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Djuric O, Deandrea S, Mantellini P, Sardanelli F, Venturelli F, Montemezzi S, Vecchio R, Bucchi L, Senore C, Giordano L, Paci E, Bonifacino A, Calabrese M, Caumo F, Degrassi F, Sassoli De' Bianchi P, Battisti F, Zappa M, Pattacini P, Campari C, Nitrosi A, Di Leo G, Frigerio A, Magni V, Fornasa F, Romanucci G, Falini P, Auzzi N, Armaroli P, Giorgi Rossi P. Budget impact analysis of introducing digital breast tomosynthesis in breast cancer screening in Italy. LA RADIOLOGIA MEDICA 2024:10.1007/s11547-024-01850-7. [PMID: 39162938 DOI: 10.1007/s11547-024-01850-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Accepted: 07/04/2024] [Indexed: 08/21/2024]
Abstract
PURPOSE This study quantifies the impact on budget and cost per health benefit of implementing digital breast tomosynthesis (DBT) in place of digital mammography (DM) for breast cancer screening among asymptomatic women in Italy. METHODS A budget impact analysis and a cost consequence analysis were conducted using parameters from the MAITA project and literature. The study considered four scenarios for DBT implementation, i.e., DBT for all women, DBT for women aged 45-49 years, DBT based on breast density (BI-RADS C + D or D only), and compared these to the current DM screening. Healthcare provider's perspective was adopted, including screening, diagnosis, and cancer treatment costs. RESULTS Introducing DBT for all women would increase overall screening costs by 20%. Targeting DBT to women aged 45-49 years or with dense breasts would result in smaller cost increases (3.2% for age-based and 1.4-10.7% for density-based scenarios). The cost per avoided interval cancer was significantly higher when DBT was applied to all women compared to targeted approaches. The cost per gained early-detected cancer slightly increases in targeted approaches, while the assumptions on the clinical significance and overdiagnosis of cancers detected by DBT and not by DM have a strong impact. CONCLUSIONS Implementing DBT as a primary breast cancer test in screening programs in Italy would lead to a substantial increase in costs. Tailoring DBT use to women aged 45-49 or with dense breasts could enhance the feasibility and sustainability of the intervention. Further research is needed to clarify the impact of DBT on overdiagnosis and the long-term outcomes.
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Affiliation(s)
- Olivera Djuric
- Azienda USL - IRCCS di Reggio Emilia, Reggio Emilia, Italy
- Centre for Environmental, Nutritional and Genetic Epidemiology (CREAGEN), University of Modena and Reggio Emilia, Modena, Italy
| | | | - Paola Mantellini
- ISPRO - Istituto per lo Studio, la Prevenzione e la Rete Oncologica, Florence, Italy
| | | | | | | | | | - Lauro Bucchi
- IRCCS Istituto Romagnolo per lo Studio dei Tumori "Dino Amadori" - IRST S.r.l., Meldola, Forlì-Cesena, Italy
| | - Carlo Senore
- AOU Città della Salute e della Scienza- CPO Piemonte Torino, Turin, Italy
| | - Livia Giordano
- AOU Città della Salute e della Scienza- CPO Piemonte Torino, Turin, Italy
| | | | | | | | | | - Flori Degrassi
- Associazione Nazionale Donne Operate al Seno - ANDOS, Milan, Italy
| | | | - Francesca Battisti
- ISPRO - Istituto per lo Studio, la Prevenzione e la Rete Oncologica, Florence, Italy
| | - Marco Zappa
- ISPRO - Istituto per lo Studio, la Prevenzione e la Rete Oncologica, Florence, Italy
| | | | - Cinzia Campari
- Azienda USL - IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Andrea Nitrosi
- Azienda USL - IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Giovanni Di Leo
- IRCCS Policlinico San Donato, San Donato Milanese, Milan, Italy
| | - Alfonso Frigerio
- AOU Città della Salute e della Scienza- CPO Piemonte Torino, Turin, Italy
| | - Veronica Magni
- IRCCS Policlinico San Donato, San Donato Milanese, Milan, Italy
| | - Francesca Fornasa
- Breast Unit ULSS9 Scaligera, Ospedale Fracastoro, San Bonifacio, Verona, Italy
| | - Giovanna Romanucci
- Breast Unit ULSS9 Scaligera, Ospedale Fracastoro, San Bonifacio, Verona, Italy
| | - Patrizia Falini
- ISPRO - Istituto per lo Studio, la Prevenzione e la Rete Oncologica, Florence, Italy
| | - Noemi Auzzi
- ISPRO - Istituto per lo Studio, la Prevenzione e la Rete Oncologica, Florence, Italy
| | - Paola Armaroli
- AOU Città della Salute e della Scienza- CPO Piemonte Torino, Turin, Italy
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Djuric O, Deandrea S, Mantellini P, Sardanelli F, Venturelli F, Montemezzi S, Vecchio R, Bucchi L, Senore C, Giordano L, Paci E, Bonifacino A, Calabrese M, Caumo F, Degrassi F, Sassoli De' Bianchi P, Battisti F, Zappa M, Pattacini P, Campari C, Nitrosi A, Di Leo G, Frigerio A, Magni V, Fornasa F, Romanucci G, Falini P, Auzzi N, Armaroli P, Giorgi Rossi P. Organizational impact of systemic implementation of digital breast tomosynthesis as a primary test for breast cancer screening in Italy. LA RADIOLOGIA MEDICA 2024; 129:1156-1172. [PMID: 39042203 DOI: 10.1007/s11547-024-01849-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Accepted: 07/04/2024] [Indexed: 07/24/2024]
Abstract
PURPOSE We present a comprehensive investigation into the organizational, social, and ethical impact of implementing digital breast tomosynthesis (DBT) as a primary test for breast cancer screening in Italy. The analyses aimed to assess the feasibility of DBT specifically for all women aged 45-74, women aged 45-49 only, or those with dense breasts only. METHODS Questions were framed according to the European Network of Health Technology Assessment (EuNetHTA) Screening Core Model to produce evidence for the resources, equity, acceptability, and feasibility domains of the Grading of Recommendations Assessment, Development and Evaluation (GRADE) decision framework. The study integrated evidence from the literature, the MAITA DBT trials, and Italian pilot programs. Structured interviews, surveys, and systematic reviews were conducted to gather data on organizational impact, acceptability among women, reading and acquisition times, and the technical requirements of DBT in screening. RESULTS Implementing DBT could significantly affect the screening program, primarily due to increased reading times and the need for additional human resources (radiologists and radiographers). Participation rates in DBT screening were similar, if not better, to those observed with standard digital mammography, indicating good acceptability among women. The study also highlighted the necessity for specific training for radiographers. The interviewed key persons unanimously considered feasible tailored screening strategies based on breast density or age, but they require effective communication with the target population. CONCLUSIONS An increase in radiologists' and radiographers' workload limits the feasibility of DBT screening. Tailored screening strategies may maximize the benefits of DBT while mitigating potential challenges.
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Affiliation(s)
- Olivera Djuric
- Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, Italy
- Centre for Environmental, Nutritional and Genetic Epidemiology (CREAGEN), University of Modena and Reggio Emilia, Modena, Italy
| | | | - Paola Mantellini
- ISPRO - Istituto per lo Studio, la Prevenzione e la Rete Oncologica, Florence, Italy
| | | | | | | | | | - Lauro Bucchi
- IRCCS Istituto Romagnolo per lo Studio dei Tumori "Dino Amadori"-IRST S.r.l., Meldola, Forlì-Cesena, Italy
| | - Carlo Senore
- AOU Città della Salute e della Scienza-CPO Piemonte Turin, Turin, Italy
| | - Livia Giordano
- AOU Città della Salute e della Scienza-CPO Piemonte Turin, Turin, Italy
| | | | | | | | | | - Flori Degrassi
- Associazione Nazionale Donne Operate al Seno-ANDOS, Milan, Italy
| | | | - Francesca Battisti
- ISPRO - Istituto per lo Studio, la Prevenzione e la Rete Oncologica, Florence, Italy
| | - Marco Zappa
- ISPRO - Istituto per lo Studio, la Prevenzione e la Rete Oncologica, Florence, Italy
| | | | - Cinzia Campari
- Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Andrea Nitrosi
- Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Giovanni Di Leo
- IRCCS Policlinico San Donato, San Donato Milanese, Milan, Italy
| | - Alfonso Frigerio
- AOU Città della Salute e della Scienza-CPO Piemonte Turin, Turin, Italy
| | - Veronica Magni
- IRCCS Policlinico San Donato, San Donato Milanese, Milan, Italy
| | - Francesca Fornasa
- Breast Unit ULSS9 Scaligera, Ospedale Fracastoro, San Bonifacio, Verona, Italy
| | - Giovanna Romanucci
- Breast Unit ULSS9 Scaligera, Ospedale Fracastoro, San Bonifacio, Verona, Italy
| | - Patrizia Falini
- ISPRO - Istituto per lo Studio, la Prevenzione e la Rete Oncologica, Florence, Italy
| | - Noemi Auzzi
- ISPRO - Istituto per lo Studio, la Prevenzione e la Rete Oncologica, Florence, Italy
| | - Paola Armaroli
- AOU Città della Salute e della Scienza-CPO Piemonte Turin, Turin, Italy
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Xing D, Lv Y, Sun B, Chu T, Bao Q, Zhang H. Develop and Validate a Nomogram Combining Contrast-Enhanced Spectral Mammography Deep Learning with Clinical-Pathological Features to Predict Neoadjuvant Chemotherapy Response in Patients with ER-Positive/HER2-Negative Breast Cancer. Acad Radiol 2024:S1076-6332(24)00200-9. [PMID: 38641451 DOI: 10.1016/j.acra.2024.03.035] [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: 01/18/2024] [Revised: 03/23/2024] [Accepted: 03/26/2024] [Indexed: 04/21/2024]
Abstract
RATIONALE AND OBJECTIVES To develop and validate a nomogram that combines contrast-enhanced spectral mammography (CESM) deep learning with clinical-pathological features to predict neoadjuvant chemotherapy (NAC) response (either low Miller Payne (MP-L) grades 1-2 or high MP (MP-H) grades 3-5) in patients with ER-positive/HER2-negative breast cancer. MATERIALS AND METHODS In this retrospective study, 265 breast cancer patients were randomly allocated into training and test sets (used for models training and testing, respectively) at a 4:1 ratio. Deep learning models, based on the pre-trained ResNet34 model and initially fine-tuned for identifying breast cancer, were trained using low-energy and subtracted CESM images. The predicted results served as deep learning features for the deep learning-based model. Clinical-pathological features, including age, progesterone receptor (PR) status, estrogen receptor (ER) status, Ki67 expression levels, and neutrophil-to-lymphocyte ratio, were used for the clinical model. All these features contributed to the nomogram. Feature selection was performed through univariate analysis. Logistic regression models were developed and chosen using a stepwise selection method. The deep learning-based and clinical models, along with the nomogram, were evaluated using precision-recall curves, receiver operating characteristic (ROC) curves, specificity, recall, accuracy, negative predictive value, positive predictive value (PPV), balanced accuracy, F1-score, and decision curve analysis (DCA). RESULTS The nomogram demonstrated considerable predictive ability, with higher area under the ROC curve (0.95, P < 0.05), accuracy (0.94), specificity (0.98), PPV (0.89), and precision (0.89) compared to the deep learning-based and clinical models. In DCA, the nomogram showed substantial clinical value in assisting breast cancer treatment decisions, exhibiting a higher net benefit than the other models. CONCLUSION The nomogram, integrating CESM deep learning with clinical-pathological features, proved valuable for predicting NAC response in patients with ER-positive/HER2-negative breast cancer. Nomogram outperformed deep learning-based and clinical models.
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Affiliation(s)
- Dong Xing
- Department of Radiology,Yantai Yuhuangding Hospital, Yantai, Shandong 264000 China
| | - Yongbin Lv
- Department of Radiology,Yantai Yuhuangding Hospital, Yantai, Shandong 264000 China
| | - Bolin Sun
- Department of Interventional Therapy, Yantai Yuhuangding Hospital, Yantai, Shandong 264000, China
| | - Tongpeng Chu
- Department of Radiology,Yantai Yuhuangding Hospital, Yantai, Shandong 264000 China; Big Data and Artificial Intelligence Lab, Yantai Yuhuangding Hospital, Yantai, Shandong 264000, China
| | - Qianhao Bao
- Shandong University of Traditional Chinese Medicine, Jinan, Shandong 250300, China
| | - Han Zhang
- Department of Radiology,Yantai Yuhuangding Hospital, Yantai, Shandong 264000 China.
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Choi CJ, Vent TL, Acciavatti RJ, Maidment ADA. Line-based iterative geometric calibration method for a tomosynthesis system. Med Phys 2024; 51:2444-2460. [PMID: 38394613 PMCID: PMC11000589 DOI: 10.1002/mp.16981] [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: 10/03/2023] [Revised: 01/10/2024] [Accepted: 01/23/2024] [Indexed: 02/25/2024] Open
Abstract
BACKGROUND A next generation tomosynthesis (NGT) system, capable of two-dimensional source motion, detector motion in the perpendicular direction, and magnification tomosynthesis, was constructed to investigate different acquisition geometries. Existing position-based geometric calibration methods proved ineffective when applied to the NGT geometries. PURPOSE A line-based iterative calibration method is developed to perform accurate geometric calibration for the NGT system. METHODS The proposed method calculates the system geometry through virtual line segments created by pairs of fiducials within a calibration phantom, by minimizing the error between the line equations computed from the true and estimated fiducial projection pairs. It further attempts to correct the 3D fiducial locations based on the initial geometric calibration. The method's performance was assessed via simulation and experimental setups with four distinct NGT geometries: X, T, XZ, and TZ. The X geometry resembles a conventional DBT acquisition along the chest wall. The T geometry forms a "T"-shaped source path in mediolateral (ML) and posteroanterior (PA) directions. A descending detector motion is added to both X and T geometries to form the XZ and TZ geometries, respectively. Simulation studies were conducted to assess the robustness of the method to geometric perturbations and inaccuracies in fiducial locations. Experimental studies were performed to assess the impact of phantom magnification and the performance of the proposed method for various geometries, compared to the traditional position-based method. Star patterns were evaluated for both qualitative and quantitative analyses; the Fourier spectral distortions (FSDs) graphs and the contrast transfer function (CTF) were extracted. The limit of spatial resolution (LSR) was measured at 5% modulation of the CTF. RESULTS The proposed method presented is highly robust to geometric perturbation and fiducial inaccuracies. After the line-based iterative method, the mean distance between the true and estimated fiducial projections was [X, T, XZ, TZ]: [0.01, 0.01, 0.02, 0.01] mm. The impact of phantom magnification was observed; a contact-mode acquisition of a calibration phantom successfully provided an accurate geometry for 1.85× magnification images of a star pattern, with the X geometry. The FSD graphs for the contact-mode T geometry acquisition presented evidence of super-resolution, with the LSR of [0°-quadrant: 8.57, 90°-quadrant: 8.47] lp/mm. Finally, a contact-mode XZ geometry acquisition and a 1.50× magnification TZ geometry acquisition were reconstructed with three calibration methods-position-based, line-based, and iterative line-based. As more advanced methods are applied, the CTF becomes more isotropic, the FSD graphs demonstrate less spectral leakage as super-resolution is achieved, and the degree of blurring artifacts reduces significantly. CONCLUSIONS This study introduces a robust calibration method tailored to the unique requirements of advanced tomosynthesis systems. By employing virtual line segments and iterative techniques, we ensure accurate geometric calibration while mitigating the limitations posed by the complex acquisition geometries of the NGT system. Our method's ability to handle various NGT configurations and its tolerance to fiducial misalignment make it a superior choice compared to traditional calibration techniques.
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Affiliation(s)
- Chloe J Choi
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Trevor L Vent
- Department of Radiology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Raymond J Acciavatti
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Andrew D A Maidment
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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Kim JG, Haslam B, Diab AR, Sakhare A, Grisot G, Lee H, Holt J, Lee CI, Lotter W, Sorensen AG. Impact of a Categorical AI System for Digital Breast Tomosynthesis on Breast Cancer Interpretation by Both General Radiologists and Breast Imaging Specialists. Radiol Artif Intell 2024; 6:e230137. [PMID: 38323914 PMCID: PMC10982824 DOI: 10.1148/ryai.230137] [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: 04/25/2023] [Revised: 12/26/2023] [Accepted: 01/22/2024] [Indexed: 02/08/2024]
Abstract
Purpose To evaluate performance improvements of general radiologists and breast imaging specialists when interpreting a set of diverse digital breast tomosynthesis (DBT) examinations with the aid of a custom-built categorical artificial intelligence (AI) system. Materials and Methods A fully balanced multireader, multicase reader study was conducted to compare the performance of 18 radiologists (nine general radiologists and nine breast imaging specialists) reading 240 retrospectively collected screening DBT mammograms (mean patient age, 59.8 years ± 11.3 [SD]; 100% women), acquired between August 2016 and March 2019, with and without the aid of a custom-built categorical AI system. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity across general radiologists and breast imaging specialists reading with versus without AI were assessed. Reader performance was also analyzed as a function of breast cancer characteristics and patient subgroups. Results Every radiologist demonstrated improved interpretation performance when reading with versus without AI, with an average AUC of 0.93 versus 0.87, demonstrating a difference in AUC of 0.06 (95% CI: 0.04, 0.08; P < .001). Improvement in AUC was observed for both general radiologists (difference of 0.08; P < .001) and breast imaging specialists (difference of 0.04; P < .001) and across all cancer characteristics (lesion type, lesion size, and pathology) and patient subgroups (race and ethnicity, age, and breast density) examined. Conclusion A categorical AI system helped improve overall radiologist interpretation performance of DBT screening mammograms for both general radiologists and breast imaging specialists and across various patient subgroups and breast cancer characteristics. Keywords: Computer-aided Diagnosis, Screening Mammography, Digital Breast Tomosynthesis, Breast Cancer, Screening, Convolutional Neural Network (CNN), Artificial Intelligence Supplemental material is available for this article. © RSNA, 2024.
