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Aribal E, Seker ME, Guldogan N, Yilmaz E. Value of automated breast ultrasound in screening: Standalone and as a supplemental to digital breast tomosynthesis. Int J Cancer 2024; 155:1466-1475. [PMID: 38989802 DOI: 10.1002/ijc.35093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Revised: 06/22/2024] [Accepted: 06/27/2024] [Indexed: 07/12/2024]
Abstract
We aimed to determine the value of standalone and supplemental automated breast ultrasound (ABUS) in detecting cancers in an opportunistic screening setting with digital breast tomosynthesis (DBT) and compare this combined screening method to DBT and ABUS alone in women older than 39 years with BI-RADS B-D density categories. In this prospective opportunistic screening study, 3466 women aged 39 or older with BI-RADS B-D density categories and with a mean age of 50 were included. The screening protocol consisted of DBT mediolateral-oblique views, 2D craniocaudal views, and ABUS with three projections for both breasts. ABUS was evaluated blinded to mammography findings. Statistical analysis evaluated diagnostic performance for DBT, ABUS, and combined workflows. Twenty-nine cancers were screen-detected. ABUS and DBT exhibited the same cancer detection rates (CDR) at 7.5/1000 whereas DBT + ABUS showed 8.4/1000, with ABUS contributing an additional CDR of 0.9/1000. Standalone ABUS outperformed DBT in detecting 12.5% more invasive cancers. DBT displayed better accuracy (95%) compared to ABUS (88%) and combined approach (86%). Sensitivities for DBT and ABUS were the same (84%), with DBT + ABUS showing a higher rate (94%). DBT outperformed ABUS in specificity (95% vs. 88%). DBT + ABUS exhibited a higher recall rate (14.89%) compared to ABUS (12.38%) and DBT (6.03%) (p < .001). Standalone ABUS detected more invasive cancers compared to DBT, with a higher recall rate. The combined approach showed a higher CDR by detecting one additional cancer per thousand.
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Affiliation(s)
- Erkin Aribal
- School of Medicine, Department of Radiology, Acibadem Mehmet Ali Aydinlar University, Istanbul, Turkey
- Department of Radiology, Acibadem Altunizade Hospital, Istanbul, Turkey
| | - Mustafa Ege Seker
- School of Medicine, Department of Radiology, Acibadem Mehmet Ali Aydinlar University, Istanbul, Turkey
| | - Nilgün Guldogan
- Department of Radiology, Acibadem Altunizade Hospital, Istanbul, Turkey
| | - Ebru Yilmaz
- Department of Radiology, Acibadem Altunizade Hospital, Istanbul, Turkey
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2
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Winkelman AJ, Tulenko K, Epstein SH, Nguyen JV, Ford C, Miller MM. Breast Cancer Screening With Automated Breast US and Mammography vs Handheld US and Mammography in Women With Dense Breasts in a Real-World Clinical Setting. JOURNAL OF BREAST IMAGING 2024; 6:493-501. [PMID: 39036960 DOI: 10.1093/jbi/wbae039] [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: 12/18/2023] [Indexed: 07/23/2024]
Abstract
OBJECTIVE We compared the performance of 2 breast cancer screening approaches, automated breast US (ABUS) with same-day mammography (ABUS/MG) and handheld US (HHUS) with same-day mammography (HHUS/MG), in women with dense breasts to better understand the relative usefulness of ABUS and HHUS in a real-world clinical setting. METHODS In this institutional review board-approved, retrospective observational study, we evaluated all ABUS/MG and HHUS/MG screening examinations performed at our institution from May 2013 to September 2021. BI-RADS categories, biopsy pathology results, and diagnostic test characteristics (eg, sensitivity, specificity) were compared between the 2 screening approaches using Fisher's exact test. RESULTS A total of 1120 women with dense breasts were included in this study, with 852 undergoing ABUS/MG and 268 undergoing HHUS/MG. The sensitivities of ABUS/MG and HHUS/MG were 100% (5/5) and 75.0% (3/4), respectively, which was not a statistically significant difference (P = .444). The ABUS/MG approach demonstrated a slightly higher specificity (97.4% [825/847] vs 94.3% [249/264]; P = .028), higher accuracy (97.4% [830/852] vs 94.0% [252/268]; P = .011), and lower biopsy recommendation rate (3.2% [27/852] vs 6.7% [18/268]; P = .019) than the HHUS/MG approach in our patient population. CONCLUSION Our findings suggest that ABUS/MG performs comparably with HHUS/MG as a breast cancer screening approach in women with dense breasts in a real-world clinical setting, with the ABUS/MG approach demonstrating a similar sensitivity and slightly higher specificity than the HHUS/MG approach. Additional variables, such as patient experience and physician time, may help determine which imaging approach to employ in specific clinical settings.
