifer Ellis
- The Royal Melbourne Hospital, Grattan Street, Parkville, Melbourne, 3101, Australia
| | - Arlene Mou
- The Royal Women's Hospital, Flemington Road, Parkville, Melbourne, Australia
| | - Clair Shadbolt
- The Royal Women's Hospital, Flemington Road, Parkville, Melbourne, Australia
| | - Allan Park
- The Royal Women's Hospital, Flemington Road, Parkville, Melbourne, Australia
| | - Jia Tay
- The Royal Melbourne Hospital, Grattan Street, Parkville, Melbourne, 3101, Australia
| | - Allison Rose
- The Royal Women's Hospital, Flemington Road, Parkville, Melbourne, Australia
| | - Gregory Bruce Mann
- The Royal Melbourne Hospital, Grattan Street, Parkville, Melbourne, 3101, Australia
- The Royal Women's Hospital, Flemington Road, Parkville, Melbourne, Australia
- Department of Surgery, The University of Melbourne, Parkville, Australia
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Pöschke P, Wenkel E, Hack CC, Beckmann MW, Uder M, Ohlmeyer S. Low-Risk Women with Suspicious Microcalcifications in Mammography-Can an Additional Breast MRI Reduce the Biopsy Rate? Diagnostics (Basel) 2023; 13:diagnostics13061197. [PMID: 36980504 PMCID: PMC10047574 DOI: 10.3390/diagnostics13061197] [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: 02/23/2023] [Revised: 03/14/2023] [Accepted: 03/20/2023] [Indexed: 03/30/2023] Open
Abstract
BACKGROUND In the German Mammography Screening Program, 62% of ductal carcinoma in situ (DCIS) and 38% of invasive breast cancers are associated with microcalcifications (MCs). Vacuum-assisted stereotactic breast biopsies are necessary to distinguish precancerous lesions from benign calcifications because mammographic discrimination is not possible. The aim of this study was to investigate if breast magnetic resonance imaging (MRM) could assist the evaluation of MCs and thus help reduce biopsy rates. METHODS In this IRB-approved study, 58 women (mean age 58 +/- 24 years) with 59 suspicious MC clusters in the MG were eligible for this prospective single-center trial. Additional breast magnetic resonance imaging (MRI) was conducted before biopsy. RESULTS The breast MRI showed a sensitivity of 86%, a specificity of 84%, a positive predictive value (PPV) of 75% and a negative predictive value (NPV) of 91% for the differentiation between benign and malignant in these 59 MCs found with MG. Breast MRI in addition to MG could increase the PPV from 36% to 75% compared to MG alone. The MRI examination led to nine additional suspicious classified lesions in the study cohort. A total of 55% (5/9) of them turned out to be malignant. A total of 32 of 59 (54 %) women with suspicious MCs and benign histology were classified as non-suspicious by MRI. CONCLUSION An additionally performed breast MRI could have increased the diagnostic reliability in the assessment of MCs. Further, in our small cohort, a considerable number of malignant lesions without mammographically visible MCs were revealed.