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Affiliation(s)
- Jiye G. Kim
- From DeepHealth, RadNet AI Solutions, 212 Elm Street, Somerville, MA 02144 (J.G.K., B.H., A.R.D., A.S., G.G., H.L., W.L., A.G.S.); Atos zData, Newark, Del (A.S.); Delaware Imaging Network, RadNet, Wilmington, Del (J.H.); Department of Radiology, University of Washington School of Medicine, Fred Hutchinson Cancer Center, Seattle, Wash (C.I.L.); Department of Health Systems & Population Health, School of Public Health, University of Washington, Seattle, Wash (C.I.L.); and Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (W.L.)
| | - Bryan Haslam
- From DeepHealth, RadNet AI Solutions, 212 Elm Street, Somerville, MA 02144 (J.G.K., B.H., A.R.D., A.S., G.G., H.L., W.L., A.G.S.); Atos zData, Newark, Del (A.S.); Delaware Imaging Network, RadNet, Wilmington, Del (J.H.); Department of Radiology, University of Washington School of Medicine, Fred Hutchinson Cancer Center, Seattle, Wash (C.I.L.); Department of Health Systems & Population Health, School of Public Health, University of Washington, Seattle, Wash (C.I.L.); and Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (W.L.)
| | - Abdul Rahman Diab
- From DeepHealth, RadNet AI Solutions, 212 Elm Street, Somerville, MA 02144 (J.G.K., B.H., A.R.D., A.S., G.G., H.L., W.L., A.G.S.); Atos zData, Newark, Del (A.S.); Delaware Imaging Network, RadNet, Wilmington, Del (J.H.); Department of Radiology, University of Washington School of Medicine, Fred Hutchinson Cancer Center, Seattle, Wash (C.I.L.); Department of Health Systems & Population Health, School of Public Health, University of Washington, Seattle, Wash (C.I.L.); and Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (W.L.)
| | - Ashwin Sakhare
- From DeepHealth, RadNet AI Solutions, 212 Elm Street, Somerville, MA 02144 (J.G.K., B.H., A.R.D., A.S., G.G., H.L., W.L., A.G.S.); Atos zData, Newark, Del (A.S.); Delaware Imaging Network, RadNet, Wilmington, Del (J.H.); Department of Radiology, University of Washington School of Medicine, Fred Hutchinson Cancer Center, Seattle, Wash (C.I.L.); Department of Health Systems & Population Health, School of Public Health, University of Washington, Seattle, Wash (C.I.L.); and Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (W.L.)
| | - Giorgia Grisot
- From DeepHealth, RadNet AI Solutions, 212 Elm Street, Somerville, MA 02144 (J.G.K., B.H., A.R.D., A.S., G.G., H.L., W.L., A.G.S.); Atos zData, Newark, Del (A.S.); Delaware Imaging Network, RadNet, Wilmington, Del (J.H.); Department of Radiology, University of Washington School of Medicine, Fred Hutchinson Cancer Center, Seattle, Wash (C.I.L.); Department of Health Systems & Population Health, School of Public Health, University of Washington, Seattle, Wash (C.I.L.); and Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (W.L.)
| | - Hyunkwang Lee
- From DeepHealth, RadNet AI Solutions, 212 Elm Street, Somerville, MA 02144 (J.G.K., B.H., A.R.D., A.S., G.G., H.L., W.L., A.G.S.); Atos zData, Newark, Del (A.S.); Delaware Imaging Network, RadNet, Wilmington, Del (J.H.); Department of Radiology, University of Washington School of Medicine, Fred Hutchinson Cancer Center, Seattle, Wash (C.I.L.); Department of Health Systems & Population Health, School of Public Health, University of Washington, Seattle, Wash (C.I.L.); and Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (W.L.)
| | - Jacqueline Holt
- From DeepHealth, RadNet AI Solutions, 212 Elm Street, Somerville, MA 02144 (J.G.K., B.H., A.R.D., A.S., G.G., H.L., W.L., A.G.S.); Atos zData, Newark, Del (A.S.); Delaware Imaging Network, RadNet, Wilmington, Del (J.H.); Department of Radiology, University of Washington School of Medicine, Fred Hutchinson Cancer Center, Seattle, Wash (C.I.L.); Department of Health Systems & Population Health, School of Public Health, University of Washington, Seattle, Wash (C.I.L.); and Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (W.L.)
| | - Christoph I. Lee
- From DeepHealth, RadNet AI Solutions, 212 Elm Street, Somerville, MA 02144 (J.G.K., B.H., A.R.D., A.S., G.G., H.L., W.L., A.G.S.); Atos zData, Newark, Del (A.S.); Delaware Imaging Network, RadNet, Wilmington, Del (J.H.); Department of Radiology, University of Washington School of Medicine, Fred Hutchinson Cancer Center, Seattle, Wash (C.I.L.); Department of Health Systems & Population Health, School of Public Health, University of Washington, Seattle, Wash (C.I.L.); and Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (W.L.)
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Wang L. Mammography with deep learning for breast cancer detection. Front Oncol 2024; 14:1281922. [PMID: 38410114 PMCID: PMC10894909 DOI: 10.3389/fonc.2024.1281922] [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: 08/23/2023] [Accepted: 01/19/2024] [Indexed: 02/28/2024] Open
Abstract
X-ray mammography is currently considered the golden standard method for breast cancer screening, however, it has limitations in terms of sensitivity and specificity. With the rapid advancements in deep learning techniques, it is possible to customize mammography for each patient, providing more accurate information for risk assessment, prognosis, and treatment planning. This paper aims to study the recent achievements of deep learning-based mammography for breast cancer detection and classification. This review paper highlights the potential of deep learning-assisted X-ray mammography in improving the accuracy of breast cancer screening. While the potential benefits are clear, it is essential to address the challenges associated with implementing this technology in clinical settings. Future research should focus on refining deep learning algorithms, ensuring data privacy, improving model interpretability, and establishing generalizability to successfully integrate deep learning-assisted mammography into routine breast cancer screening programs. It is hoped that the research findings will assist investigators, engineers, and clinicians in developing more effective breast imaging tools that provide accurate diagnosis, sensitivity, and specificity for breast cancer.
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Affiliation(s)
- Lulu Wang
- Biomedical Device Innovation Center, Shenzhen Technology University, Shenzhen, China
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7
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Gao M, Fessler JA, Chan HP. Model-based deep CNN-regularized reconstruction for digital breast tomosynthesis with a task-based CNN image assessment approach. Phys Med Biol 2023; 68:245024. [PMID: 37988758 PMCID: PMC10719554 DOI: 10.1088/1361-6560/ad0eb4] [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: 06/30/2023] [Revised: 11/02/2023] [Accepted: 11/21/2023] [Indexed: 11/23/2023]
Abstract
Objective. Digital breast tomosynthesis (DBT) is a quasi-three-dimensional breast imaging modality that improves breast cancer screening and diagnosis because it reduces fibroglandular tissue overlap compared with 2D mammography. However, DBT suffers from noise and blur problems that can lower the detectability of subtle signs of cancers such as microcalcifications (MCs). Our goal is to improve the image quality of DBT in terms of image noise and MC conspicuity.Approach. We proposed a model-based deep convolutional neural network (deep CNN or DCNN) regularized reconstruction (MDR) for DBT. It combined a model-based iterative reconstruction (MBIR) method that models the detector blur and correlated noise of the DBT system and the learning-based DCNN denoiser using the regularization-by-denoising framework. To facilitate the task-based image quality assessment, we also proposed two DCNN tools for image evaluation: a noise estimator (CNN-NE) trained to estimate the root-mean-square (RMS) noise of the images, and an MC classifier (CNN-MC) as a DCNN model observer to evaluate the detectability of clustered MCs in human subject DBTs.Main results. We demonstrated the efficacies of CNN-NE and CNN-MC on a set of physical phantom DBTs. The MDR method achieved low RMS noise and the highest detection area under the receiver operating characteristic curve (AUC) rankings evaluated by CNN-NE and CNN-MC among the reconstruction methods studied on an independent test set of human subject DBTs.Significance. The CNN-NE and CNN-MC may serve as a cost-effective surrogate for human observers to provide task-specific metrics for image quality comparisons. The proposed reconstruction method shows the promise of combining physics-based MBIR and learning-based DCNNs for DBT image reconstruction, which may potentially lead to lower dose and higher sensitivity and specificity for MC detection in breast cancer screening and diagnosis.
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Affiliation(s)
- Mingjie Gao
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109, United States of America
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109, United States of America
| | - Jeffrey A Fessler
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109, United States of America
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109, United States of America
| | - Heang-Ping Chan
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109, United States of America
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Fan Y, Zhao D, Su J, Yuan W, Niu S, Guo W, Jiang W. Radiomic Signatures Based on Mammography and Magnetic Resonance Imaging as New Markers for Estimation of Ki-67 and HER-2 Status in Breast Cancer. J Comput Assist Tomogr 2023; 47:890-897. [PMID: 37948363 DOI: 10.1097/rct.0000000000001502] [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: 07/23/2023]
Abstract
OBJECTIVE The aim of the study is to investigate the values of intratumoral and peritumoral regions based on mammography and magnetic resonance imaging for the prediction of Ki-67 and human epidermal growth factor (HER-2) status in breast cancer (BC). METHODS Two hundred BC patients were consecutively enrolled between January 2017 and March 2021 and divided into training (n = 133) and validation (n = 67) groups. All the patients underwent breast mammography and magnetic resonance imaging screening. Features were derived from intratumoral and peritumoral regions of the tumor and selected using the least absolute shrinkage and selection operator regression to build radiomic signatures (RSs). Receiver operating characteristic curve analysis and the DeLong test were performed to assess and compare each RS. RESULTS For each modality, the combined RSs integrating features from intratumoral and peritumoral regions always showed better prediction performance for predicting Ki-67 and HER-2 status compared with the RSs derived from intratumoral or peritumoral regions separately. The multimodality and multiregional combined RSs achieved the best prediction performance for predicting the Ki-67 and HER-2 status with an area under the receiver operating characteristic curve of 0.888 and 0.868 in the training cohort and 0.800 and 0.848 in the validation cohort, respectively. CONCLUSIONS Peritumoral areas provide complementary information to intratumoral regions of BC. The developed multimodality and multiregional combined RSs have good potential for noninvasive evaluation of Ki-67 and HER-2 status in BC.
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Affiliation(s)
- Ying Fan
- From the School of Intelligent Medicine, China Medical University, Shenyang
| | - Dan Zhao
- Department of Medical Imaging, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Liaoning
| | - Juan Su
- From the School of Intelligent Medicine, China Medical University, Shenyang
| | - Wendi Yuan
- From the School of Intelligent Medicine, China Medical University, Shenyang
| | - Shuxian Niu
- From the School of Intelligent Medicine, China Medical University, Shenyang
| | - Wei Guo
- College of Computer Science, Shenyang Aerospace University, Shenyang
| | - Wenyan Jiang
- Department of Scientific Research and Academic, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Liaoning, People's Republic. China
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9
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Tang H, Wang J, Sun L, Wang S, Xiang J, Xi Y, Chen Y, Jiang Y. A new projection correction based voting strategy for breast calcification artifact reduction. Phys Med Biol 2023; 68:185012. [PMID: 37582378 DOI: 10.1088/1361-6560/acf093] [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: 05/07/2023] [Accepted: 08/15/2023] [Indexed: 08/17/2023]
Abstract
Objective.Digital Breast Tomosynthesis (DBT) is an imaging technique that combines traditional tomography with image processing and reconstruction techniques. In screening for breast cancer, high attenuation lesion will cause calcification hardening artifacts, which reduces the reconstructed image quality and limits diagnostic accuracy. We focus on the reconstruction artifacts that are caused by high-attenuation features in DBT, and aim to propose an efficient and accurate method to remove calcification artifacts and retain calcification information.Approach.The proposed method first introduces a new segmentation method, which can segment breast calcification accurately and effectively. Then an interpolation method is used to eliminate both the calcified area and artifact area in the projection images which are then used to reconstruct the image without artifacts and calcifications. Finally, the interpolated reconstructed image and the unprocessed reconstructed image are fused under the proposed voting strategy to obtain the DBT image with calcification artifacts removal.Main results.18 groups of simulated projection data and 10 groups of real projection data collected by us are used to evaluate the proposed method. Experimental results show that our algorithm can effectively reduce the calcification artifact and preserve the effective information in the image as well.Significance.The proposed method utilizes a novel projection correction based voting fusion strategy for image fusion, and is advanced in reducing breast calcification artifacts compared with other state-of-the-art methods. Our work paves the way for more efficient and precise DBT breast cancer screening.
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Affiliation(s)
- Hui Tang
- Laboratory of Image Science and Technology, School of Computer Science and Engineering, Southeast University, Nanjing, People's Republic of China
- Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, Nanjing, People's Republic of China
| | - Jiashun Wang
- Laboratory of Image Science and Technology, School of Computer Science and Engineering, Southeast University, Nanjing, People's Republic of China
| | - Liang Sun
- Laboratory of Image Science and Technology, School of Computer Science and Engineering, Southeast University, Nanjing, People's Republic of China
| | - Shijie Wang
- Laboratory of Image Science and Technology, School of Computer Science and Engineering, Southeast University, Nanjing, People's Republic of China
- Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, Nanjing, People's Republic of China
| | - Jun Xiang
- CT RPA Department, Shanghai United Imaging Healthcare Co., Ltd, Shanghai, People's Republic of China
| | - Yan Xi
- Jiangsu First-Imaging Medical Equipment Co., Ltd, Nantong, Jiangsu, People's Republic of China
| | - Yang Chen
- Laboratory of Image Science and Technology, School of Computer Science and Engineering, Southeast University, Nanjing, People's Republic of China
- Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, Nanjing, People's Republic of China
| | - Yanni Jiang
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, No. 300 Guangzhou Road, Nanjing, People's Republic of China
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10
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La Forgia D, Signorile R, Bove S, Arezzo F, Cormio G, Daniele A, Dellino M, Fanizzi A, Gatta G, Lafranceschina M, Massafra R, Rizzo A, Zito FA, Neri E, Faggioni L. Impact of the systematic introduction of tomosynthesis on breast biopsies: 10 years of results. LA RADIOLOGIA MEDICA 2023:10.1007/s11547-023-01640-7. [PMID: 37198373 DOI: 10.1007/s11547-023-01640-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Accepted: 04/21/2023] [Indexed: 05/19/2023]
Abstract
Digital Breast Tomosynthesis (DBT) is a cutting-edge technology introduced in recent years as an in-depth analysis of breast cancer diagnostics. Compared with 2D Full-Field Digital Mammography, DBT has demonstrated greater sensitivity and specificity in detecting breast tumors. This work aims to quantitatively evaluate the impact of the systematic introduction of DBT in terms of Biopsy Rate and Positive Predictive Values for the number of biopsies performed (PPV-3). For this purpose, we collected 69,384 mammograms and 7894 biopsies, of which 6484 were Core Biopsies and 1410 were stereotactic Vacuum-assisted Breast Biopsies (VABBs), performed on female patients afferent to the Breast Unit of the Istituto Tumori "Giovanni Paolo II" of Bari from 2012 to 2021, thus, in the period before, during and after the systematic introduction of DBT. Linear regression analysis was then implemented to investigate how the Biopsy Rate had changed over the 10 year screening. The next step was to focus on VABBs, which were generally performed during in-depth examinations of mammogram detected lesions. Finally, three radiologists from the institute's Breast Unit underwent a comparative study to ascertain their performances in terms of breast cancer detection rates before and after the introduction of DBT. As a result, it was demonstrated that both the overall Biopsy Rate and the VABBs Biopsy Rate significantly decreased following the introduction of DBT, with the diagnosis of an equal number of tumors. Besides, no statistically significant differences were observed among the three operators evaluated. In conclusion, this work highlights how the systematic introduction of DBT has significantly impacted the breast cancer diagnostic procedure, by improving the diagnostic quality and thereby reducing needless biopsies, resulting in a consequent reduction in costs.
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Affiliation(s)
- Daniele La Forgia
- Istituto Tumori Giovanni Paolo II, I.R.C.C.S, Via Orazio Flacco 65, 70124, Bari, Italy
| | - Rahel Signorile
- Istituto Tumori Giovanni Paolo II, I.R.C.C.S, Via Orazio Flacco 65, 70124, Bari, Italy
| | - Samantha Bove
- Istituto Tumori Giovanni Paolo II, I.R.C.C.S, Via Orazio Flacco 65, 70124, Bari, Italy
| | - Francesca Arezzo
- Istituto Tumori Giovanni Paolo II, I.R.C.C.S, Via Orazio Flacco 65, 70124, Bari, Italy
- Department of Interdisciplinary Medicine (DIM), University of Bari Aldo Moro, 70121, Bari, Italy
| | - Gennaro Cormio
- Istituto Tumori Giovanni Paolo II, I.R.C.C.S, Via Orazio Flacco 65, 70124, Bari, Italy
- Department of Interdisciplinary Medicine (DIM), University of Bari Aldo Moro, 70121, Bari, Italy
| | - Antonella Daniele
- Istituto Tumori Giovanni Paolo II, I.R.C.C.S, Via Orazio Flacco 65, 70124, Bari, Italy
| | - Miriam Dellino
- Clinic of Obstetrics and Gynecology, San Paolo Hospital, 70123, Bari, Italy
- Department of Biomedical Sciences and Human Oncology, University of Bari Aldo Moro, 70100, Bari, Italy
| | - Annarita Fanizzi
- Istituto Tumori Giovanni Paolo II, I.R.C.C.S, Via Orazio Flacco 65, 70124, Bari, Italy.
| | - Gianluca Gatta
- Breast Unit, Department of Clinical and Experimental Internship, University of Campania Luigi Vanvitelli, Via De Crecchio 7, 80138, Naples, Italy
| | - Miria Lafranceschina
- Istituto Tumori Giovanni Paolo II, I.R.C.C.S, Via Orazio Flacco 65, 70124, Bari, Italy
| | - Raffaella Massafra
- Istituto Tumori Giovanni Paolo II, I.R.C.C.S, Via Orazio Flacco 65, 70124, Bari, Italy.
| | - Alessandro Rizzo
- Istituto Tumori Giovanni Paolo II, I.R.C.C.S, Via Orazio Flacco 65, 70124, Bari, Italy
| | | | - Emanuele Neri
- Academic Radiology, Department of Translational Research, University of Pisa, Via Roma 67, 56126, Pisa, Italy
| | - Lorenzo Faggioni
- Academic Radiology, Department of Translational Research, University of Pisa, Via Roma 67, 56126, Pisa, Italy
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11
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Murtas F, Landoni V, Ordòñez P, Greco L, Ferranti FR, Russo A, Perracchio L, Vidiri A. Clinical-radiomic models based on digital breast tomosynthesis images: a preliminary investigation of a predictive tool for cancer diagnosis. Front Oncol 2023; 13:1152158. [PMID: 37251915 PMCID: PMC10213670 DOI: 10.3389/fonc.2023.1152158] [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: 01/27/2023] [Accepted: 04/24/2023] [Indexed: 05/31/2023] Open
Abstract
Objective This study aimed to develop a clinical-radiomic model based on radiomic features extracted from digital breast tomosynthesis (DBT) images and clinical factors that may help to discriminate between benign and malignant breast lesions. Materials and methods A total of 150 patients were included in this study. DBT images acquired in the setting of a screening protocol were used. Lesions were delineated by two expert radiologists. Malignity was always confirmed by histopathological data. The data were randomly divided into training and validation set with an 80:20 ratio. A total of 58 radiomic features were extracted from each lesion using the LIFEx Software. Three different key methods of feature selection were implemented in Python: (1) K best (KB), (2) sequential (S), and (3) Random Forrest (RF). A model was therefore produced for each subset of seven variables using a machine-learning algorithm, which exploits the RF classification based on the Gini index. Results All three clinical-radiomic models show significant differences (p < 0.05) between malignant and benign tumors. The area under the curve (AUC) values of the models obtained with three different feature selection methods were 0.72 [0.64,0.80], 0.72 [0.64,0.80] and 0.74 [0.66,0.82] for KB, SFS, and RF, respectively. Conclusion The clinical-radiomic models developed by using radiomic features from DBT images showed a good discriminating power and hence may help radiologists in breast cancer tumor diagnoses already at the first screening.