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Affiliation(s)
- Andrew J Winkelman
- Department of Radiology, University of Michigan Health System, Ann Arbor, MI, USA
| | | | - Samantha H Epstein
- Department of Radiology and Medical Imaging, University of Virginia Health System, Charlottesville, VA, USA
| | - Jonathan V Nguyen
- Department of Radiology and Medical Imaging, University of Virginia Health System, Charlottesville, VA, USA
| | - Clay Ford
- Senior Research Data Scientist/Statistics, University of Virginia Health System, Charlottesville, VA, USA
| | - Matthew M Miller
- Department of Radiology and Medical Imaging, University of Virginia Health System, Charlottesville, VA, USA
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3
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Yamashita MW, Larsen LH, Perez J, Edwards AV, Papaioannou J, Jiang Y. Comparison of Mammography and Mammography with Supplemental Whole-Breast US Tomography for Cancer Detection in Patients with Dense Breasts. Radiology 2024; 311:e231680. [PMID: 38888480 DOI: 10.1148/radiol.231680] [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: 06/20/2024]
Abstract
BACKGROUND Women with dense breasts benefit from supplemental cancer screening with US, but US has low specificity. PURPOSE To evaluate the performance of breast US tomography (UST) combined with full-field digital mammography (FFDM) compared with FFDM alone for breast cancer screening in women with dense breasts. MATERIALS AND METHODS This retrospective multireader multicase study included women with dense breasts who underwent FFDM and UST at 10 centers between August 2017 and October 2019 as part of a prospective case collection registry. All patients in the registry with cancer were included; patients with benign biopsy or negative follow-up imaging findings were randomly selected for inclusion. Thirty-two Mammography Quality Standards Act-qualified radiologists independently evaluated FFDM followed immediately by FFDM plus UST for suspicious findings and assigned a Breast Imaging Reporting and Data System (BI-RADS) category. The superiority of FFDM plus UST versus FFDM alone for cancer detection (assessed with area under the receiver operating characteristic curve [AUC]), BI-RADS 4 sensitivity, and BI-RADS 3 sensitivity and specificity were evaluated using the two-sided significance level of α = .05. Noninferiority of BI-RADS 4 specificity was evaluated at the one-sided significance level of α = .025 with a -10% margin. RESULTS Among 140 women (mean age, 56 years ±10 [SD]; 36 with cancer, 104 without), FFDM plus UST achieved superior performance compared with FFDM alone (AUC, 0.60 [95% CI: 0.51, 0.69] vs 0.54 [95% CI: 0.45, 0.64]; P = .03). For FFDM plus UST versus FFDM alone, BI-RADS 4 mean sensitivity was superior (37% [428 of 1152] vs 30% [343 of 1152]; P = .03) and BI-RADS 4 mean specificity was noninferior (82% [2741 of 3328] vs 88% [2916 of 3328]; P = .004). For FFDM plus UST versus FFDM, no difference in BI-RADS 3 mean sensitivity was observed (40% [461 of 1152] vs 33% [385 of 1152]; P = .08), but BI-RADS 3 mean specificity was superior (75% [2491 of 3328] vs 69% [2299 of 3328]; P = .04). CONCLUSION In women with dense breasts, FFDM plus UST improved cancer detection by radiologists versus FFDM alone. Clinical trial registration nos. NCT03257839 and NCT04260620 Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Mann in this issue.