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Affiliation(s)
- Patrik Pöschke
- Department of Gynecology and Obstetrics, University Hospital Erlangen, Universitätsstraße 21-23, 91054 Erlangen, Germany
| | - Evelyn Wenkel
- Radiologie München, Burgstraße 7, 80331 München, Germany
- Medizinische Fakultät, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany
| | - Carolin C Hack
- Department of Gynecology and Obstetrics, University Hospital Erlangen, Universitätsstraße 21-23, 91054 Erlangen, Germany
| | - Matthias W Beckmann
- Department of Gynecology and Obstetrics, University Hospital Erlangen, Universitätsstraße 21-23, 91054 Erlangen, Germany
| | - Michael Uder
- Department of Radiology, University Hospital Erlangen, Maximiliansplatz 3, 91054 Erlangen, Germany
| | - Sabine Ohlmeyer
- Department of Radiology, University Hospital Erlangen, Maximiliansplatz 3, 91054 Erlangen, Germany
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50
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Müller-Franzes G, Huck L, Tayebi Arasteh S, Khader F, Han T, Schulz V, Dethlefsen E, Kather JN, Nebelung S, Nolte T, Kuhl C, Truhn D. Using Machine Learning to Reduce the Need for Contrast Agents in Breast MRI through Synthetic Images. Radiology 2023; 307:e222211. [PMID: 36943080 DOI: 10.1148/radiol.222211] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/23/2023]
Abstract
Background Reducing the amount of contrast agent needed for contrast-enhanced breast MRI is desirable. Purpose To investigate if generative adversarial networks (GANs) can recover contrast-enhanced breast MRI scans from unenhanced images and virtual low-contrast-enhanced images. Materials and Methods In this retrospective study of breast MRI performed from January 2010 to December 2019, simulated low-contrast images were produced by adding virtual noise to the existing contrast-enhanced images. GANs were then trained to recover the contrast-enhanced images from the simulated low-contrast images (approach A) or from the unenhanced T1- and T2-weighted images (approach B). Two experienced radiologists were tasked with distinguishing between real and synthesized contrast-enhanced images using both approaches. Image appearance and conspicuity of enhancing lesions on the real versus synthesized contrast-enhanced images were independently compared and rated on a five-point Likert scale. P values were calculated by using bootstrapping. Results A total of 9751 breast MRI examinations from 5086 patients (mean age, 56 years ± 10 [SD]) were included. Readers who were blinded to the nature of the images could not distinguish real from synthetic contrast-enhanced images (average accuracy of differentiation: approach A, 52 of 100; approach B, 61 of 100). The test set included images with and without enhancing lesions (29 enhancing masses and 21 nonmass enhancement; 50 total). When readers who were not blinded compared the appearance of the real versus synthetic contrast-enhanced images side by side, approach A image ratings were significantly higher than those of approach B (mean rating, 4.6 ± 0.1 vs 3.0 ± 0.2; P < .001), with the noninferiority margin met by synthetic images from approach A (P < .001) but not B (P > .99). Conclusion Generative adversarial networks may be useful to enable breast MRI with reduced contrast agent dose. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Bahl in this issue.
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Affiliation(s)
- Gustav Müller-Franzes
- From the Department of Diagnostic and Interventional Radiology (G.M.F., L.H., S.T.A., F.K., E.D., S.N., T.N., C.K., D.T.) and Department of Medicine III (J.N.K.), University Hospital RWTH Aachen, Pauwelsstrasse 30, Aachen 52074, Germany; and Department of Physics of Molecular Imaging Systems, Division of Experimental Molecular Imaging, RWTH Aachen University, Aachen, Germany (T.H., V.S.)
| | - Luisa Huck
- From the Department of Diagnostic and Interventional Radiology (G.M.F., L.H., S.T.A., F.K., E.D., S.N., T.N., C.K., D.T.) and Department of Medicine III (J.N.K.), University Hospital RWTH Aachen, Pauwelsstrasse 30, Aachen 52074, Germany; and Department of Physics of Molecular Imaging Systems, Division of Experimental Molecular Imaging, RWTH Aachen University, Aachen, Germany (T.H., V.S.)
| | - Soroosh Tayebi Arasteh
- From the Department of Diagnostic and Interventional Radiology (G.M.F., L.H., S.T.A., F.K., E.D., S.N., T.N., C.K., D.T.) and Department of Medicine III (J.N.K.), University Hospital RWTH Aachen, Pauwelsstrasse 30, Aachen 52074, Germany; and Department of Physics of Molecular Imaging Systems, Division of Experimental Molecular Imaging, RWTH Aachen University, Aachen, Germany (T.H., V.S.)
| | - Firas Khader
- From the Department of Diagnostic and Interventional Radiology (G.M.F., L.H., S.T.A., F.K., E.D., S.N., T.N., C.K., D.T.) and Department of Medicine III (J.N.K.), University Hospital RWTH Aachen, Pauwelsstrasse 30, Aachen 52074, Germany; and Department of Physics of Molecular Imaging Systems, Division of Experimental Molecular Imaging, RWTH Aachen University, Aachen, Germany (T.H., V.S.)