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Affiliation(s)
- Federica Murtas
- Medical Physics Department, IRCCS Regina Elena National Cancer Institute, Rome, Italy
- Department of Biomedicine and Prevention, University of Rome "Tor Vergata", Rome, Italy
| | - Valeria Landoni
- Medical Physics Department, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Pedro Ordòñez
- Medical Physics Department, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Laura Greco
- Radiology and Diagnostic Imaging Department, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Francesca Romana Ferranti
- Radiology and Diagnostic Imaging Department, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Andrea Russo
- Pathology Department, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Letizia Perracchio
- Pathology Department, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Antonello Vidiri
- Radiology and Diagnostic Imaging Department, IRCCS Regina Elena National Cancer Institute, Rome, Italy
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12
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Trieu PD(Y, Noakes J, Li T, Borecky N, Brennan PC, Barron ML, Lewis SJ. Radiologists' performance in reading digital breast tomosynthesis with and without synthesized views for cancer detection. Br J Radiol 2023; 96:20220704. [PMID: 36802348 PMCID: PMC10161913 DOI: 10.1259/bjr.20220704] [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: 07/18/2022] [Revised: 01/20/2023] [Accepted: 02/02/2023] [Indexed: 02/23/2023] Open
Abstract
OBJECTIVE The study aims to evaluate the diagnostic efficacy of radiologists and radiology trainees in digital breast tomosynthesis (DBT) alone vs DBT plus synthesized view (SV) for an understanding of the adequacy of DBT images to identify cancer lesions. METHODS Fifty-five observers (30 radiologists and 25 radiology trainees) participated in reading a set of 35 cases (15 cancer) with 28 readers reading DBT and 27 readers reading DBT plus SV. Two groups of readers had similar experience in interpreting mammograms. The performances of participants in each reading mode were compared with the ground truth and calculated in term of specificity, sensitivity, and ROC AUC. The cancer detection rate in various levels of breast density, lesion types and lesion sizes between 'DBT' and 'DBT + SV' were also analyzed. The difference in diagnostic accuracy of readers between two reading modes was assessed using Man-Whitney U test. p < 0.05 indicated a significant result. RESULTS There was no significant difference in specificity (0.67-vs-0.65; p = 0.69), sensitivity (0.77-vs-0.71; p = 0.09), ROC AUC (0.77-vs-0.73; p = 0.19) of radiologists reading DBT plus SV compared with radiologists reading DBT. Similar result was found in radiology trainees with no significant difference in specificity (0.70-vs-0.63; p = 0.29), sensitivity (0.44-vs-0.55; p = 0.19), ROC AUC (0.59-vs-0.62; p = 0.60) between two reading modes. Radiologists and trainees obtained similar results in two reading modes for cancer detection rate with different levels of breast density, cancer types and sizes of lesions (p > 0.05). CONCLUSION Findings show that the diagnostic performances of radiologists and radiology trainees in DBT alone and DBT plus SV were equivalent in identifying cancer and normal cases. ADVANCES IN KNOWLEDGE DBT alone had equivalent diagnostic accuracy as DBT plus SV which could imply the consideration of using DBT as a sole modality without SV.
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Affiliation(s)
- Phuong Dung (Yun) Trieu
- Discipline of Medical Imaging Sciences, Faculty of Medicine and Health. The University of Sydney, New South Wales, Australia
| | | | - Tong Li
- Discipline of Medical Imaging Sciences, Faculty of Medicine and Health. The University of Sydney, New South Wales, Australia
| | | | - Patrick C Brennan
- Discipline of Medical Imaging Sciences, Faculty of Medicine and Health. The University of Sydney, New South Wales, Australia
| | - Melissa L Barron
- Discipline of Medical Imaging Sciences, Faculty of Medicine and Health. The University of Sydney, New South Wales, Australia
| | - Sarah J Lewis
- Discipline of Medical Imaging Sciences, Faculty of Medicine and Health. The University of Sydney, New South Wales, Australia
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13
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Li Y, He Z, Pan J, Zeng W, Liu J, Zeng Z, Xu W, Xu Z, Wang S, Wen C, Zeng H, Wu J, Ma X, Chen W, Lu Y. Atypical architectural distortion detection in digital breast tomosynthesis: a computer-aided detection model with adaptive receptive field. Phys Med Biol 2023; 68. [PMID: 36595312 DOI: 10.1088/1361-6560/acaba7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 12/14/2022] [Indexed: 12/15/2022]
Abstract
Objective. In digital breast tomosynthesis (DBT), architectural distortion (AD) is a breast lesion that is difficult to detect. Compared with typical ADs, which have radial patterns, identifying a typical ADs is more difficult. Most existing computer-aided detection (CADe) models focus on the detection of typical ADs. This study focuses on atypical ADs and develops a deep learning-based CADe model with an adaptive receptive field in DBT.Approach. Our proposed model uses a Gabor filter and convergence measure to depict the distribution of fibroglandular tissues in DBT slices. Subsequently, two-dimensional (2D) detection is implemented using a deformable-convolution-based deep learning framework, in which an adaptive receptive field is introduced to extract global features in slices. Finally, 2D candidates are aggregated to form the three-dimensional AD detection results. The model is trained on 99 positive cases with ADs and evaluated on 120 AD-positive cases and 100 AD-negative cases.Main results. A convergence-measure-based model and deep-learning model without an adaptive receptive field are reproduced as controls. Their mean true positive fractions (MTPF) ranging from 0.05 to 4 false positives per volume are 0.3846 ± 0.0352 and 0.6501 ± 0.0380, respectively. Our proposed model achieves an MTPF of 0.7148 ± 0.0322, which is a significant improvement (p< 0.05) compared with the other two methods. In particular, our model detects more atypical ADs, primarily contributing to the performance improvement.Significance. The adaptive receptive field helps the model improve the atypical AD detection performance. It can help radiologists identify more ADs in breast cancer screening.
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Affiliation(s)
- Yue Li
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, People's Republic of China.,Guangdong Province Key Laboratory of Computational Science, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Zilong He
- Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, People's Republic of China
| | - Jiawei Pan
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, People's Republic of China.,Guangdong Province Key Laboratory of Computational Science, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Weixiong Zeng
- Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, People's Republic of China
| | - Jialing Liu
- Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, People's Republic of China
| | - Zhaodong Zeng
- Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, People's Republic of China
| | - Weimin Xu
- Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, People's Republic of China
| | - Zeyuan Xu
- Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, People's Republic of China
| | - Sina Wang
- Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, People's Republic of China
| | - Chanjuan Wen
- Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, People's Republic of China
| | - Hui Zeng
- Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, People's Republic of China
| | - Jiefang Wu
- Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, People's Republic of China
| | - Xiangyuan Ma
- Department of Biomedical Engineering, College of Engineering, Shantou University, Shantou, People's Republic of China.,Guangdong Province Key Laboratory of Computational Science, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Weiguo Chen
- Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, People's Republic of China
| | - Yao Lu
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, People's Republic of China.,Guangdong Province Key Laboratory of Computational Science, Sun Yat-sen University, Guangzhou, People's Republic of China.,Shanghai Key Laboratory of Molecular Imaging, Shanghai University of Medicine and Health Sciences, Shanghai, People's Republic of China
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14
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Ido M, Saito M, Banno H, Ito Y, Goto M, Ando T, Kousaka J, Mouri Y, Fujii K, Imai T, Nakano S, Suzuki K, Murotani K. Clinical performance of digital breast tomosynthesis-guided vacuum-assisted biopsy: a single-institution experience in Japan. BMC Med Imaging 2023; 23:2. [PMID: 36604648 PMCID: PMC9817251 DOI: 10.1186/s12880-022-00896-1] [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: 05/27/2022] [Accepted: 09/12/2022] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND The purpose of this study was to evaluate the clinical performance of Digital Breast Tomosynthesis guided vacuum-assisted biopsy (DBT-VAB) for microcalcifications in the breast. METHODS Retrospective review of 131 mammography-guided VABs at our institution were performed. All of the targets were calcification lesion suspicious for cancer. 45 consecutive stereotactic vacuum-assisted biopsies (ST-VABs) and 86 consecutive DBT-VABs were compared. Written informed consent was obtained. Tissue sampling methods and materials were the same with both systems. Student's t-test was used to compare procedure time and the Fisher's exact test was used to compare success rate, complications, and histopathologic findings for the 2 methods. RESULTS The tissue sampling success rate was 95.6% for ST-VAB (43/45) and 97.7% (84/86) for DBT-VAB. Time for positioning (10.6 ± 6.4 vs. 6.7 ± 5.3 min), time for biopsy (33.4 ± 13.1 vs. 22.5 ± 13.1 min), and overall procedure time (66.6 ± 16.6 min vs. 54.5 ± 13.0 min) were substantially shorter with DBT-VAB (P < 0.0001). There were no differences in the distribution of pathological findings between the 2 groups. CONCLUSION Depth information and stable visibility of the target provided by DBT images led to quick decisions about target coordinates and improved the clinical performance of microcalcification biopsies.
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Affiliation(s)
- Mirai Ido
- grid.411234.10000 0001 0727 1557Division of Breast and Endocrine Surgery, Department of Surgery, Aichi Medical University, 1-1 Yazakokarimata, Nagakute-City, Aichi 480-1195 Japan
| | - Masayuki Saito
- grid.411234.10000 0001 0727 1557Division of Breast and Endocrine Surgery, Department of Surgery, Aichi Medical University, 1-1 Yazakokarimata, Nagakute-City, Aichi 480-1195 Japan
| | - Hirona Banno
- grid.411234.10000 0001 0727 1557Division of Breast and Endocrine Surgery, Department of Surgery, Aichi Medical University, 1-1 Yazakokarimata, Nagakute-City, Aichi 480-1195 Japan
| | - Yukie Ito
- grid.411234.10000 0001 0727 1557Division of Breast and Endocrine Surgery, Department of Surgery, Aichi Medical University, 1-1 Yazakokarimata, Nagakute-City, Aichi 480-1195 Japan
| | - Manami Goto
- grid.411234.10000 0001 0727 1557Division of Breast and Endocrine Surgery, Department of Surgery, Aichi Medical University, 1-1 Yazakokarimata, Nagakute-City, Aichi 480-1195 Japan
| | - Takahito Ando
- grid.411234.10000 0001 0727 1557Division of Breast and Endocrine Surgery, Department of Surgery, Aichi Medical University, 1-1 Yazakokarimata, Nagakute-City, Aichi 480-1195 Japan
| | - Junko Kousaka
- grid.411234.10000 0001 0727 1557Division of Breast and Endocrine Surgery, Department of Surgery, Aichi Medical University, 1-1 Yazakokarimata, Nagakute-City, Aichi 480-1195 Japan
| | - Yukako Mouri
- grid.411234.10000 0001 0727 1557Division of Breast and Endocrine Surgery, Department of Surgery, Aichi Medical University, 1-1 Yazakokarimata, Nagakute-City, Aichi 480-1195 Japan
| | - Kimihito Fujii
- grid.411234.10000 0001 0727 1557Division of Breast and Endocrine Surgery, Department of Surgery, Aichi Medical University, 1-1 Yazakokarimata, Nagakute-City, Aichi 480-1195 Japan
| | - Tsuneo Imai
- grid.411234.10000 0001 0727 1557Division of Breast and Endocrine Surgery, Department of Surgery, Aichi Medical University, 1-1 Yazakokarimata, Nagakute-City, Aichi 480-1195 Japan
| | - Shogo Nakano
- grid.411234.10000 0001 0727 1557Division of Breast and Endocrine Surgery, Department of Surgery, Aichi Medical University, 1-1 Yazakokarimata, Nagakute-City, Aichi 480-1195 Japan
| | - Kojiro Suzuki
- grid.411234.10000 0001 0727 1557Department of Radiology, Aichi Medical University, 1-1 Yazakokarimata, Nagakute-City, Aichi 480-1195 Japan
| | - Kenta Murotani
- grid.410781.b0000 0001 0706 0776Biostatistic Center, Graduate School of Medicine, Kurume University, 67 Asahi-machi Kurume, Fukuoka, 80-0011 Japan
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15
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Marshall NW, Bosmans H. Performance evaluation of digital breast tomosynthesis systems: physical methods and experimental data. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac9a35] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 10/13/2022] [Indexed: 11/17/2022]
Abstract
Abstract
Digital breast tomosynthesis (DBT) has become a well-established breast imaging technique, whose performance has been investigated in many clinical studies, including a number of prospective clinical trials. Results from these studies generally point to non-inferiority in terms of microcalcification detection and superior mass-lesion detection for DBT imaging compared to digital mammography (DM). This modality has become an essential tool in the clinic for assessment and ad-hoc screening but is not yet implemented in most breast screening programmes at a state or national level. While evidence on the clinical utility of DBT has been accumulating, there has also been progress in the development of methods for technical performance assessment and quality control of these imaging systems. DBT is a relatively complicated ‘pseudo-3D’ modality whose technical assessment poses a number of difficulties. This paper reviews methods for the technical performance assessment of DBT devices, starting at the component level in part one and leading up to discussion of system evaluation with physical test objects in part two. We provide some historical and basic theoretical perspective, often starting from methods developed for DM imaging. Data from a multi-vendor comparison are also included, acquired under the medical physics quality control protocol developed by EUREF and currently being consolidated by a European Federation of Organisations for Medical Physics working group. These data and associated methods can serve as a reference for the development of reference data and provide some context for clinical studies.
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16
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Chae EY, Cha JH, Shin HJ, Choi WJ, Kim J, Kim SM, Kim HH. [Patterns in the Use and Perception of Digital Breast Tomosynthesis: A Survey of Korean Breast Radiologists]. JOURNAL OF THE KOREAN SOCIETY OF RADIOLOGY 2022; 83:1327-1341. [PMID: 36545425 PMCID: PMC9748450 DOI: 10.3348/jksr.2021.0162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Revised: 11/11/2021] [Accepted: 02/10/2022] [Indexed: 11/18/2022]
Abstract
Purpose To evaluate the pattern of use and the perception of digital breast tomosynthesis (DBT) among Korean breast radiologists. Materials and Methods From March 22 to 29, 2021, an online survey comprising 27 questions was sent to members of the Korean Society of Breast Imaging. Questions related to practice characteristics, utilization and perception of DBT, and research interests. Results were analyzed based on factors using logistic regression. Results Overall, 120 of 257 members responded to the survey (response rate, 46.7%), 67 (55.8%) of whom reported using DBT. The overall satisfaction with DBT was 3.31 (1-5 scale). The most-cited DBT advantages were decreased recall rate (55.8%), increased lesion conspicuity (48.3%), and increased cancer detection (45.8%). The most-cited DBT disadvantages were extra cost for patients (46.7%), insufficient calcification characterization (43.3%), insufficient improvement in diagnostic performance (39.2%), and radiation dose (35.8%). Radiologists reported increased storage requirements and interpretation time for barriers to implementing DBT. Conclusion Further improvement of DBT techniques reflecting feedback from the user's perspective will help increase the acceptance of DBT in Korea.
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17
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Neubauer C, Yilmaz JS, Bronsert P, Pichotka M, Bamberg F, Windfuhr-Blum M, Erbes T, Neubauer J. Accuracy of cone-beam computed tomography, digital mammography and digital breast tomosynthesis for microcalcifications and margins to microcalcifications in breast specimens. Sci Rep 2022; 12:17639. [PMID: 36271228 PMCID: PMC9587219 DOI: 10.1038/s41598-022-21616-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 09/29/2022] [Indexed: 01/18/2023] Open
Abstract
Accurate determination of resection margins in breast specimens is important as complete removal of malignancy is a prerequisite for patients' outcome. Mammography (DM) as 2D-technique provides only limited value in margin assessment. Therefore, we investigated whether cone-beam computed tomography (CBCT) or digital breast tomosynthesis (DBT) has incremental value in assessing margins to microcalcifications. Three independent readers investigated breast specimens for presence of microcalcifications and the smallest distance to margins. Histopathology served as gold standard. Microcalcifications were detected in 15 out of 21 included specimens (71%). Pooled sensitivity for DM, DBT and CBCT for microcalcifications compared to preoperative DM was 0.98 (CI 0.94-0.99), 0.83 (CI 0.73-0.94) and 0.94 (CI 0.87-0.99), pooled specificity was 0.99 (CI 0.99-0.99), 0.73 (CI 0.51-0.96) and 0.60 (CI 0.35-0.85). Mean measurement error for margin determination for DM, DBT and CBCT was 10 mm, 14 mm and 6 mm (p = 0.002) with significant difference between CBCT and the other devices (p < 0.03). Mean reading time required by the readers to analyze DM, DBT and CBCT, was 36, 43 and 54 s (p < 0.001). Although DM allows reliable detection of microcalcifications, measurement of resection margin was significantly more accurate with CBCT. Thus, a combination of methods or improved CBCT might provide a more accurate determination of disease-free margins in breast specimens.
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Affiliation(s)
- Claudia Neubauer
- grid.5963.9Department of Radiology, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany
| | - Jannina Samantha Yilmaz
- grid.5963.9Department of Radiology, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany
| | - Peter Bronsert
- grid.5963.9Institute for Surgical Pathology, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany ,grid.5963.9Tumorbank Comprehensive Cancer Center Freiburg, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg Im Breisgau, Germany ,grid.5963.9Core Facility for Histopathology and Digital Pathology, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg Im Breisgau, Germany
| | - Martin Pichotka
- grid.5963.9Medical Physics, Department of Radiology, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany
| | - Fabian Bamberg
- grid.5963.9Department of Radiology, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany
| | - Marisa Windfuhr-Blum
- grid.5963.9Department of Radiology, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany
| | - Thalia Erbes
- grid.5963.9Department of Obstetrics and Gynecology, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany
| | - Jakob Neubauer
- grid.5963.9Department of Radiology, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany
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18
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Local Diagnostic Reference Levels for Full-Field Digital Mammography and Digital Breast Tomosynthesis in a Tertiary Hospital in Malaysia. Healthcare (Basel) 2022; 10:healthcare10101917. [PMID: 36292364 PMCID: PMC9601326 DOI: 10.3390/healthcare10101917] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 09/08/2022] [Accepted: 09/16/2022] [Indexed: 11/16/2022] Open
Abstract
A set of national diagnostic reference levels (DRLs) was established in Malaysia for a range of breast thicknesses in 2013, but no updates for full-field digital mammography (FFDM) and digital breast tomosynthesis (DBT). Due to the increasing number of DBTs used and concern over radiation exposure, this study aimed to explore and establish local diagnostic reference levels for FFDM and DBT in Malaysia health facilities at different compressed breast thickness (CBT) ranges. The CBT, kilovoltage peak (kVp), Entrance surface dose (ESD), and average glandular dose (AGD) were retrospectively extracted from the mammography Digital Imaging and Communications in Medicine (DICOM) header. The 75th and 95th percentile values were obtained for the AGD distribution of each mammography projection for three sets of CBT range. The difference in AGD values between FFDM and DBT at three CBT ranges was determined. The DRLs for FFDM were 1.13 mGy, 1.52 mGy, and 2.87 mGy, while DBT were 1.18 mGy, 1.88 mGy, and 2.78 mGy at CBT ranges of 20−39 mm, 40−59 mm, and 60−99 mm, respectively. The AGD of DBT was significantly higher than FFDM for both mammographic views (p < 0.005). All three CBT groups showed a significant difference in AGD values for FFDM and DBT (p < 0.005). The local DRLs from this study were lower than the national DRLs, with the AGD of FFDM significantly lower than DBT.
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19
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Lesion-specific exposure parameters for breast cancer diagnosis on digital breast tomosynthesis and full-field digital mammography. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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20
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Modern Diagnostic Imaging Technique Applications and Risk Factors in the Medical Field: A Review. BIOMED RESEARCH INTERNATIONAL 2022; 2022:5164970. [PMID: 35707373 PMCID: PMC9192206 DOI: 10.1155/2022/5164970] [Citation(s) in RCA: 39] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 05/25/2022] [Indexed: 11/18/2022]
Abstract
Medical imaging is the process of visual representation of different tissues and organs of the human body to monitor the normal and abnormal anatomy and physiology of the body. There are many medical imaging techniques used for this purpose such as X-ray, computed tomography (CT), positron emission tomography (PET), magnetic resonance imaging (MRI), single-photon emission computed tomography (SPECT), digital mammography, and diagnostic sonography. These advanced medical imaging techniques have many applications in the diagnosis of myocardial diseases, cancer of different tissues, neurological disorders, congenital heart disease, abdominal illnesses, complex bone fractures, and other serious medical conditions. There are benefits as well as some risks to every imaging technique. There are some steps for minimizing the radiation exposure risks from imaging techniques. Advance medical imaging modalities such as PET/CT hybrid, three-dimensional ultrasound computed tomography (3D USCT), and simultaneous PET/MRI give high resolution, better reliability, and safety to diagnose, treat, and manage complex patient abnormalities. These techniques ensure the production of new accurate imaging tools with improving resolution, sensitivity, and specificity. In the future, with mounting innovations and advancements in technology systems, the medical diagnostic field will become a field of regular measurement of various complex diseases and will provide healthcare solutions.