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Affiliation(s)
- Mary W Yamashita
- From the Department of Radiology, University of Southern California, Keck School of Medicine, Keck Hospital, 1500 San Pablo St, 2nd Floor, Suite 2250, Los Angeles, CA 90033 (M.W.Y., L.H.L.); Department of Biostatistics, Avania U.S., Marlborough, Mass (J. Perez); and Department of Radiology, The University of Chicago, Chicago, Ill (A.V.E., J. Papaioannou, Y.J.)
| | - Linda H Larsen
- From the Department of Radiology, University of Southern California, Keck School of Medicine, Keck Hospital, 1500 San Pablo St, 2nd Floor, Suite 2250, Los Angeles, CA 90033 (M.W.Y., L.H.L.); Department of Biostatistics, Avania U.S., Marlborough, Mass (J. Perez); and Department of Radiology, The University of Chicago, Chicago, Ill (A.V.E., J. Papaioannou, Y.J.)
| | - Jeremiah Perez
- From the Department of Radiology, University of Southern California, Keck School of Medicine, Keck Hospital, 1500 San Pablo St, 2nd Floor, Suite 2250, Los Angeles, CA 90033 (M.W.Y., L.H.L.); Department of Biostatistics, Avania U.S., Marlborough, Mass (J. Perez); and Department of Radiology, The University of Chicago, Chicago, Ill (A.V.E., J. Papaioannou, Y.J.)
| | - Alexandra V Edwards
- From the Department of Radiology, University of Southern California, Keck School of Medicine, Keck Hospital, 1500 San Pablo St, 2nd Floor, Suite 2250, Los Angeles, CA 90033 (M.W.Y., L.H.L.); Department of Biostatistics, Avania U.S., Marlborough, Mass (J. Perez); and Department of Radiology, The University of Chicago, Chicago, Ill (A.V.E., J. Papaioannou, Y.J.)
| | - John Papaioannou
- From the Department of Radiology, University of Southern California, Keck School of Medicine, Keck Hospital, 1500 San Pablo St, 2nd Floor, Suite 2250, Los Angeles, CA 90033 (M.W.Y., L.H.L.); Department of Biostatistics, Avania U.S., Marlborough, Mass (J. Perez); and Department of Radiology, The University of Chicago, Chicago, Ill (A.V.E., J. Papaioannou, Y.J.)
| | - Yulei Jiang
- From the Department of Radiology, University of Southern California, Keck School of Medicine, Keck Hospital, 1500 San Pablo St, 2nd Floor, Suite 2250, Los Angeles, CA 90033 (M.W.Y., L.H.L.); Department of Biostatistics, Avania U.S., Marlborough, Mass (J. Perez); and Department of Radiology, The University of Chicago, Chicago, Ill (A.V.E., J. Papaioannou, Y.J.)
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Al-Karawi D, Al-Zaidi S, Helael KA, Obeidat N, Mouhsen AM, Ajam T, Alshalabi BA, Salman M, Ahmed MH. A Review of Artificial Intelligence in Breast Imaging. Tomography 2024; 10:705-726. [PMID: 38787015 PMCID: PMC11125819 DOI: 10.3390/tomography10050055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 04/14/2024] [Accepted: 05/06/2024] [Indexed: 05/25/2024] Open
Abstract
With the increasing dominance of artificial intelligence (AI) techniques, the important prospects for their application have extended to various medical fields, including domains such as in vitro diagnosis, intelligent rehabilitation, medical imaging, and prognosis. Breast cancer is a common malignancy that critically affects women's physical and mental health. Early breast cancer screening-through mammography, ultrasound, or magnetic resonance imaging (MRI)-can substantially improve the prognosis for breast cancer patients. AI applications have shown excellent performance in various image recognition tasks, and their use in breast cancer screening has been explored in numerous studies. This paper introduces relevant AI techniques and their applications in the field of medical imaging of the breast (mammography and ultrasound), specifically in terms of identifying, segmenting, and classifying lesions; assessing breast cancer risk; and improving image quality. Focusing on medical imaging for breast cancer, this paper also reviews related challenges and prospects for AI.