| | - Tianyu Han
- From the Department of Diagnostic and Interventional Radiology (G.M.F., L.H., S.T.A., F.K., E.D., S.N., T.N., C.K., D.T.) and Department of Medicine III (J.N.K.), University Hospital RWTH Aachen, Pauwelsstrasse 30, Aachen 52074, Germany; and Department of Physics of Molecular Imaging Systems, Division of Experimental Molecular Imaging, RWTH Aachen University, Aachen, Germany (T.H., V.S.)
| | - Volkmar Schulz
- From the Department of Diagnostic and Interventional Radiology (G.M.F., L.H., S.T.A., F.K., E.D., S.N., T.N., C.K., D.T.) and Department of Medicine III (J.N.K.), University Hospital RWTH Aachen, Pauwelsstrasse 30, Aachen 52074, Germany; and Department of Physics of Molecular Imaging Systems, Division of Experimental Molecular Imaging, RWTH Aachen University, Aachen, Germany (T.H., V.S.)
| | - Ebba Dethlefsen
- From the Department of Diagnostic and Interventional Radiology (G.M.F., L.H., S.T.A., F.K., E.D., S.N., T.N., C.K., D.T.) and Department of Medicine III (J.N.K.), University Hospital RWTH Aachen, Pauwelsstrasse 30, Aachen 52074, Germany; and Department of Physics of Molecular Imaging Systems, Division of Experimental Molecular Imaging, RWTH Aachen University, Aachen, Germany (T.H., V.S.)
| | - Jakob Nikolas Kather
- From the Department of Diagnostic and Interventional Radiology (G.M.F., L.H., S.T.A., F.K., E.D., S.N., T.N., C.K., D.T.) and Department of Medicine III (J.N.K.), University Hospital RWTH Aachen, Pauwelsstrasse 30, Aachen 52074, Germany; and Department of Physics of Molecular Imaging Systems, Division of Experimental Molecular Imaging, RWTH Aachen University, Aachen, Germany (T.H., V.S.)
| | - Sven Nebelung
- From the Department of Diagnostic and Interventional Radiology (G.M.F., L.H., S.T.A., F.K., E.D., S.N., T.N., C.K., D.T.) and Department of Medicine III (J.N.K.), University Hospital RWTH Aachen, Pauwelsstrasse 30, Aachen 52074, Germany; and Department of Physics of Molecular Imaging Systems, Division of Experimental Molecular Imaging, RWTH Aachen University, Aachen, Germany (T.H., V.S.)
| | - Teresa Nolte
- From the Department of Diagnostic and Interventional Radiology (G.M.F., L.H., S.T.A., F.K., E.D., S.N., T.N., C.K., D.T.) and Department of Medicine III (J.N.K.), University Hospital RWTH Aachen, Pauwelsstrasse 30, Aachen 52074, Germany; and Department of Physics of Molecular Imaging Systems, Division of Experimental Molecular Imaging, RWTH Aachen University, Aachen, Germany (T.H., V.S.)
| | - Christiane Kuhl
- From the Department of Diagnostic and Interventional Radiology (G.M.F., L.H., S.T.A., F.K., E.D., S.N., T.N., C.K., D.T.) and Department of Medicine III (J.N.K.), University Hospital RWTH Aachen, Pauwelsstrasse 30, Aachen 52074, Germany; and Department of Physics of Molecular Imaging Systems, Division of Experimental Molecular Imaging, RWTH Aachen University, Aachen, Germany (T.H., V.S.)
| | - Daniel Truhn
- From the Department of Diagnostic and Interventional Radiology (G.M.F., L.H., S.T.A., F.K., E.D., S.N., T.N., C.K., D.T.) and Department of Medicine III (J.N.K.), University Hospital RWTH Aachen, Pauwelsstrasse 30, Aachen 52074, Germany; and Department of Physics of Molecular Imaging Systems, Division of Experimental Molecular Imaging, RWTH Aachen University, Aachen, Germany (T.H., V.S.)
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