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21
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Heindel W, Weigel S, Gerß J, Hense HW, Sommer A, Krischke M, Kerschke L. Digital breast tomosynthesis plus synthesised mammography versus digital screening mammography for the detection of invasive breast cancer (TOSYMA): a multicentre, open-label, randomised, controlled, superiority trial. Lancet Oncol 2022; 23:601-611. [PMID: 35427470 DOI: 10.1016/s1470-2045(22)00194-2] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 03/11/2022] [Accepted: 03/18/2022] [Indexed: 12/22/2022]
Abstract
BACKGROUND Two dimensional (2D) full-field digital mammography is the current standard of breast cancer screening. Digital breast tomosynthesis generates pseudo-three dimensional datasets of the breast from which synthesised 2D (s2D) mammograms can be reconstructed. This innovative approach reduces the likelihood of overlapping breast tissues that can conceal features of malignancy. We aimed to compare digital breast tomosynthesis plus s2D mammography with digital screening mammography for the detection of invasive breast cancer. METHODS TOSYMA was a randomised, open-label, superiority trial done at 17 screening units in two federal states of Germany. Eligible participants were women aged 50-69 years who had been invited to participate in a population-wide, quality-controlled mammography screening programme. Women were randomly assigned (1:1) to digital breast tomosynthesis plus s2D mammography or digital mammography alone using block randomisation (block size of 32), stratified by site. The primary endpoints were the detection rate of invasive breast cancer and invasive interval cancer rate at 24 months, analysed in the modified full analysis set, which included all randomly assigned participants who underwent either type of screening examination. Ten examinations, corresponding to a second study participation, were excluded. Analyses were done according to the intention-to-treat principle. Interval cancer rates will be reported in the follow-up study. Safety was assessed in the as-treated population, which included all participants who were randomly assigned. This trial is registered with ClinicalTrials.gov, NCT03377036, and is closed to accrual. FINDINGS Between July 5, 2018, and Dec 30, 2020, 99 689 women were randomly assigned to digital breast tomosynthesis plus s2D mammography (n=49 804) or digital mammography (n=49 830). Invasive breast cancers were detected in 354 of 49 715 women with evaluable primary endpoint data in the digital breast tomosynthesis plus s2D group (detection rate 7·1 cases per 1000 women screened) and in 240 of 49 762 women in the digital mammography group (4·8 cases per 1000 women screened; odds ratio 1·48 [95% CI 1·25-1·75]; p<0·0001). Adverse events and device deficiencies were rare (six adverse events in each group; 23 device deficiencies in the digital breast tomosynthesis plus s2D group vs five device deficiencies in the digital mammography group) and no serious adverse events were reported. INTERPRETATION The results from this study indicate that the detection rate for invasive breast cancer was significantly higher with digital breast tomosynthesis plus s2D mammography than digital mammography alone. Evaluation of interval cancer rates in the follow-up study will further help to investigate incremental long-term benefits of digital breast tomosynthesis screening. FUNDING Deutsche Forschungsgemeinschaft (German Research Foundation).
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Affiliation(s)
- Walter Heindel
- Clinic for Radiology and Reference Center for Mammography Münster, University of Münster and University Hospital Münster, Münster, Germany.
| | - Stefanie Weigel
- Clinic for Radiology and Reference Center for Mammography Münster, University of Münster and University Hospital Münster, Münster, Germany
| | - Joachim Gerß
- Institute of Biostatistics and Clinical Research, University of Münster and University Hospital Münster, Münster, Germany
| | - Hans-Werner Hense
- Institute of Epidemiology and Social Medicine, University of Münster and University Hospital Münster, Münster, Germany
| | - Alexander Sommer
- Clinic for Radiology and Reference Center for Mammography Münster, University of Münster and University Hospital Münster, Münster, Germany
| | - Miriam Krischke
- Centre for Clinical Trials Münster, University of Münster and University Hospital Münster, Münster, Germany
| | - Laura Kerschke
- Institute of Biostatistics and Clinical Research, University of Münster and University Hospital Münster, Münster, Germany
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22
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Opitz M, Zensen S, Breuckmann K, Bos D, Forsting M, Hoffmann O, Stuschke M, Wetter A, Guberina N. Breast Radiation Exposure of 3D Digital Breast Tomosynthesis Compared to Full-Field Digital Mammography in a Clinical Follow-Up Setting. Diagnostics (Basel) 2022; 12:diagnostics12020456. [PMID: 35204547 PMCID: PMC8871344 DOI: 10.3390/diagnostics12020456] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 01/30/2022] [Accepted: 02/03/2022] [Indexed: 02/04/2023] Open
Abstract
According to a position paper of the European Commission Initiative on Breast Cancer (ECIBC), DBT is close to being introduced in European breast cancer screening programmes. Our study aimed to examine radiation dose delivered by digital breast tomosynthesis (DBT) and digital mammography (FFDM) in comparison to sole FFDM in a clinical follow-up setting and in an identical patient cohort. Retrospectively, 768 breast examinations of 96 patients were included. Patients received both DBT and FFDM between May 2015 and July 2019: (I) FFDM in cranio-caudal (CC) and DBT in mediolateral oblique (MLO) view, as well as a (II) follow-up examination with FFDM in CC and MLO view. The mean glandular dose (MGD) was determined by the mammography system according to Dance’s model. The MGD (standard deviation (SD), interquartile range (IQR)) was distributed as follows: (I) (CCFFDM+MLODBT) (a) left FFDMCC 1.40 mGy (0.36 mGy, 1.13–1.59 mGy), left DBTMLO 1.62 mGy (0.51 mGy, 1.27–1.82 mGy); (b) right FFDMCC 1.36 mGy (0.34 mGy, 1.14–1.51 mGy), right DBTMLO 1.59 mGy (0.52 mGy, 1.27–1.62 mGy). (II) (CCFFDM+MLOFFDM) (a) left FFDMCC 1.35 mGy (0.35 mGy, 1.10–1.60 mGy), left FFDMMLO 1.40 mGy (0.39 mGy, 1.12–1.59 mGy), (b) right FFDMCC 1.35 mGy (0.33 mGy, 1.12–1.48 mGy), right FFDMMLO 1.40 mGy (0.36 mGy, 1.14–1.58 mGy). MGD was significantly higher for DBT mlo views compared to FFDM (p < 0.001). Radiation dose was significantly higher for DBT in MLO views compared to FFDM. However, the MGD of DBT MLO lies below the national diagnostic reference level of 2 mGy for an FFDM view. Hence, our results support the use of either DBT or FFDM as suggested in the ECIBC’s Guidelines.
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Affiliation(s)
- Marcel Opitz
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, 45147 Essen, Germany; (K.B.); (D.B.); (M.F.); (A.W.); (N.G.)
- Correspondence: (M.O.); (S.Z.)
| | - Sebastian Zensen
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, 45147 Essen, Germany; (K.B.); (D.B.); (M.F.); (A.W.); (N.G.)
- Correspondence: (M.O.); (S.Z.)
| | - Katharina Breuckmann
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, 45147 Essen, Germany; (K.B.); (D.B.); (M.F.); (A.W.); (N.G.)
| | - Denise Bos
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, 45147 Essen, Germany; (K.B.); (D.B.); (M.F.); (A.W.); (N.G.)
| | - Michael Forsting
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, 45147 Essen, Germany; (K.B.); (D.B.); (M.F.); (A.W.); (N.G.)
| | - Oliver Hoffmann
- Department of Obstetrics and Gynecology, University Hospital Essen, 45147 Essen, Germany;
| | - Martin Stuschke
- West German Cancer Center, Department of Radiotherapy, University Hospital Essen, 45147 Essen, Germany;
| | - Axel Wetter
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, 45147 Essen, Germany; (K.B.); (D.B.); (M.F.); (A.W.); (N.G.)
- Department of Diagnostic and Interventional Radiology, Neuroradiology, Asklepios Klinikum Harburg, 21075 Hamburg, Germany
| | - Nika Guberina
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, 45147 Essen, Germany; (K.B.); (D.B.); (M.F.); (A.W.); (N.G.)
- West German Cancer Center, Department of Radiotherapy, University Hospital Essen, 45147 Essen, Germany;
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23
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Mackenzie A, Thomson EL, Mitchell M, Elangovan P, van Ongeval C, Cockmartin L, Warren LM, Wilkinson LS, Wallis MG, Given-Wilson RM, Dance DR, Young KC. Virtual clinical trial to compare cancer detection using combinations of 2D mammography, digital breast tomosynthesis and synthetic 2D imaging. Eur Radiol 2022; 32:806-814. [PMID: 34331118 DOI: 10.1007/s00330-021-08197-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 06/07/2021] [Accepted: 07/01/2021] [Indexed: 11/28/2022]
Abstract
OBJECTIVES This study was designed to compare the detection of subtle lesions (calcification clusters or masses) when using the combination of digital breast tomosynthesis (DBT) and synthetic mammography (SM) with digital mammography (DM) alone or combined with DBT. METHODS A set of 166 cases without cancer was acquired on a DBT mammography system. Realistic subtle calcification clusters and masses in the DM images and DBT planes were digitally inserted into 104 of the acquired cases. Three study arms were created: DM alone, DM with DBT and SM with DBT. Five mammographic readers located the centre of any lesion within the images that should be recalled for further investigation and graded their suspiciousness. A JAFROC figure of merit (FoM) and lesion detection fraction (LDF) were calculated for each study arm. The visibility of the lesions in the DBT images was compared with SM and DM images. RESULTS For calcification clusters, there were no significant differences (p > 0.075) in FoM or LDF. For masses, the FoM and LDF were significantly improved in the arms using DBT compared to DM alone (p < 0.001). On average, both calcification clusters and masses were more visible on DBT than on DM and SM images. CONCLUSIONS This study demonstrated that masses were detected better with DBT than with DM alone and there was no significant difference (p = 0.075) in LDF between DM&DBT and SM&DBT for calcifications clusters. Our results support previous studies that it may be acceptable to not acquire digital mammography alongside tomosynthesis for subtle calcification clusters and ill-defined masses. KEY POINTS • The detection of masses was significantly better using DBT than with digital mammography alone. • The detection of calcification clusters was not significantly different between digital mammography and synthetic 2D images combined with tomosynthesis. • Our results support previous studies that it may be acceptable to not acquire digital mammography alongside tomosynthesis for subtle calcification clusters and ill-defined masses for the imaging technology used.
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Affiliation(s)
- Alistair Mackenzie
- National Coordinating Centre for the Physics in Mammography, Royal Surrey NHS Foundation Trust, Guildford, UK.
| | - Emma L Thomson
- National Coordinating Centre for the Physics in Mammography, Royal Surrey NHS Foundation Trust, Guildford, UK
- Department of Physics, University of Surrey, Guildford, UK
| | - Melissa Mitchell
- National Coordinating Centre for the Physics in Mammography, Royal Surrey NHS Foundation Trust, Guildford, UK
- Department of Physics, University of Surrey, Guildford, UK
| | - Premkumar Elangovan
- National Coordinating Centre for the Physics in Mammography, Royal Surrey NHS Foundation Trust, Guildford, UK
| | | | - Lesley Cockmartin
- Department of Imaging and Pathology, Division of Medical Physics and Quality Assessment, KU Leuven, Herestraat 49, B-3000, Leuven, Belgium
| | - Lucy M Warren
- National Coordinating Centre for the Physics in Mammography, Royal Surrey NHS Foundation Trust, Guildford, UK
| | - Louise S Wilkinson
- Oxford Breast Imaging Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Matthew G Wallis
- Cambridge Breast Unit, Cambridge University Hospitals NHS Foundation Trust, Cambridge & NIHR Cambridge Biomedical Research Centre, Cambridge, UK
| | | | - David R Dance
- National Coordinating Centre for the Physics in Mammography, Royal Surrey NHS Foundation Trust, Guildford, UK
- Department of Physics, University of Surrey, Guildford, UK
| | - Kenneth C Young
- National Coordinating Centre for the Physics in Mammography, Royal Surrey NHS Foundation Trust, Guildford, UK
- Department of Physics, University of Surrey, Guildford, UK
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Chan HP, Helvie MA, Klein KA, McLaughlin C, Neal CH, Oudsema R, Rahman WT, Roubidoux MA, Hadjiiski LM, Zhou C, Samala RK. Effect of Dose Level on Radiologists' Detection of Microcalcifications in Digital Breast Tomosynthesis: An Observer Study with Breast Phantoms. Acad Radiol 2022; 29 Suppl 1:S42-S49. [PMID: 32950384 DOI: 10.1016/j.acra.2020.07.038] [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: 05/19/2020] [Revised: 07/30/2020] [Accepted: 07/30/2020] [Indexed: 11/16/2022]
Abstract
OBJECTIVES To compare radiologists' sensitivity, confidence level, and reading efficiency of detecting microcalcifications in digital breast tomosynthesis (DBT) at two clinically relevant dose levels. MATERIALS AND METHODS Six 5-cm-thick heterogeneous breast phantoms embedded with a total of 144 simulated microcalcification clusters of four speck sizes were imaged at two dose modes by a clinical DBT system. The DBT volumes at the two dose levels were read independently by six MQSA radiologists and one fellow with 1-33 years (median 12 years) of experience in a fully-crossed counter-balanced manner. The radiologist located each potential cluster and rated its conspicuity and his/her confidence that the marked location contained a cluster. The differences in the results between the two dose modes were analyzed by two-tailed paired t-test. RESULTS Compared to the lower-dose mode, the average glandular dose in the higher-dose mode for the 5-cm phantoms increased from 1.34 to 2.07 mGy. The detection sensitivity increased for all speck sizes and significantly for the two smaller sizes (p <0.05). An average of 13.8% fewer false positive clusters was marked. The average conspicuity rating and the radiologists' confidence level were higher for all speck sizes and reached significance (p <0.05) for the three larger sizes. The average reading time per detected cluster reduced significantly (p <0.05) by an average of 13.2%. CONCLUSION For a 5-cm-thick breast, an increase in average glandular dose from 1.34 to 2.07 mGy for DBT imaging increased the conspicuity of microcalcifications, improved the detection sensitivity by radiologists, increased their confidence levels, reduced false positive detections, and increased the reading efficiency.
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Affiliation(s)
- Heang-Ping Chan
- Department of Radiology, University of Michigan, 1500 E. Medical Center Dr., Med Inn Building C477, Ann Arbor, MI 48109-5842.
| | - Mark A Helvie
- Department of Radiology, University of Michigan, 1500 E. Medical Center Dr., Med Inn Building C477, Ann Arbor, MI 48109-5842
| | - Katherine A Klein
- Department of Radiology, University of Michigan, 1500 E. Medical Center Dr., Med Inn Building C477, Ann Arbor, MI 48109-5842
| | - Carol McLaughlin
- Department of Radiology, University of Michigan, 1500 E. Medical Center Dr., Med Inn Building C477, Ann Arbor, MI 48109-5842
| | - Colleen H Neal
- Department of Radiology, University of Michigan, 1500 E. Medical Center Dr., Med Inn Building C477, Ann Arbor, MI 48109-5842
| | - Rebecca Oudsema
- Department of Radiology, University of Michigan, 1500 E. Medical Center Dr., Med Inn Building C477, Ann Arbor, MI 48109-5842
| | - W Tania Rahman
- Department of Radiology, University of Michigan, 1500 E. Medical Center Dr., Med Inn Building C477, Ann Arbor, MI 48109-5842
| | - Marilyn A Roubidoux
- Department of Radiology, University of Michigan, 1500 E. Medical Center Dr., Med Inn Building C477, Ann Arbor, MI 48109-5842
| | - Lubomir M Hadjiiski
- Department of Radiology, University of Michigan, 1500 E. Medical Center Dr., Med Inn Building C477, Ann Arbor, MI 48109-5842
| | - Chuan Zhou
- Department of Radiology, University of Michigan, 1500 E. Medical Center Dr., Med Inn Building C477, Ann Arbor, MI 48109-5842
| | - Ravi K Samala
- Department of Radiology, University of Michigan, 1500 E. Medical Center Dr., Med Inn Building C477, Ann Arbor, MI 48109-5842
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Agarwal R, Yap MH, Hasan MK, Zwiggelaar R, Martí R. Deep Learning in Mammography Breast Cancer Detection. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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26
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Cesário GJ, Paixão L, Santos R, Chevalier M, Attie MRP, Nogueira MS, Souza DN. Proposal of an algorithm to evaluate geometric distortion and artifact spreading in digital breast tomosynthesis. Acta Radiol 2021; 63:1344-1352. [PMID: 34797750 DOI: 10.1177/02841851211041823] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND According to the European Reference Organization for Quality Assurance Breast Screening and European Diagnostic Services, the spatial accuracy of reconstructed images and reconstruction artifacts must be evaluated in digital breast tomosynthesis (DBT) quality control procedures. PURPOSE To propose a computational algorithm to evaluate the geometric distortion and artifact spreading (GDAS) in DBT images. MATERIAL AND METHODS The proposed algorithm analyzed tomosynthesis images of a phantom that contains aluminum spheres (1 mm in diameter) arranged in a rectangular matrix spaced 5 cm apart that was inserted in 5-mm-thick polymethylmethacrylate (PMMA). RESULTS The obtained results were compared with the values provided by the algorithm developed by the National Coordinating Center for the Physics of Mammography (NCCPM). In the comparison, the results depended on the dimensions of the region of interest (ROI). This dependence proves the benefit of the proposed algorithm because it allows the user to select the ROI. CONCLUSION The computational algorithm proved to be useful for the evaluation of GDAS in DBT images, in the same way as the reference algorithm (NCCPM), as well as allowing the selection of the ROI dimensions that best suit the spreading of the artifact in the analyzed images.
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Affiliation(s)
- Greiciane J Cesário
- Departamento de Física, Universidade Federal de Sergipe (UFS), São Cristóvão, SE, Brazil
| | - Lucas Paixão
- Departamento de Ciências da Computação, Universidade Federal de Sergipe (UFS), São Cristóvão, SE, Brazil
| | - Reneilson Santos
- Centro de Desenvolvimento de Pesquisa Nuclear –CDTN/CNEN, Belo Horizonte, MG, Brazil
| | - Margarita Chevalier
- Medical Physics Group, Radiology, Rehabilitation and Physiotherapy Department, Complutense University of Madrid, Madrid, Spain
| | - Márcia RP Attie
- Departamento de Física, Universidade Federal de Sergipe (UFS), São Cristóvão, SE, Brazil
| | - Maria S Nogueira
- Centro de Desenvolvimento de Pesquisa Nuclear –CDTN/CNEN, Belo Horizonte, MG, Brazil
| | - Divanizia N Souza
- Departamento de Física, Universidade Federal de Sergipe (UFS), São Cristóvão, SE, Brazil
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Gilbert FJ, Hickman SE, Baxter GC, Allajbeu I, James J, Caraco C, Vinnicombe S. Opportunities in cancer imaging: risk-adapted breast imaging in screening. Clin Radiol 2021; 76:763-773. [PMID: 33820637 DOI: 10.1016/j.crad.2021.02.013] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Accepted: 02/19/2021] [Indexed: 12/17/2022]
Abstract
In the UK, women between 50-70 years are invited for 3-yearly mammography screening irrespective of their likelihood of developing breast cancer. The only risk adaption is for women with >30% lifetime risk who are offered annual magnetic resonance imaging (MRI) and mammography, and annual mammography for some moderate-risk women. Using questionnaires, breast density, and polygenic risk scores, it is possible to stratify the population into the lowest 20% risk, who will develop <4% of cancers and the top 4%, who will develop 18% of cancers. Mammography is a good screening test but has low sensitivity of 60% in the 9% of women with the highest category of breast density (BIRADS D) who have a 2.5- to fourfold breast cancer risk. There is evidence that adding ultrasound to the screening mammogram can increase the cancer detection rate and reduce advanced stage interval and next round cancers. Similarly, alternative tests such as contrast-enhanced mammography (CESM) or abbreviated MRI (ABB-MRI) are much more effective in detecting cancer in women with dense breasts. Scintimammography has been shown to be a viable alternative for dense breasts or for follow-up in those with a personal history of breast cancer and scarring as result of treatment. For supplemental screening to be worthwhile in these women, new technologies need to reduce the number of stage II cancers and be cost effective when tested in large scale trials. This article reviews the evidence for supplemental imaging and examines whether a risk-stratified approach is feasible.
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Affiliation(s)
- F J Gilbert
- Department of Radiology, University of Cambridge School of Clinical Medicine, Box 218, Level 5, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK; Department of Radiology, Addenbrookes Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
| | - S E Hickman
- Department of Radiology, University of Cambridge School of Clinical Medicine, Box 218, Level 5, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
| | - G C Baxter
- Department of Radiology, University of Cambridge School of Clinical Medicine, Box 218, Level 5, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
| | - I Allajbeu
- Department of Radiology, University of Cambridge School of Clinical Medicine, Box 218, Level 5, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK; Department of Radiology, Addenbrookes Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - J James
- Nottingham Breast Institute, City Hospital, Nottingham, UK
| | - C Caraco
- Department of Radiology, University of Cambridge School of Clinical Medicine, Box 218, Level 5, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
| | - S Vinnicombe
- Thirlestaine Breast Centre, Cheltenham, UK; Ninewells Hospital and Medical School, University of Dundee, UK
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Niu S, Wang X, Zhao N, Liu G, Kan Y, Dong Y, Cui EN, Luo Y, Yu T, Jiang X. Radiomic Evaluations of the Diagnostic Performance of DM, DBT, DCE MRI, DWI, and Their Combination for the Diagnosisof Breast Cancer. Front Oncol 2021; 11:725922. [PMID: 34568055 PMCID: PMC8461299 DOI: 10.3389/fonc.2021.725922] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Accepted: 08/23/2021] [Indexed: 12/29/2022] Open
Abstract
Objectives This study aims to evaluate digital mammography (DM), digital breast tomosynthesis (DBT), dynamic contrast-enhanced (DCE), and diffusion-weighted (DW) MRI, individually and combined, for the values in the diagnosis of breast cancer, and propose a visualized clinical-radiomics nomogram for potential clinical uses. Methods A total of 120 patients were enrolled between September 2017 and July 2018, all underwent preoperative DM, DBT, DCE, and DWI scans. Radiomics features were extracted and selected using the least absolute shrinkage and selection operator (LASSO) regression. A radiomics nomogram was constructed integrating the radiomics signature and important clinical predictors, and assessed with the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). Results The radiomics signature derived from DBT plus DM generated a lower area under the ROC curve (AUC) and sensitivity, but a higher specificity compared with that from DCE plus DWI. The nomogram integrating the combined radiomics signature, age, and menstruation status achieved the best diagnostic performance in the training (AUCs, nomogram vs. combined radiomics signature vs. clinical model, 0.975 vs. 0.964 vs. 0.782) and validation (AUCs, nomogram vs. combined radiomics signature vs. clinical model, 0.983 vs. 0.978 vs. 0.680) cohorts. DCA confirmed the potential clinical usefulness of the nomogram. Conclusions The DBT plus DM provided a lower AUC and sensitivity, but a higher specificity than DCE plus DWI for detecting breast cancer. The proposed clinical-radiomics nomogram has diagnostic advantages over each modality, and can be considered as an efficient tool for breast cancer screening.
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Affiliation(s)
- Shuxian Niu
- Department of Biomedical Engineering, School of Fundamental Sciences, China Medical University, Shenyang, China
| | - Xiaoyu Wang
- Liaoning Cancer Hospital and Institute, Cancer Hospital of China Medical University, Shenyang, China
| | - Nannan Zhao
- Liaoning Cancer Hospital and Institute, Cancer Hospital of China Medical University, Shenyang, China
| | - Guanyu Liu
- Liaoning Cancer Hospital and Institute, Cancer Hospital of China Medical University, Shenyang, China
| | - Yangyang Kan
- Liaoning Cancer Hospital and Institute, Cancer Hospital of China Medical University, Shenyang, China
| | - Yue Dong
- Liaoning Cancer Hospital and Institute, Cancer Hospital of China Medical University, Shenyang, China
| | - E-Nuo Cui
- School of Computer Science and Engineering, Shenyang University, Shenyang, China
| | - Yahong Luo
- Liaoning Cancer Hospital and Institute, Cancer Hospital of China Medical University, Shenyang, China
| | - Tao Yu
- Liaoning Cancer Hospital and Institute, Cancer Hospital of China Medical University, Shenyang, China
| | - Xiran Jiang
- Department of Biomedical Engineering, School of Fundamental Sciences, China Medical University, Shenyang, China
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Vancoillie L, Cockmartin L, Marshall N, Bosmans H. The impact on lesion detection via a multi-vendor study: A phantom-based comparison of digital mammography, digital breast tomosynthesis, and synthetic mammography. Med Phys 2021; 48:6270-6292. [PMID: 34407213 DOI: 10.1002/mp.15171] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 07/27/2021] [Accepted: 07/27/2021] [Indexed: 11/09/2022] Open
Abstract
PURPOSE The aim of this study is to perform a test object-based comparison of the imaging performance of digital mammography (DM), digital breast tomosynthesis (DBT), and synthetic mammography (SM). METHODS Two test objects were used, the CDMAM and the L1-structured phantom. Small-detail detectability was assessed using CDMAM and the microcalcification simulating specks in the L1-structured background. Detection of spiculated and non-spiculated mass-like objects was assessed using the L1 phantom. Six different systems were included: Amulet Innovality (Fujifilm), Senographe Pristina (GEHC), 3Dimensions (Hologic), Giotto Class (IMS), Clarity 2D/3D (Planmed), and Mammomat Revelation (Siemens). Images were acquired under automatic exposure control (AEC) and at adjusted levels of AEC/2 and 2 × AEC level. Threshold gold thickness (Ttr ) was established for the 0.13-mm-diameter CDMAM discs. Threshold diameters for the calcifications (dtr_c ), the spiculated masses (dtr_sm ), and for the non-spiculated masses (dtr_nsm ) were established. The threshold condition was defined as the thickness or diameter for a 62.5% correct score. RESULTS Ttr for DM was generally superior to DBT, which in turn was superior to SM, but for most systems, these differences between modes were not significant. For L1, no significant differences in dtr_c were found between DM and DBT. The increase in dtr_c from DM to SM at AEC dose was 1%, 19%, 11%, 14%, 46%, and 27% for the Fujifilm, GEHC, Hologic, IMS, Planmed, and Siemens, respectively, indicating significantly poorer performance for all vendors except for Fujifilm, Hologic, and IMS. For both mass types, DBT performed better than SM, while SM showed no significant difference with DM (except for Fujifilm spiculated masses). The dose had an impact on small-detail detectability for both phantoms but did not influence the detection of either mass type. CONCLUSIONS Both phantoms indicated potentially reduced small-detail detectability for SM versus DM and DBT and should therefore not be used in stand-alone mode. The L1 phantom demonstrated no significant difference in microcalcification detection between DM and DBT and also demonstrated the superiority of DBT, compared to DM for mass detection, for all six systems.
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Affiliation(s)
- Liesbeth Vancoillie
- Department of Imaging and Pathology, KU Leuven, Division of Medical Physics & Quality Assessment, Leuven, Belgium
| | | | - Nicholas Marshall
- Department of Imaging and Pathology, KU Leuven, Division of Medical Physics & Quality Assessment, Leuven, Belgium.,Department of Radiology, UZ Leuven, Leuven, Belgium
| | - Hilde Bosmans
- Department of Imaging and Pathology, KU Leuven, Division of Medical Physics & Quality Assessment, Leuven, Belgium.,Department of Radiology, UZ Leuven, Leuven, Belgium
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Clauser P, Baltzer PAT, Kapetas P, Woitek R, Weber M, Leone F, Bernathova M, Helbich TH. One view or two views for wide-angle tomosynthesis with synthetic mammography in the assessment setting? Eur Radiol 2021; 32:661-670. [PMID: 34324025 PMCID: PMC8660729 DOI: 10.1007/s00330-021-08079-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 04/14/2021] [Accepted: 05/19/2021] [Indexed: 11/30/2022]
Abstract
Objectives To evaluate the diagnostic performance in the assessment setting of three protocols: one-view wide-angle digital breast tomosynthesis (WA-DBT) with synthetic mammography (SM), two-view WA-DBT/SM, and two-view digital mammography (DM). Methods Included in this retrospective study were patients who underwent bilateral two-view DM and WA-DBT. SM were reconstructed from the WA-DBT data. The standard of reference was histology and/or 2 years follow-up. Included were 205 women with 179 lesions (89 malignant, 90 benign). Four blinded readers randomly evaluated images to assess density, lesion type, and level of suspicion according to BI-RADS. Three protocols were evaluated: two-view DM, one-view (mediolateral oblique) WA-DBT/SM, and two-view WA-DBT/SM. Detection rate, sensitivity, specificity, and accuracy were calculated and compared using multivariate analysis. Reading time was assessed. Results The detection rate was higher with two-view WA-DBT/SM (p = 0.063). Sensitivity was higher for two-view WA-DBT/SM compared to two-view DM (p = 0.001) and one-view WA-DBT/SM (p = 0.058). No significant differences in specificity were found. Accuracy was higher with both one-view WA-DBT/SM and two-view WA-DBT/SM compared to DM (p = 0.003 and > 0.001, respectively). Accuracy did not differ between one- and two-view WA-DBT/SM. Two-view WA-DBT/SM performed better for masses and asymmetries. Reading times were significantly longer when WA-DBT was evaluated. Conclusions One-view and two-view WA-DBT/SM can achieve a higher diagnostic performance compared to two-view DM. The detection rate and sensitivity were highest with two-view WA-DBT/SM. Two-view WA-DBT/SM appears to be the most appropriate tool for the assessment of breast lesions. Key Points • Detection rate with two-view wide-angle digital breast tomosynthesis (WA-DBT) is significantly higher than with two-view digital mammography in the assessment setting. • Diagnostic accuracy of one-view and two-view WA-DBT with synthetic mammography (SM) in the assessment setting is higher than that of two-view digital mammography. • Compared to one-view WA-DBT with SM, two-view WA-DBT with SM seems to be the most appropriate tool for the assessment of breast lesions.
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Affiliation(s)
- Paola Clauser
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna/General Hospital Vienna, Waehringer Guertel 18-20, Vienna, Austria.
| | - Pascal A T Baltzer
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna/General Hospital Vienna, Waehringer Guertel 18-20, Vienna, Austria
| | - Panagiotis Kapetas
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna/General Hospital Vienna, Waehringer Guertel 18-20, Vienna, Austria
| | - Ramona Woitek
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna/General Hospital Vienna, Waehringer Guertel 18-20, Vienna, Austria
| | - Michael Weber
- Division of General and Pediatric Radiology, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
| | - Federica Leone
- Ospedale Luigi Sacco - Polo Universitario, via G.B. Grassi 74, 20157, Milan, Italy
| | - Maria Bernathova
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna/General Hospital Vienna, Waehringer Guertel 18-20, Vienna, Austria
| | - Thomas H Helbich
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna/General Hospital Vienna, Waehringer Guertel 18-20, Vienna, Austria
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31
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Wan S, Arhatari BD, Nesterets YI, Mayo SC, Thompson D, Fox J, Kumar B, Prodanovic Z, Hausermann D, Maksimenko A, Hall C, Dimmock M, Pavlov KM, Lockie D, Rickard M, Gadomkar Z, Aminzadeh A, Vafa E, Peele A, Quiney HM, Lewis S, Gureyev TE, Brennan PC, Taba ST. Effect of x-ray energy on the radiological image quality in propagation-based phase-contrast computed tomography of the breast. J Med Imaging (Bellingham) 2021; 8:052108. [PMID: 34268442 DOI: 10.1117/1.jmi.8.5.052108] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2021] [Accepted: 06/28/2021] [Indexed: 01/22/2023] Open
Abstract
Purpose: Breast cancer is the most common cancer in women in developing and developed countries and is responsible for 15% of women's cancer deaths worldwide. Conventional absorption-based breast imaging techniques lack sufficient contrast for comprehensive diagnosis. Propagation-based phase-contrast computed tomography (PB-CT) is a developing technique that exploits a more contrast-sensitive property of x-rays: x-ray refraction. X-ray absorption, refraction, and contrast-to-noise in the corresponding images depend on the x-ray energy used, for the same/fixed radiation dose. The aim of this paper is to explore the relationship between x-ray energy and radiological image quality in PB-CT imaging. Approach: Thirty-nine mastectomy samples were scanned at the imaging and medical beamline at the Australian Synchrotron. Samples were scanned at various x-ray energies of 26, 28, 30, 32, 34, and 60 keV using a Hamamatsu Flat Panel detector at the same object-to-detector distance of 6 m and mean glandular dose of 4 mGy. A total of 132 image sets were produced for analysis. Seven observers rated PB-CT images against absorption-based CT (AB-CT) images of the same samples on a five-point scale. A visual grading characteristics (VGC) study was used to determine the difference in image quality. Results: PB-CT images produced at 28, 30, 32, and 34 keV x-ray energies demonstrated statistically significant higher image quality than reference AB-CT images. The optimum x-ray energy, 30 keV, displayed the largest area under the curve ( AUC VGC ) of 0.754 ( p = 0.009 ). This was followed by 32 keV ( AUC VGC = 0.731 , p ≤ 0.001 ), 34 keV ( AUC VGC = 0.723 , p ≤ 0.001 ), and 28 keV ( AUC VGC = 0.654 , p = 0.015 ). Conclusions: An optimum energy range (around 30 keV) in the PB-CT technique allows for higher image quality at a dose comparable to conventional mammographic techniques. This results in improved radiological image quality compared with conventional techniques, which may ultimately lead to higher diagnostic efficacy and a reduction in breast cancer mortalities.
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Affiliation(s)
- Sarina Wan
- University of Sydney, Faculty of Medicine and Health, Department of Medical Radiation Sciences, Lidcombe, Australia
| | - Benedicta D Arhatari
- Australian Synchrotron, ANSTO, Clayton, Australia.,University of Melbourne, School of Physics, Parkville, Australia
| | - Yakov I Nesterets
- Commonwealth Scientific and Industrial Research Organisation, Clayton, Australia.,University of New England, School of Science and Technology, Armidale, Australia
| | - Sheridan C Mayo
- Commonwealth Scientific and Industrial Research Organisation, Clayton, Australia
| | - Darren Thompson
- Commonwealth Scientific and Industrial Research Organisation, Clayton, Australia.,University of New England, School of Science and Technology, Armidale, Australia
| | - Jane Fox
- Monash University, Faculty of Medicine, Nursing and Health Sciences, Clayton, Australia.,Monash Health, Department of Pathology, Clayton, Australia
| | - Beena Kumar
- Monash Health, Department of Pathology, Clayton, Australia
| | | | | | | | | | - Matthew Dimmock
- Monash University, Faculty of Medicine, Nursing and Health Sciences, Clayton, Australia
| | - Konstantin M Pavlov
- University of New England, School of Science and Technology, Armidale, Australia.,University of Canterbury, School of Physical and Chemical Sciences, Christchurch, New Zealand.,Monash University, School of Physics and Astronomy, Clayton, Australia
| | - Darren Lockie
- Maroondah BreastScreen, Eastern Health, Ringwood, Australia
| | - Mary Rickard
- University of Sydney, Faculty of Medicine and Health, Department of Medical Radiation Sciences, Lidcombe, Australia
| | - Ziba Gadomkar
- University of Sydney, Faculty of Medicine and Health, Department of Medical Radiation Sciences, Lidcombe, Australia
| | - Alaleh Aminzadeh
- University of Melbourne, School of Physics, Parkville, Australia
| | - Elham Vafa
- University of Sydney, Faculty of Medicine and Health, Department of Medical Radiation Sciences, Lidcombe, Australia
| | - Andrew Peele
- Australian Synchrotron, ANSTO, Clayton, Australia
| | - Harry M Quiney
- University of Melbourne, School of Physics, Parkville, Australia
| | - Sarah Lewis
- University of Sydney, Faculty of Medicine and Health, Department of Medical Radiation Sciences, Lidcombe, Australia
| | - Timur E Gureyev
- University of Sydney, Faculty of Medicine and Health, Department of Medical Radiation Sciences, Lidcombe, Australia.,University of Melbourne, School of Physics, Parkville, Australia.,University of New England, School of Science and Technology, Armidale, Australia.,Monash University, School of Physics and Astronomy, Clayton, Australia
| | - Patrick C Brennan
- University of Sydney, Faculty of Medicine and Health, Department of Medical Radiation Sciences, Lidcombe, Australia
| | - Seyedamir Tavakoli Taba
- University of Sydney, Faculty of Medicine and Health, Department of Medical Radiation Sciences, Lidcombe, Australia
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Pinto MC, Rodriguez-Ruiz A, Pedersen K, Hofvind S, Wicklein J, Kappler S, Mann RM, Sechopoulos I. Impact of Artificial Intelligence Decision Support Using Deep Learning on Breast Cancer Screening Interpretation with Single-View Wide-Angle Digital Breast Tomosynthesis. Radiology 2021; 300:529-536. [PMID: 34227882 DOI: 10.1148/radiol.2021204432] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
Background The high volume of data in digital breast tomosynthesis (DBT) and the lack of agreement on how to best implement it in screening programs makes its use challenging. Purpose To compare radiologist performance when reading single-view wide-angle DBT images with and without an artificial intelligence (AI) system for decision and navigation support. Materials and Methods A retrospective observer study was performed with bilateral mediolateral oblique examinations and corresponding synthetic two-dimensional images acquired between June 2016 and February 2018 with a wide-angle DBT system. Fourteen breast screening radiologists interpreted 190 DBT examinations (90 normal, 26 with benign findings, and 74 with malignant findings), with the reference standard being verified by using histopathologic analysis or at least 1 year of follow-up. Reading was performed in two sessions, separated by at least 4 weeks, with a random mix of examinations being read with and without AI decision and navigation support. Forced Breast Imaging Reporting and Data System (categories 1-5) and level of suspicion (1-100) scores were given per breast by each reader. The area under the receiver operating characteristic curve (AUC) and the sensitivity and specificity were compared between conditions by using the public-domain iMRMC software. The average reading times were compared by using the Wilcoxon signed rank test. Results The 190 women had a median age of 54 years (range, 48-63 years). The examination-based reader-averaged AUC was higher when interpreting results with AI support than when reading unaided (0.88 [95% CI: 0.84, 0.92] vs 0.85 [95% CI: 0.80, 0.89], respectively; P = .01). The average sensitivity increased with AI support (64 of 74, 86% [95% CI: 80%, 92%] vs 60 of 74, 81% [95% CI: 74%, 88%]; P = .006), whereas no differences in the specificity (85 of 116, 73.3% [95% CI: 65%, 81%] vs 83 of 116, 71.6% [95% CI: 65%, 78%]; P = .48) or reading time (48 seconds vs 45 seconds; P = .35) were detected. Conclusion Using a single-view digital breast tomosynthesis (DBT) and artificial intelligence setup could allow for a more effective screening program with higher performance, especially in terms of an increase in cancers detected, than using single-view DBT alone. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Chan and Helvie in this issue.
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Affiliation(s)
- Marta C Pinto
- From the Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein 10, 6525 GA, Post 766, Nijmegen, the Netherlands (M.C.P., R.M.M., I.S.); ScreenPoint Medical, Nijmegen, the Netherlands (A.R.R.); Cancer Registry of Norway, Oslo, Norway (K.P., S.H.); Siemens Healthcare, Forchheim, Germany (J.W., S.K.); Department of Radiology, the Netherlands Cancer Institute, Amsterdam, the Netherlands (R.M.M.); and the Dutch Expert Centre for Screening, Nijmegen, the Netherlands (I.S.)
| | - Alejandro Rodriguez-Ruiz
- From the Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein 10, 6525 GA, Post 766, Nijmegen, the Netherlands (M.C.P., R.M.M., I.S.); ScreenPoint Medical, Nijmegen, the Netherlands (A.R.R.); Cancer Registry of Norway, Oslo, Norway (K.P., S.H.); Siemens Healthcare, Forchheim, Germany (J.W., S.K.); Department of Radiology, the Netherlands Cancer Institute, Amsterdam, the Netherlands (R.M.M.); and the Dutch Expert Centre for Screening, Nijmegen, the Netherlands (I.S.)
| | - Kristin Pedersen
- From the Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein 10, 6525 GA, Post 766, Nijmegen, the Netherlands (M.C.P., R.M.M., I.S.); ScreenPoint Medical, Nijmegen, the Netherlands (A.R.R.); Cancer Registry of Norway, Oslo, Norway (K.P., S.H.); Siemens Healthcare, Forchheim, Germany (J.W., S.K.); Department of Radiology, the Netherlands Cancer Institute, Amsterdam, the Netherlands (R.M.M.); and the Dutch Expert Centre for Screening, Nijmegen, the Netherlands (I.S.)
| | - Solveig Hofvind
- From the Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein 10, 6525 GA, Post 766, Nijmegen, the Netherlands (M.C.P., R.M.M., I.S.); ScreenPoint Medical, Nijmegen, the Netherlands (A.R.R.); Cancer Registry of Norway, Oslo, Norway (K.P., S.H.); Siemens Healthcare, Forchheim, Germany (J.W., S.K.); Department of Radiology, the Netherlands Cancer Institute, Amsterdam, the Netherlands (R.M.M.); and the Dutch Expert Centre for Screening, Nijmegen, the Netherlands (I.S.)
| | - Julia Wicklein
- From the Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein 10, 6525 GA, Post 766, Nijmegen, the Netherlands (M.C.P., R.M.M., I.S.); ScreenPoint Medical, Nijmegen, the Netherlands (A.R.R.); Cancer Registry of Norway, Oslo, Norway (K.P., S.H.); Siemens Healthcare, Forchheim, Germany (J.W., S.K.); Department of Radiology, the Netherlands Cancer Institute, Amsterdam, the Netherlands (R.M.M.); and the Dutch Expert Centre for Screening, Nijmegen, the Netherlands (I.S.)
| | - Steffen Kappler
- From the Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein 10, 6525 GA, Post 766, Nijmegen, the Netherlands (M.C.P., R.M.M., I.S.); ScreenPoint Medical, Nijmegen, the Netherlands (A.R.R.); Cancer Registry of Norway, Oslo, Norway (K.P., S.H.); Siemens Healthcare, Forchheim, Germany (J.W., S.K.); Department of Radiology, the Netherlands Cancer Institute, Amsterdam, the Netherlands (R.M.M.); and the Dutch Expert Centre for Screening, Nijmegen, the Netherlands (I.S.)
| | - Ritse M Mann
- From the Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein 10, 6525 GA, Post 766, Nijmegen, the Netherlands (M.C.P., R.M.M., I.S.); ScreenPoint Medical, Nijmegen, the Netherlands (A.R.R.); Cancer Registry of Norway, Oslo, Norway (K.P., S.H.); Siemens Healthcare, Forchheim, Germany (J.W., S.K.); Department of Radiology, the Netherlands Cancer Institute, Amsterdam, the Netherlands (R.M.M.); and the Dutch Expert Centre for Screening, Nijmegen, the Netherlands (I.S.)
| | - Ioannis Sechopoulos
- From the Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein 10, 6525 GA, Post 766, Nijmegen, the Netherlands (M.C.P., R.M.M., I.S.); ScreenPoint Medical, Nijmegen, the Netherlands (A.R.R.); Cancer Registry of Norway, Oslo, Norway (K.P., S.H.); Siemens Healthcare, Forchheim, Germany (J.W., S.K.); Department of Radiology, the Netherlands Cancer Institute, Amsterdam, the Netherlands (R.M.M.); and the Dutch Expert Centre for Screening, Nijmegen, the Netherlands (I.S.)
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Alves MS, Belinato W, Santos WS, Galeano DC, Neves LP, Perini AP, N Souza D. Dosimetry in Digital Breast Tomosynthesis Evaluated by Monte Carlo Technique. HEALTH PHYSICS 2021; 121:18-29. [PMID: 33867436 DOI: 10.1097/hp.0000000000001407] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
ABSTRACT The influence of the angular deviation of the tube during digital breast tomosynthesis (DBT) acquisition to the dose in the examined breast and in other organs and tissues is not well known. In this work, the Monte Carlo method was used with an adult female virtual anthropomorphic phantom to investigate the impact of this angular variation on the breast dose. The absorbed dose in the examined breast was normalized by the air kerma, which resulted in an absorbed dose coefficient (DT/Kair) for the breast. The absorbed dose in each organ was normalized by the glandular dose in the breast, resulting in the relative organ dose (ROD). An adult female virtual anthropomorphic phantom (FSTA_M50_H50) was incorporated into a scenario containing tomosynthesis equipment with Mo/Mo, Mo/Rh, and W/Rh target/filter combinations and tube voltages of 28 kV. The comparison between the results of the simulations considering digital mammography (DM) and DBT data showed that the DT/Kair values for the examined breast obtained with the DBT parameters were up to 24 times higher than with the DT/Kair obtained with DM parameters. A DT/Kair of 0.97 × 10-1 mGy mGy-1 was obtained in a DBT exam of the right breast. Considering the other organs, the highest ROD values were observed in the thyroid (6.45 × 10-4), eyes (3.87 × 10-4), liver (1.95 × 10-5), and eye lenses (3.21 × 10-3). A variation in the absorbed dose values for the breast and other organs was observed for all projections different from 0°.
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Affiliation(s)
- Marcos S Alves
- Departamento de Física, Universidade Federal de Sergipe (UFS), São Cristóvão, Sergipe, Brazil
| | - Walmir Belinato
- Instituto Federal da Bahia (IFBA), Vitória da Conquista, BA, Brazil
| | - William S Santos
- Instituto de Física, Universidade Federal de Uberlândia, Uberlândia, MG, Brazil
| | - Diego C Galeano
- Hospital Universitário Júlio Müller, Universidade Federal de Mato Grosso, Cuiabá, MT, Brazil
| | | | | | - Divanizia N Souza
- Departamento de Física, Universidade Federal de Sergipe (UFS), São Cristóvão, Sergipe, Brazil
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Cohen EO, Perry RE, Tso HH, Phalak KA, Lesslie MD, Gerlach KE, Sun J, Srinivasan A, Leung JWT. Breast cancer screening in women with and without implants: retrospective study comparing digital mammography to digital mammography combined with digital breast tomosynthesis. Eur Radiol 2021; 31:9499-9510. [PMID: 34014380 DOI: 10.1007/s00330-021-08040-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 03/29/2021] [Accepted: 05/04/2021] [Indexed: 11/29/2022]
Abstract
OBJECTIVES Compare four groups being screened: women without breast implants undergoing digital mammography (DM), women without breast implants undergoing DM with digital breast tomosynthesis (DM/DBT), women with implants undergoing DM, and women with implants undergoing DM/DBT. METHODS Mammograms from February 2011 to March 2017 were retrospectively reviewed after 13,201 were excluded for a unilateral implant or prior breast cancer. Patients had been allowed to choose between DM and DM/DBT screening. Mammography performance metrics were compared using chi-square tests. RESULTS Six thousand forty-one women with implants and 91,550 women without implants were included. In mammograms without implants, DM (n = 113,973) and DM/DBT (n = 61,896) yielded recall rates (RRs) of 8.53% and 6.79% (9726/113,973 and 4204/61,896, respectively, p < .001), cancer detection rates per 1000 exams (CDRs) of 3.96 and 5.12 (451/113,973 and 317/61,896, respectively, p = .003), and positive predictive values for recall (PPV1s) of 4.64% and 7.54% (451/9726 and 317/4204, respectively, p < .001), respectively. In mammograms with implants, DM (n = 6815) and DM/DBT (n = 5138) yielded RRs of 5.81% and 4.87% (396/6815 and 250/5138, respectively, p = .158), CDRs of 2.49 and 2.92 (17/6815 and 15/5138, respectively, p > 0.999), and PPV1s of 4.29% and 6.0% (17/396 and 15/250, respectively, p > 0.999), respectively. CONCLUSIONS DM/DBT significantly improved recall rates, cancer detection rates, and positive predictive values for recall compared to DM alone in women without implants. DM/DBT performance in women with implants trended towards similar improvements, though no metric was statistically significant. KEY POINTS • Digital mammography with tomosynthesis improved recall rates, cancer detection rates, and positive predictive values for recall compared to digital mammography alone for women without implants. • Digital mammography with tomosynthesis trended towards improving recall rates, cancer detection rates, and positive predictive values for recall compared to digital mammography alone for women with implants, but these trends were not statistically significant - likely related to sample size.
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Affiliation(s)
- Ethan O Cohen
- Department of Breast Imaging, Unit 1350, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA.
| | - Rachel E Perry
- Department of Breast Imaging, Unit 1350, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA
| | - Hilda H Tso
- Department of Breast Imaging, Unit 1350, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA
| | - Kanchan A Phalak
- Department of Breast Imaging, Unit 1350, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA
| | - Michele D Lesslie
- Department of Breast Imaging, Unit 1350, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA
| | - Karen E Gerlach
- Department of Breast Imaging, Unit 1350, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA
| | - Jia Sun
- Department of Biostatistics, Unit 1411, The University of Texas MD Anderson Cancer Center, PO Box 301402, Houston, TX, 77230-1402, USA
| | - Ashmitha Srinivasan
- Department of Breast Imaging, Unit 1350, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA
| | - Jessica W T Leung
- Department of Breast Imaging, Unit 1350, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA
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Swiecicki A, Konz N, Buda M, Mazurowski MA. A generative adversarial network-based abnormality detection using only normal images for model training with application to digital breast tomosynthesis. Sci Rep 2021; 11:10276. [PMID: 33986361 PMCID: PMC8119417 DOI: 10.1038/s41598-021-89626-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Accepted: 04/20/2021] [Indexed: 01/07/2023] Open
Abstract
Deep learning has shown tremendous potential in the task of object detection in images. However, a common challenge with this task is when only a limited number of images containing the object of interest are available. This is a particular issue in cancer screening, such as digital breast tomosynthesis (DBT), where less than 1% of cases contain cancer. In this study, we propose a method to train an inpainting generative adversarial network to be used for cancer detection using only images that do not contain cancer. During inference, we removed a part of the image and used the network to complete the removed part. A significant error in completing an image part was considered an indication that such location is unexpected and thus abnormal. A large dataset of DBT images used in this study was collected at Duke University. It consisted of 19,230 reconstructed volumes from 4348 patients. Cancerous masses and architectural distortions were marked with bounding boxes by radiologists. Our experiments showed that the locations containing cancer were associated with a notably higher completion error than the non-cancer locations (mean error ratio of 2.77). All data used in this study has been made publicly available by the authors.
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Affiliation(s)
- Albert Swiecicki
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA.
| | - Nicholas Konz
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
| | - Mateusz Buda
- Department of Radiology, Duke University, Durham, NC, USA
| | - Maciej A Mazurowski
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA.,Department of Radiology, Duke University, Durham, NC, USA
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Hofvind S, Moshina N, Holen ÅS, Danielsen AS, Lee CI, Houssami N, Aase HS, Akslen LA, Haldorsen IS. Interval and Subsequent Round Breast Cancer in a Randomized Controlled Trial Comparing Digital Breast Tomosynthesis and Digital Mammography Screening. Radiology 2021; 300:66-76. [PMID: 33973840 DOI: 10.1148/radiol.2021203936] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Background Prevalent digital breast tomosynthesis (DBT) has shown higher cancer detection rates and lower recall rates compared with those of digital mammography (DM). However, data are limited on rates and histopathologic tumor characteristics of interval and subsequent round screen-detected cancers for DBT. Purpose To follow women randomized to screening with DBT or DM and to investigate rates and tumor characteristics of interval and subsequent round screen-detected cancers. Materials and Methods To-Be is a randomized controlled trial comparing the outcome of DBT and DM in organized breast cancer screening. The trial included 28 749 women, with 22 306 women returning for subsequent DBT screening 2 years later (11 201 and 11 105 originally screened with DBT and DM, respectively). Differences in rates, means, and distribution of histopathologic tumor characteristics between women prevalently screened with DBT versus DM were evaluated with Z tests, t tests, and χ2 tests. Relative risk (RR) with 95% CIs was calculated for the cancer rates. Results Interval cancer rates were 1.4 per 1000 screens (20 of 14 380; 95% CI: 0.9, 2.1) for DBT versus 2.0 per 1000 screens (29 of 14 369; 95% CI: 1.4, 2.9; P = .20) for DM. The rates of subsequent round screen-detected cancer were 8.1 per 1000 (95% CI: 6.6, 10.0) for women originally screened with DBT and 9.1 per 1000 (95% CI: 7.4, 11.0; P = .43) for women screened with DM. The distribution of tumor characteristics did not differ between groups for either interval or subsequent screen-detected cancer. The RR of interval cancer was 0.69 (95% CI: 0.39, 1.22; P = .20) for DBT versus DM, whereas RR of subsequent screen-detected cancer for women prevalently screened with DBT versus DM was 0.89 (95% CI: 0.67, 1.19; P = .43). Conclusion Rates of interval or subsequent round screen-detected cancers and their tumor characteristics did not differ between women originally screened with digital breast tomosynthesis (DBT) versus digital mammography. The analysis suggests that the benefits of prevalent DBT screening did not come at the expense of worse downstream screening performance measures in a population-based screening program. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Taourel in this issue.
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Affiliation(s)
- Solveig Hofvind
- From the Cancer Registry of Norway, PO 5313, Maiorstuen, 0304 Oslo, Norway (S.H., N.M., Å.S.H., A.S.D.); Faculty of Health Sciences, Oslo Metropolitan University, Oslo, Norway (S.H.); Department of Radiology, University of Washington School of Medicine, Seattle, Wash (C.I.L.); Department of Health Services, University of Washington School of Public Health, Seattle, Wash (C.I.L.); Sydney School of Public Health, Faculty of Medicine and Health, University of Sydney, Sydney, Australia (N.H.); Department of Radiology (H.S.A., I.S.H.), Department of Pathology (L.A.A.), and Mohn Medical Imaging and Visualization Centre (I.S.H.), Haukeland University Hospital, Bergen, Norway; and Department of Clinical Medicine (H.S.A., I.S.H.), Section for Pathology (L.A.A.), and Centre for Cancer Biomarkers CCBIO (L.A.A.), University of Bergen, Bergen, Norway
| | - Nataliia Moshina
- From the Cancer Registry of Norway, PO 5313, Maiorstuen, 0304 Oslo, Norway (S.H., N.M., Å.S.H., A.S.D.); Faculty of Health Sciences, Oslo Metropolitan University, Oslo, Norway (S.H.); Department of Radiology, University of Washington School of Medicine, Seattle, Wash (C.I.L.); Department of Health Services, University of Washington School of Public Health, Seattle, Wash (C.I.L.); Sydney School of Public Health, Faculty of Medicine and Health, University of Sydney, Sydney, Australia (N.H.); Department of Radiology (H.S.A., I.S.H.), Department of Pathology (L.A.A.), and Mohn Medical Imaging and Visualization Centre (I.S.H.), Haukeland University Hospital, Bergen, Norway; and Department of Clinical Medicine (H.S.A., I.S.H.), Section for Pathology (L.A.A.), and Centre for Cancer Biomarkers CCBIO (L.A.A.), University of Bergen, Bergen, Norway
| | - Åsne S Holen
- From the Cancer Registry of Norway, PO 5313, Maiorstuen, 0304 Oslo, Norway (S.H., N.M., Å.S.H., A.S.D.); Faculty of Health Sciences, Oslo Metropolitan University, Oslo, Norway (S.H.); Department of Radiology, University of Washington School of Medicine, Seattle, Wash (C.I.L.); Department of Health Services, University of Washington School of Public Health, Seattle, Wash (C.I.L.); Sydney School of Public Health, Faculty of Medicine and Health, University of Sydney, Sydney, Australia (N.H.); Department of Radiology (H.S.A., I.S.H.), Department of Pathology (L.A.A.), and Mohn Medical Imaging and Visualization Centre (I.S.H.), Haukeland University Hospital, Bergen, Norway; and Department of Clinical Medicine (H.S.A., I.S.H.), Section for Pathology (L.A.A.), and Centre for Cancer Biomarkers CCBIO (L.A.A.), University of Bergen, Bergen, Norway
| | - Anders S Danielsen
- From the Cancer Registry of Norway, PO 5313, Maiorstuen, 0304 Oslo, Norway (S.H., N.M., Å.S.H., A.S.D.); Faculty of Health Sciences, Oslo Metropolitan University, Oslo, Norway (S.H.); Department of Radiology, University of Washington School of Medicine, Seattle, Wash (C.I.L.); Department of Health Services, University of Washington School of Public Health, Seattle, Wash (C.I.L.); Sydney School of Public Health, Faculty of Medicine and Health, University of Sydney, Sydney, Australia (N.H.); Department of Radiology (H.S.A., I.S.H.), Department of Pathology (L.A.A.), and Mohn Medical Imaging and Visualization Centre (I.S.H.), Haukeland University Hospital, Bergen, Norway; and Department of Clinical Medicine (H.S.A., I.S.H.), Section for Pathology (L.A.A.), and Centre for Cancer Biomarkers CCBIO (L.A.A.), University of Bergen, Bergen, Norway
| | - Christoph I Lee
- From the Cancer Registry of Norway, PO 5313, Maiorstuen, 0304 Oslo, Norway (S.H., N.M., Å.S.H., A.S.D.); Faculty of Health Sciences, Oslo Metropolitan University, Oslo, Norway (S.H.); Department of Radiology, University of Washington School of Medicine, Seattle, Wash (C.I.L.); Department of Health Services, University of Washington School of Public Health, Seattle, Wash (C.I.L.); Sydney School of Public Health, Faculty of Medicine and Health, University of Sydney, Sydney, Australia (N.H.); Department of Radiology (H.S.A., I.S.H.), Department of Pathology (L.A.A.), and Mohn Medical Imaging and Visualization Centre (I.S.H.), Haukeland University Hospital, Bergen, Norway; and Department of Clinical Medicine (H.S.A., I.S.H.), Section for Pathology (L.A.A.), and Centre for Cancer Biomarkers CCBIO (L.A.A.), University of Bergen, Bergen, Norway
| | - Nehmat Houssami
- From the Cancer Registry of Norway, PO 5313, Maiorstuen, 0304 Oslo, Norway (S.H., N.M., Å.S.H., A.S.D.); Faculty of Health Sciences, Oslo Metropolitan University, Oslo, Norway (S.H.); Department of Radiology, University of Washington School of Medicine, Seattle, Wash (C.I.L.); Department of Health Services, University of Washington School of Public Health, Seattle, Wash (C.I.L.); Sydney School of Public Health, Faculty of Medicine and Health, University of Sydney, Sydney, Australia (N.H.); Department of Radiology (H.S.A., I.S.H.), Department of Pathology (L.A.A.), and Mohn Medical Imaging and Visualization Centre (I.S.H.), Haukeland University Hospital, Bergen, Norway; and Department of Clinical Medicine (H.S.A., I.S.H.), Section for Pathology (L.A.A.), and Centre for Cancer Biomarkers CCBIO (L.A.A.), University of Bergen, Bergen, Norway
| | - Hildegunn S Aase
- From the Cancer Registry of Norway, PO 5313, Maiorstuen, 0304 Oslo, Norway (S.H., N.M., Å.S.H., A.S.D.); Faculty of Health Sciences, Oslo Metropolitan University, Oslo, Norway (S.H.); Department of Radiology, University of Washington School of Medicine, Seattle, Wash (C.I.L.); Department of Health Services, University of Washington School of Public Health, Seattle, Wash (C.I.L.); Sydney School of Public Health, Faculty of Medicine and Health, University of Sydney, Sydney, Australia (N.H.); Department of Radiology (H.S.A., I.S.H.), Department of Pathology (L.A.A.), and Mohn Medical Imaging and Visualization Centre (I.S.H.), Haukeland University Hospital, Bergen, Norway; and Department of Clinical Medicine (H.S.A., I.S.H.), Section for Pathology (L.A.A.), and Centre for Cancer Biomarkers CCBIO (L.A.A.), University of Bergen, Bergen, Norway
| | - Lars A Akslen
- From the Cancer Registry of Norway, PO 5313, Maiorstuen, 0304 Oslo, Norway (S.H., N.M., Å.S.H., A.S.D.); Faculty of Health Sciences, Oslo Metropolitan University, Oslo, Norway (S.H.); Department of Radiology, University of Washington School of Medicine, Seattle, Wash (C.I.L.); Department of Health Services, University of Washington School of Public Health, Seattle, Wash (C.I.L.); Sydney School of Public Health, Faculty of Medicine and Health, University of Sydney, Sydney, Australia (N.H.); Department of Radiology (H.S.A., I.S.H.), Department of Pathology (L.A.A.), and Mohn Medical Imaging and Visualization Centre (I.S.H.), Haukeland University Hospital, Bergen, Norway; and Department of Clinical Medicine (H.S.A., I.S.H.), Section for Pathology (L.A.A.), and Centre for Cancer Biomarkers CCBIO (L.A.A.), University of Bergen, Bergen, Norway
| | - Ingfrid S Haldorsen
- From the Cancer Registry of Norway, PO 5313, Maiorstuen, 0304 Oslo, Norway (S.H., N.M., Å.S.H., A.S.D.); Faculty of Health Sciences, Oslo Metropolitan University, Oslo, Norway (S.H.); Department of Radiology, University of Washington School of Medicine, Seattle, Wash (C.I.L.); Department of Health Services, University of Washington School of Public Health, Seattle, Wash (C.I.L.); Sydney School of Public Health, Faculty of Medicine and Health, University of Sydney, Sydney, Australia (N.H.); Department of Radiology (H.S.A., I.S.H.), Department of Pathology (L.A.A.), and Mohn Medical Imaging and Visualization Centre (I.S.H.), Haukeland University Hospital, Bergen, Norway; and Department of Clinical Medicine (H.S.A., I.S.H.), Section for Pathology (L.A.A.), and Centre for Cancer Biomarkers CCBIO (L.A.A.), University of Bergen, Bergen, Norway
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Aase HS, Danielsen AS, Hoff SR, Holen ÅS, Haldorsen IS, Hovda T, Hanestad B, Sandvik CK, Hofvind S. Mammographic features and screening outcome in a randomized controlled trial comparing digital breast tomosynthesis and digital mammography. Eur J Radiol 2021; 141:109753. [PMID: 34053786 DOI: 10.1016/j.ejrad.2021.109753] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Revised: 04/17/2021] [Accepted: 04/30/2021] [Indexed: 11/16/2022]
Abstract
PURPOSE To compare the distribution of mammographic features among women recalled for further assessment after screening with digital breast tomosynthesis (DBT) versus digital mammography (DM), and to assess associations between features and final outcome of the screening, including immunohistochemical subtypes of the tumour. METHODS This randomized controlled trial was performed in Bergen, Norway, and included 28,749 women, of which 1015 were recalled due to mammographic findings. Mammographic features were classified according to a modified BI-RADS-scale. The distribution were compared using 95 % confidence intervals (CI). RESULTS Asymmetry was the most common feature of all recalls, 24.3 % (108/444) for DBT and 38.9 % (222/571) for DM. Spiculated mass was most common for breast cancer after screening with DBT (36.8 %, 35/95, 95 %CI: 27.2-47.4) while calcifications (23.0 %, 20/87, 95 %CI: 14.6-33.2) was the most frequent after DM. Among women screened with DBT, 0.13 % (95 %CI: 0.08-0.21) had benign outcome after recall due to indistinct mass while the percentage was 0.28 % (95 %CI: 0.20-0.38) for DM. The distributions were 0.70 % (95 %CI: 0.57-0.85) versus 1.46 % (95 %CI: 1.27-1.67) for asymmetry and 0.24 % (95 %CI: 0.16-0.33) versus 0.54 % (95 %CI: 0.43-0.68) for obscured mass, among women screened with DBT versus DM, respectively. Spiculated mass was the most common feature among women diagnosed with non-luminal A-like cancer after DBT and after DM. CONCLUSIONS Spiculated mass was the dominant feature for breast cancer among women screened with DBT while calcifications was the most frequent feature for DM. Further studies exploring the clinical relevance of mammographic features visible particularly on DBT are warranted.
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Affiliation(s)
- H S Aase
- Department of Radiology, Haukeland University Hospital, Bergen, Norway; Department of Clinical Medicine, University of Bergen, 5020, Bergen, Norway.
| | - A S Danielsen
- Section for Breast Cancer Screening, Cancer Registry of Norway, Oslo, Norway; Norwegian Institute of Public Health, Oslo, Norway.
| | - S R Hoff
- Department of Radiology, Møre and Romsdal Hospital Trust, Ålesund, Norway.
| | - Å S Holen
- Section for Breast Cancer Screening, Cancer Registry of Norway, Oslo, Norway.
| | - I S Haldorsen
- Department of Radiology, Haukeland University Hospital, Bergen, Norway; Department of Clinical Medicine, University of Bergen, 5020, Bergen, Norway; Centre for Medical Imaging and Visualization, Haukeland University Hospital, Bergen, Norway.
| | - T Hovda
- Department of Radiology, Vestre Viken Hospital Trust, Drammen, Norway.
| | - B Hanestad
- Department of Radiology, Haukeland University Hospital, Bergen, Norway.
| | - C K Sandvik
- Department of Radiology, Haukeland University Hospital, Bergen, Norway.
| | - S Hofvind
- Section for Breast Cancer Screening, Cancer Registry of Norway, Oslo, Norway; Faculty of Health Sciences, Oslo Metropolitan University, Oslo, Norway.
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Meta-analysis of prospective studies evaluating breast cancer detection and interval cancer rates for digital breast tomosynthesis versus mammography population screening. Eur J Cancer 2021; 148:14-23. [DOI: 10.1016/j.ejca.2021.01.035] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Revised: 01/25/2021] [Accepted: 01/28/2021] [Indexed: 12/11/2022]
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Cressman S, Mar C, Sam J, Kan L, Lohrisch C, Spinelli JJ. The cost-effectiveness of adding tomosynthesis to mammography-based breast cancer screening: an economic analysis. CMAJ Open 2021; 9:E443-E450. [PMID: 33888549 PMCID: PMC8101637 DOI: 10.9778/cmajo.20200154] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Abstract
BACKGROUND Observational studies show that digital breast tomosynthesis (DBT) combined with digital mammography (DM) can reduce recall rates and increases rates of breast cancer detection. The objective of this study was to examine the cost-effectiveness of DBT plus DM versus DM alone in British Columbia and to identify parameters that can improve the efficiency of breast cancer screening programs. METHODS We conducted an economic analysis based on data from a cohort of screening participants in the BC Cancer Breast Screening Program. The decision model simulated lifetime costs and outcomes for participants in breast cancer screening who were aged 40-74 years between 2012 and 2017. We analyzed rates of health care resource utilization, health state costs and estimated incremental cost-effectiveness ratios (ICERs), to measure incremental cost differences per quality-adjusted life years (QALYs) gained from the addition of DBT to DM-based screening, from the government payer's perspective. RESULTS The model simulated economic outcomes for 112 249 screening participants. We found that the ICER was highly sensitive to recall rate reductions and insensitive to parameters related to cancer detection. If DBT plus DM can reduce absolute recall rates by more than 2.1%, the base-case scenario had an ICER of $17 149 per QALY. At a willingness-to-pay threshold of $100 000 per QALY, more than 95% of the probabilistic simulations favoured the adoption of DBT plus DM versus DM alone. The ICER depended heavily on the ability of DBT plus DM to reduce recall rates. INTERPRETATION The addition of DBT to DM would be considered cost-effective owing to the low positive predictive value of screening with DM alone. Reductions in false-positive recall rates should be monitored closely.
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Affiliation(s)
- Sonya Cressman
- Department of Integrative Oncology (Cressman), BC Cancer Research Centre, Vancouver, BC; Faculty of Health Sciences (Cressman), Simon Fraser University, Burnaby, BC; Cancer Screening (Mar, Sam, Kan), BC Cancer; Department of Radiology (Mar), Division of Medical Oncology (Lohrisch) and School of Population and Public Health (Spinelli), Faculty of Medicine, University of British Columbia; Department of Medical Oncology (Lohrisch), BC Cancer; Division of Population Oncology (Spinelli), BC Cancer, Vancouver, BC
| | - Colin Mar
- Department of Integrative Oncology (Cressman), BC Cancer Research Centre, Vancouver, BC; Faculty of Health Sciences (Cressman), Simon Fraser University, Burnaby, BC; Cancer Screening (Mar, Sam, Kan), BC Cancer; Department of Radiology (Mar), Division of Medical Oncology (Lohrisch) and School of Population and Public Health (Spinelli), Faculty of Medicine, University of British Columbia; Department of Medical Oncology (Lohrisch), BC Cancer; Division of Population Oncology (Spinelli), BC Cancer, Vancouver, BC
| | - Janette Sam
- Department of Integrative Oncology (Cressman), BC Cancer Research Centre, Vancouver, BC; Faculty of Health Sciences (Cressman), Simon Fraser University, Burnaby, BC; Cancer Screening (Mar, Sam, Kan), BC Cancer; Department of Radiology (Mar), Division of Medical Oncology (Lohrisch) and School of Population and Public Health (Spinelli), Faculty of Medicine, University of British Columbia; Department of Medical Oncology (Lohrisch), BC Cancer; Division of Population Oncology (Spinelli), BC Cancer, Vancouver, BC
| | - Lisa Kan
- Department of Integrative Oncology (Cressman), BC Cancer Research Centre, Vancouver, BC; Faculty of Health Sciences (Cressman), Simon Fraser University, Burnaby, BC; Cancer Screening (Mar, Sam, Kan), BC Cancer; Department of Radiology (Mar), Division of Medical Oncology (Lohrisch) and School of Population and Public Health (Spinelli), Faculty of Medicine, University of British Columbia; Department of Medical Oncology (Lohrisch), BC Cancer; Division of Population Oncology (Spinelli), BC Cancer, Vancouver, BC
| | - Caroline Lohrisch
- Department of Integrative Oncology (Cressman), BC Cancer Research Centre, Vancouver, BC; Faculty of Health Sciences (Cressman), Simon Fraser University, Burnaby, BC; Cancer Screening (Mar, Sam, Kan), BC Cancer; Department of Radiology (Mar), Division of Medical Oncology (Lohrisch) and School of Population and Public Health (Spinelli), Faculty of Medicine, University of British Columbia; Department of Medical Oncology (Lohrisch), BC Cancer; Division of Population Oncology (Spinelli), BC Cancer, Vancouver, BC
| | - John J Spinelli
- Department of Integrative Oncology (Cressman), BC Cancer Research Centre, Vancouver, BC; Faculty of Health Sciences (Cressman), Simon Fraser University, Burnaby, BC; Cancer Screening (Mar, Sam, Kan), BC Cancer; Department of Radiology (Mar), Division of Medical Oncology (Lohrisch) and School of Population and Public Health (Spinelli), Faculty of Medicine, University of British Columbia; Department of Medical Oncology (Lohrisch), BC Cancer; Division of Population Oncology (Spinelli), BC Cancer, Vancouver, BC
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Moshina N, Larsen M, Holen ÅS, Waade GG, Aase HS, Hofvind S. Digital breast tomosynthesis in a population based mammographic screening program: Breast compression and early performance measures. Eur J Radiol 2021; 139:109665. [PMID: 33823373 DOI: 10.1016/j.ejrad.2021.109665] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2020] [Revised: 03/11/2021] [Accepted: 03/14/2021] [Indexed: 11/24/2022]
Abstract
PURPOSE We aimed to determine if compression force or pressure could be associated with early performance measures for women screened with digital breast tomosynthesis (DBT) in BreastScreen Norway. Early performance measures included rates of consensus, recall, and screen-detected breast cancer. METHOD Data on compression force and pressure, compressed breast thickness and breast characteristics were extracted from an automated software for density assessment of DBT screening examinations for 25,286 women. For descriptive analyses, force (Newton, N) and pressure (kilopascal, kPa) were categorized into quartiles. Analyses were stratified by mammographic view, craniocaudal (CC) and mediolateral oblique (MLO). Logistic regression with restricted cubic splines was used to investigate the association between force and pressure as continuous exposures and early performance measures adjusted for age, compressed breast thickness and fibroglandular volume. RESULTS Mean age of the screened women was 60.7 (SD = 5.2) years. Mean compression force was 90.8 (SD = 14.2) N for CC and 106.3 (SD = 20.6) N for MLO, and pressure was 11.3 (SD = 3.6) kPa for CC and 8.7 (SD = 2.0) kPa for MLO. The highest rates of screen-detected cancer were observed for low force (1.04 % for <82.5 N for CC and 1.07 % for <92.0 N for MLO) and low pressure (1.07 % for <7.2 kPa for MLO). No association was found between force or pressure as continuous exposures and early performance measures in adjusted regression analyses. CONCLUSIONS We found the highest rates of screen-detected cancer for low force and pressure, but no significant association between continuous values of force or pressure and early performance measures in DBT. The findings might indicate that the levels of force and pressure in DBT are of lower significance for screening performance than reported in standard digital mammography.
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Affiliation(s)
| | | | | | - Gunvor G Waade
- Cancer Registry of Norway, Oslo, Norway; Faculty of Health Sciences Oslo Metropolitan University, Oslo, Norway.
| | - Hildegunn S Aase
- Department of Radiology, Haukeland University Hospital, Bergen, Norway; Department of Clinical Medicine, University of Bergen, Bergen, Norway.
| | - Solveig Hofvind
- Cancer Registry of Norway, Oslo, Norway; Faculty of Health Sciences Oslo Metropolitan University, Oslo, Norway.
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Sanmugasiva VV, Ramli Hamid MT, Fadzli F, Rozalli FI, Yeong CH, Ab Mumin N, Rahmat K. Diagnostic accuracy of digital breast tomosynthesis in combination with 2D mammography for the characterisation of mammographic abnormalities. Sci Rep 2020; 10:20628. [PMID: 33244075 PMCID: PMC7691352 DOI: 10.1038/s41598-020-77456-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Accepted: 11/02/2020] [Indexed: 11/22/2022] Open
Abstract
This study aims to assess the diagnostic accuracy of digital breast tomosynthesis in combination with full field digital mammography (DBT + FFDM) in the charaterisation of Breast Imaging-reporting and Data System (BI-RADS) category 3, 4 and 5 lesions. Retrospective cross-sectional study of 390 patients with BI-RADS 3, 4 and 5 mammography with available histopathology examination results were recruited from in a single center of a multi-ethnic Asian population. 2 readers independently reported the FFDM and DBT images and classified lesions detected (mass, calcifications, asymmetric density and architectural distortion) based on American College of Radiology-BI-RADS lexicon. Of the 390 patients recruited, 182 malignancies were reported. Positive predictive value (PPV) of cancer was 46.7%. The PPV in BI-RADS 4a, 4b, 4c and 5 were 6.0%, 38.3%, 68.9%, and 93.1%, respectively. Among all the cancers, 76% presented as masses, 4% as calcifications and 20% as asymmetry. An additional of 4% of cancers were detected on ultrasound. The sensitivity, specificity, PPV and NPV of mass lesions detected on DBT + FFDM were 93.8%, 85.1%, 88.8% and 91.5%, respectively. The PPV for calcification is 61.6% and asymmetry is 60.7%. 81.6% of cancer detected were invasive and 13.3% were in-situ type. Our study showed that DBT is proven to be an effective tool in the diagnosis and characterization of breast lesions and supports the current body of literature that states that integrating DBT to FFDM allows good characterization of breast lesions and accurate diagnosis of cancer.
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Affiliation(s)
- Vithya Visalatchi Sanmugasiva
- Department of Biomedical Imaging, University Malaya Research Imaging Centre, University of Malaya, 50603, Kuala Lumpur, Malaysia
| | - Marlina Tanty Ramli Hamid
- Department of Biomedical Imaging, University Malaya Research Imaging Centre, University of Malaya, 50603, Kuala Lumpur, Malaysia.,Department of Radiology, Faculty of Medicine, University Teknologi MARA, Sungai Buloh, Selangor, Malaysia
| | - Farhana Fadzli
- Department of Biomedical Imaging, University Malaya Research Imaging Centre, University of Malaya, 50603, Kuala Lumpur, Malaysia
| | - Faizatul Izza Rozalli
- Department of Biomedical Imaging, University Malaya Research Imaging Centre, University of Malaya, 50603, Kuala Lumpur, Malaysia
| | - Chai Hong Yeong
- School of Medicine, Faculty of Health and Medical Sciences, Taylor's University, 47500, Subang Jaya, Selangor, Malaysia
| | - Nazimah Ab Mumin
- Department of Biomedical Imaging, University Malaya Research Imaging Centre, University of Malaya, 50603, Kuala Lumpur, Malaysia.,Department of Radiology, Faculty of Medicine, University Teknologi MARA, Sungai Buloh, Selangor, Malaysia
| | - Kartini Rahmat
- Department of Biomedical Imaging, University Malaya Research Imaging Centre, University of Malaya, 50603, Kuala Lumpur, Malaysia.
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Barba D, León-Sosa A, Lugo P, Suquillo D, Torres F, Surre F, Trojman L, Caicedo A. Breast cancer, screening and diagnostic tools: All you need to know. Crit Rev Oncol Hematol 2020; 157:103174. [PMID: 33249359 DOI: 10.1016/j.critrevonc.2020.103174] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 09/18/2020] [Accepted: 11/05/2020] [Indexed: 02/06/2023] Open
Abstract
Breast cancer is one of the most frequent malignancies among women worldwide. Methods for screening and diagnosis allow health care professionals to provide personalized treatments that improve the outcome and survival. Scientists and physicians are working side-by-side to develop evidence-based guidelines and equipment to detect cancer earlier. However, the lack of comprehensive interdisciplinary information and understanding between biomedical, medical, and technology professionals makes innovation of new screening and diagnosis tools difficult. This critical review gathers, for the first time, information concerning normal breast and cancer biology, established and emerging methods for screening and diagnosis, staging and grading, molecular and genetic biomarkers. Our purpose is to address key interdisciplinary information about these methods for physicians and scientists. Only the multidisciplinary interaction and communication between scientists, health care professionals, technical experts and patients will lead to the development of better detection tools and methods for an improved screening and early diagnosis.
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Affiliation(s)
- Diego Barba
- Escuela de Medicina, Universidad San Francisco de Quito USFQ, Quito, Ecuador; Instituto de Investigaciones en Biomedicina, Universidad San Francisco de Quito USFQ, Quito, Ecuador; Mito-Act Research Consortium, Quito, Ecuador
| | - Ariana León-Sosa
- Escuela de Medicina, Universidad San Francisco de Quito USFQ, Quito, Ecuador; Instituto de Investigaciones en Biomedicina, Universidad San Francisco de Quito USFQ, Quito, Ecuador; Mito-Act Research Consortium, Quito, Ecuador
| | - Paulina Lugo
- Hospital de los Valles HDLV, Quito, Ecuador; Fundación Ayuda Familiar y Comunitaria AFAC, Quito, Ecuador
| | - Daniela Suquillo
- Instituto de Investigaciones en Biomedicina, Universidad San Francisco de Quito USFQ, Quito, Ecuador; Mito-Act Research Consortium, Quito, Ecuador; Ingeniería en Procesos Biotecnológicos, Colegio de Ciencias Biológicas y Ambientales COCIBA, Universidad San Francisco de Quito USFQ, Quito, Ecuador
| | - Fernando Torres
- Escuela de Medicina, Universidad San Francisco de Quito USFQ, Quito, Ecuador; Hospital de los Valles HDLV, Quito, Ecuador
| | - Frederic Surre
- University of Glasgow, James Watt School of Engineering, Glasgow, G12 8QQ, United Kingdom
| | - Lionel Trojman
- LISITE, Isep, 75006, Paris, France; Universidad San Francisco de Quito USFQ, Colegio de Ciencias e Ingenierías Politécnico - USFQ, Instituto de Micro y Nanoelectrónica, IMNE, USFQ, Quito, Ecuador
| | - Andrés Caicedo
- Escuela de Medicina, Universidad San Francisco de Quito USFQ, Quito, Ecuador; Instituto de Investigaciones en Biomedicina, Universidad San Francisco de Quito USFQ, Quito, Ecuador; Mito-Act Research Consortium, Quito, Ecuador; Sistemas Médicos SIME, Universidad San Francisco de Quito USFQ, Quito, Ecuador.
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43
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Marsh MW, Benefield TS, Lee S, Pritchard M, Earnhardt K, Agans R, Henderson LM. Availability Versus Utilization of Supplemental Breast Cancer Screening Post Passage of Breast Density Legislation. J Womens Health (Larchmt) 2020; 30:579-586. [PMID: 32960137 DOI: 10.1089/jwh.2020.8528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Objective: Despite the lack of evidence that supplemental screening in women with dense breasts reduces breast cancer mortality, 38 states have passed breast density legislation, with some including recommendations for supplemental screening. The objective of this study is to compare the availability versus use of supplemental breast cancer screening modalities and determine factors driving use of supplemental screening in rural versus urban settings. Methods: A 50-item mailed survey using the Tailored Design Method was sent to American College of Radiology mammography-accredited facilities in North Carolina in 2017. Respondents included 94 facilities (48 rural and 46 urban locations). Survey questions focused on breast cancer and supplemental screening services, breast density, risk factors/assessment, and facility demographics. Results: The survey response rate was 60.3% (94/156). Among the 94 respondents, 64.0% (n = 60) reported availability of any type of supplemental screening (digital breast tomosynthesis [DBT], ultrasound, or magnetic resonance imaging [MRI]). In facilities where supplemental screening modalities were available, the most commonly utilized supplemental screening modality was DBT (96.4%), compared with ultrasound (35.7%) and MRI (46.7%). Facilities reported using supplemental screening based on patient breast density (48.3%), referring physician recommendation (63.3%), reading radiologist recommendation (63.3%), breast cancer risk factors (48.3%), and patient request (40.0%). Urban facilities were more likely than rural facilities to base supplemental screening on breast cancer risk factors (62.5% vs. 32.1%; p-value = 0.019), referring physician (75.0% vs. 50.0%; p-value = 0.045), and reading radiologist (78.1% vs. 46.4%; p-value = 0.011). Conclusion: In our study, supplemental screening modalities were widely available, with facilities more likely to use DBT for supplemental screening compared to other modalities.
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Affiliation(s)
- Mary W Marsh
- Department of Radiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Thad S Benefield
- Department of Radiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Sheila Lee
- Department of Radiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Michael Pritchard
- Department of Radiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Katie Earnhardt
- Department of Radiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Robert Agans
- Department of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Louise M Henderson
- Department of Radiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,Department of Epidemiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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Vancoillie L, Marshall N, Cockmartin L, Vignero J, Zhang G, Bosmans H. Verification of the accuracy of a hybrid breast imaging simulation framework for virtual clinical trial applications. J Med Imaging (Bellingham) 2020; 7:042804. [PMID: 32341939 PMCID: PMC7175415 DOI: 10.1117/1.jmi.7.4.042804] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Accepted: 04/06/2020] [Indexed: 11/14/2022] Open
Abstract
Purpose: The impact of system parameters on signal detectability can be studied with simulation platforms. We describe the steps taken to verify and confirm the accuracy of a local platform developed for the use in virtual clinical trials. Approach: The platform simulates specific targets into existing two-dimensional full-field digital mammography and digital breast tomosynthesis images acquired on a Siemens Inspiration system. There are three steps: (1) creation of voxel models or analytical objects; (2) generation of a realistic object template with accurate resolution, scatter, and noise properties; and (3) insertion and reconstruction. Four objects were simulated: a 0.5-mm aluminium (Al) sphere and a 0.2-mm-thick Al sheet in a PMMA stack, a 0.8-mm steel edge and a three-dimensional mass model in a structured background phantom. Simulated results were compared to acquired data. Results: Peak contrast and signal difference-to-noise ratio (SDNR) were in close agreement ( < 5 % error) for both sphere and sheet. The similarity of pixel value profiles for sphere and sheet in the x y direction and the artifact spread function for real and simulated spheres confirmed accurate geometric modeling. Absolute and relative average deviation between modulation transfer function measured from a real and simulated edges showed accurate sharpness modelling for spatial frequencies up to the Nyquist frequency. Real and simulated objects could not be differentiated visually. Conclusions: The results indicate that this simulation framework is a strong candidate for use in virtual clinical studies.
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Affiliation(s)
- Liesbeth Vancoillie
- KU Leuven, Division of Medical Physics and Quality Assessment, Department of Imaging and Pathology, Leuven, Belgium
| | - Nicholas Marshall
- KU Leuven, Division of Medical Physics and Quality Assessment, Department of Imaging and Pathology, Leuven, Belgium
- UZ Leuven, Department of Radiology, Leuven, Belgium
| | | | | | - Guozhi Zhang
- UZ Leuven, Department of Radiology, Leuven, Belgium
| | - Hilde Bosmans
- KU Leuven, Division of Medical Physics and Quality Assessment, Department of Imaging and Pathology, Leuven, Belgium
- UZ Leuven, Department of Radiology, Leuven, Belgium
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Scott AM, Lashley MG, Drury NB, Dale PS. Comparison of Call-Back Rates between Digital Mammography and Digital Breast Tomosynthesis. Am Surg 2020. [DOI: 10.1177/000313481908500837] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The effect of mammographic screening on the natural history and evolution of breast cancer treatment cannot be overstated; however, despite intensive and resource consuming screening, advanced breast cancer is still diagnosed frequently. The development of three-dimensional mammography or digital breast tomosynthesis (DBT) has already demonstrated greater sensitivity in the diagnosis of breast pathology and effectiveness in identifying early breast cancers. In addition to being a more sensitive screening tool, other studies indicate DBT has a lower call-back rate when compared with traditional DM. This study compares call-back rates between these two screening tools. A single institution, retrospective review was conducted of almost 20,000 patient records who underwent digital mammography or DBTin the years 2016 to 2018. These charts were analyzed for documentation of imaging type, Breast Imaging Reporting and Data System 0 status, call-back status, and type of further imaging that was required. Charts for 19,863 patients were reviewed, 17,899 digital mammography examinations were conducted compared with 11,331 DBT examinations resulting in 1,066 and 689 Breast Imaging Reporting and Data System 0 studies, respectively. Of the DM call-backs, 82.08 per cent were recommended for additional radiographic imaging and 17.82 per cent for ultrasound imaging. In the DBT group, only 39.77 per cent of callbacks were recommended for additional radiographic imaging and 60.09 per cent for ultrasound imaging. Our data suggest that DBT results in less call-back for additional mammographic images as compared with digital mammography. DBT may offer benefits over DM, including less imaging before biopsy, less time before biopsy, quicker diagnosis, and improved patient satisfaction.
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Affiliation(s)
- Anthony M. Scott
- From the Department of General Surgery, Medical Center Navicent Health, Macon, Georgia
| | - Madison G. Lashley
- From the Department of General Surgery, Medical Center Navicent Health, Macon, Georgia
| | - Nicholas B. Drury
- From the Department of General Surgery, Medical Center Navicent Health, Macon, Georgia
| | - Paul S. Dale
- From the Department of General Surgery, Medical Center Navicent Health, Macon, Georgia
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46
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Sprague BL, Coley RY, Kerlikowske K, Rauscher GH, Henderson LM, Onega T, Lee CI, Herschorn SD, Tosteson ANA, Miglioretti DL. Assessment of Radiologist Performance in Breast Cancer Screening Using Digital Breast Tomosynthesis vs Digital Mammography. JAMA Netw Open 2020; 3:e201759. [PMID: 32227180 PMCID: PMC7292996 DOI: 10.1001/jamanetworkopen.2020.1759] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Importance Many US radiologists have screening mammography recall rates above the expert-recommended threshold of 12%. The influence of digital breast tomosynthesis (DBT) on the distribution of radiologist recall rates is uncertain. Objective To evaluate radiologists' recall and cancer detection rates before and after beginning interpretation of DBT examinations. Design, Setting, and Participants This cohort study included 198 radiologists from 104 radiology facilities in the Breast Cancer Surveillance Consortium who interpreted 251 384 DBT and 2 000 681 digital mammography (DM) screening examinations from 2009 to 2017, including 126 radiologists (63.6%) who interpreted DBT examinations during the study period and 72 (36.4%) who exclusively interpreted DM examinations (to adjust for secular trends). Data were analyzed from April 2018 to July 2019. Exposures Digital breast tomosynthesis and DM screening examinations. Main Outcomes and Measures Recall rate and cancer detection rate. Results A total of 198 radiologists interpreted 2 252 065 DM and DBT examinations (2 000 681 [88.8%] DM examinations; 251 384 [11.2%] DBT examinations; 710 934 patients [31.6%] aged 50-59 years; 1 448 981 [64.3%] non-Hispanic white). Among the 126 radiologists (63.6%) who interpreted DBT examinations, 83 (65.9%) had unadjusted DM recall rates of no more than 12% before using DBT, with a median (interquartile range) recall rate of 10.0% (7.5%-13.0%). On DBT examinations, 96 (76.2%) had an unadjusted recall rate of no more than 12%, with a median (interquartile range) recall rate of 8.8% (6.3%-11.3%). A secular trend in recall rate was observed, with the multivariable-adjusted risk of recall on screening examinations declining by 1.2% (95% CI, 0.9%-1.5%) per year. After adjusting for examination characteristics and secular trends, recall rates were 15% lower on DBT examinations compared with DM examinations interpreted before DBT use (relative risk, 0.85; 95% CI, 0.83-0.87). Adjusted recall rates were significantly lower on DBT examinations compared with DM examinations interpreted before DBT use for 45 radiologists (35.7%) and significantly higher for 18 (14.3%); 63 (50.0%) had no statistically significant change. The unadjusted cancer detection rate on DBT was 5.3 per 1000 examinations (95% CI, 5.0-5.7 per 1000 examinations) compared with 4.7 per 1000 examinations (95% CI, 4.6-4.8 per 1000 examinations) on DM examinations interpreted before DM use (multivariable-adjusted risk ratio, 1.21; 95% CI, 1.11-1.33). Conclusions and Relevance In this study, DBT was associated with an overall decrease in recall rate and an increase in cancer detection rate. However, our results indicated that there is wide variability among radiologists, including a subset of radiologists who experienced increased recall rates on DBT examinations. Radiology practices should audit radiologist DBT screening performance and consider additional DBT training for radiologists whose performance does not improve as expected.
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Affiliation(s)
- Brian L Sprague
- Department of Surgery, University of Vermont Cancer Center, University of Vermont, Burlington
- Department of Radiology, University of Vermont Cancer Center, University of Vermont, Burlington
| | - R Yates Coley
- Kaiser Permanente Washington Health Research Institute, Seattle
| | - Karla Kerlikowske
- Department of Medicine, University of California, San Francisco
- General Internal Medicine Section, Department of Veterans Affairs, University of California, San Francisco
- Department of Epidemiology and Biostatistics, University of California, San Francisco
| | - Garth H Rauscher
- Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois at Chicago
| | - Louise M Henderson
- Department of Radiology, University of North Carolina, Chapel Hill
- Department of Epidemiology, University of North Carolina, Chapel Hill
| | - Tracy Onega
- Department of Biomedical Data Science, The Dartmouth Institute for Health Policy and Clinical Practice and Norris Cotton Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire
- Department of Epidemiology, The Dartmouth Institute for Health Policy and Clinical Practice and Norris Cotton Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire
| | - Christoph I Lee
- Department of Radiology, University of Washington School of Medicine, Seattle
- Hutchinson Institute for Cancer Outcomes Research, Seattle, Washington
| | - Sally D Herschorn
- Department of Radiology, University of Vermont Cancer Center, University of Vermont, Burlington
| | - Anna N A Tosteson
- The Dartmouth Institute for Health Policy and Clinical Practice, Lebanon, New Hampshire
- Norris Cotton Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire
| | - Diana L Miglioretti
- Division of Biostatistics, Department of Public Health Sciences, University of California Davis School of Medicine, Davis
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle
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Comparing Diagnostic Performance of Digital Breast Tomosynthesis and Full-Field Digital Mammography. J Am Coll Radiol 2020; 17:999-1003. [PMID: 32068009 DOI: 10.1016/j.jacr.2020.01.010] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Revised: 01/14/2020] [Accepted: 01/16/2020] [Indexed: 11/24/2022]
Abstract
OBJECTIVE Compare diagnostic performance of screening full-field digital mammography (FFDM), a hybrid FFDM and digital breast tomosynthesis (DBT) environment, and DBT only. MATERIALS AND METHODS This institutional review board-approved, retrospective study consisted of all patients undergoing screening mammography at an urban academic medical center and outpatient imaging facility between January 1, 2011, and December 31, 2017. We used the electronic health record data warehouse to extract report data and patient demographics. A validated natural language processing algorithm extracted BI-RADS score from each report. An institutional cancer registry identified cancer diagnoses. Primary outcomes of recall rate, cancer detection rate (CDR), and positive predictive value 1 (PPV1) were calculated for three periods: FFDM-only environment, hybrid environment, and DBT-only environment. A χ2 test was used to compare recall rate, CDR, and PPV1. RESULTS A total of 179,028 screening mammograms comprised the study cohort: 41,818 (23.3%) during the FFDM-only period, 83,125 (46.4%) during the hybrid period, and 54,084 (30.2%) during the DBT-only period. Recall rates were 10.4% (4,279 of 41,280) for the FFDM-only period, 10.6% (8,761 of 82,917) for the hybrid period, and 10.8% (5,850 of 54,020) for the DBT-only period (P = .96). CDR (cancers per 1,000 examinations) was 2.6 per 1,000, 4.9 per 1,000, and 6.0 per 1,000 for FFDM only, hybrid, and DBT only, respectively (P < .01). PPV1s (number of cancers per number of recalls) were 2.5% for the FFDM-only period, 4.6% for the hybrid period, and 5.6% for the DBT-only period (P < .01). CONCLUSION Recall rates were not significantly different within the three periods in the breast imaging practice. However, PPV1 and CDR were significantly higher with DBT only.
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Hovda T, Holen ÅS, Lång K, Albertsen JL, Bjørndal H, Brandal SHB, Sahlberg KK, Skaane P, Suhrke P, Hofvind S. Interval and Consecutive Round Breast Cancer after Digital Breast Tomosynthesis and Synthetic 2D Mammography versus Standard 2D Digital Mammography in BreastScreen Norway. Radiology 2020; 294:256-264. [DOI: 10.1148/radiol.2019191337] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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49
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Petropoulos AE, Skiadopoulos SG, Karahaliou AN, Messaris GAT, Arikidis NS, Costaridou LI. Quantitative assessment of microcalcification cluster image quality in digital breast tomosynthesis, 2-dimensional and synthetic mammography. Med Biol Eng Comput 2019; 58:187-209. [DOI: 10.1007/s11517-019-02072-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2019] [Accepted: 11/05/2019] [Indexed: 12/01/2022]
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50
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Kamal RM, Moustafa AFI, Fakhry S, Kamal EF, Radwan A, Hilal A, Hassan M. Adding the merits of contrast to the ease of mammography; can we highlight what’s behind breast asymmetries? THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2019. [DOI: 10.1186/s43055-019-0039-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
Breast symmetry is one of the major things that radiologists assess when looking at mammograms and is one of the most challenging mammographic findings to evaluate. Contrast-enhanced spectral mammography (CESM) is an emerging mammography technique that has shown comparable sensitivity and specificity to MRI. The purpose of this study is to assess the value of CESM in characterization of breast asymmetries (BAs) and if it should be incorporated in its diagnostic work-up.
Results
Three hundred sixty-five patients with mean age of 47 years were included in the study. CESM was performed aiming for characterization of 380 suspicious or indeterminate breast asymmetries. Assessment of subtracted high-energy images (HEI) markedly improves the overall accuracy reaching 88.4%. Further improvement of the overall accuracy was achieved on combined assessment of the low-energy images (LEI), subtracted high-energy images (HEI), and ultrasound reaching 91.3%.
Conclusion
CESM is considered as a valuable complementary imaging tool considering the evaluation of breast asymmetries and should be incorporated in its diagnostic work-up in cases not resolved on an initial combined mammography and targeted ultrasound study especially in the presence of a heterogeneous dense breast parenchyma. Yet, this may be hindered in the presence of inflammatory signs because of the overlapping imaging criteria.
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