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Affiliation(s)
- Dhurgham Al-Karawi
- Medical Analytica Ltd., 26a Castle Park Industrial Park, Flint CH6 5XA, UK;
| | - Shakir Al-Zaidi
- Medical Analytica Ltd., 26a Castle Park Industrial Park, Flint CH6 5XA, UK;
| | - Khaled Ahmad Helael
- Royal Medical Services, King Hussein Medical Hospital, King Abdullah II Ben Al-Hussein Street, Amman 11855, Jordan;
| | - Naser Obeidat
- Department of Diagnostic Radiology and Nuclear Medicine, Faculty of Medicine, Jordan University of Science and Technology, Irbid 22110, Jordan; (N.O.); (A.M.M.); (T.A.); (B.A.A.); (M.S.)
| | - Abdulmajeed Mounzer Mouhsen
- Department of Diagnostic Radiology and Nuclear Medicine, Faculty of Medicine, Jordan University of Science and Technology, Irbid 22110, Jordan; (N.O.); (A.M.M.); (T.A.); (B.A.A.); (M.S.)
| | - Tarek Ajam
- Department of Diagnostic Radiology and Nuclear Medicine, Faculty of Medicine, Jordan University of Science and Technology, Irbid 22110, Jordan; (N.O.); (A.M.M.); (T.A.); (B.A.A.); (M.S.)
| | - Bashar A. Alshalabi
- Department of Diagnostic Radiology and Nuclear Medicine, Faculty of Medicine, Jordan University of Science and Technology, Irbid 22110, Jordan; (N.O.); (A.M.M.); (T.A.); (B.A.A.); (M.S.)
| | - Mohamed Salman
- Department of Diagnostic Radiology and Nuclear Medicine, Faculty of Medicine, Jordan University of Science and Technology, Irbid 22110, Jordan; (N.O.); (A.M.M.); (T.A.); (B.A.A.); (M.S.)
| | - Mohammed H. Ahmed
- School of Computing, Coventry University, 3 Gulson Road, Coventry CV1 5FB, UK;
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Alves KL, Freitas R, Paulinelli RR, Borges MN. Comparison of medical image interpretation time between conventional and automated methods of breast ultrasound. REVISTA BRASILEIRA DE GINECOLOGIA E OBSTETRÍCIA 2024; 46:e-rbgo15. [PMID: 38765504 PMCID: PMC11075416 DOI: 10.61622/rbgo/2024ao15] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 08/03/2023] [Indexed: 05/22/2024] Open
Abstract
Objective To compare the medical image interpretation's time between the conventional and automated methods of breast ultrasound in patients with breast lesions. Secondarily, to evaluate the agreement between the two methods and interobservers. Methods This is a cross-sectional study with prospective data collection. The agreement's degrees were established in relation to the breast lesions's ultrasound descriptors. To determine the accuracy of each method, a biopsy of suspicious lesions was performed, considering the histopathological result as the diagnostic gold standard. Results We evaluated 27 women. Conventional ultrasound used an average medical time of 10.77 minutes (± 2.55) greater than the average of 7.38 minutes (± 2.06) for automated ultrasound (p<0.001). The degrees of agreement between the methods ranged from 0.75 to 0.95 for researcher 1 and from 0.71 to 0.98 for researcher 2. Among the researchers, the degrees of agreement were between 0.63 and 1 for automated ultrasound and between 0.68 and 1 for conventional ultrasound. The area of the ROC curve for the conventional method was 0.67 (p=0.003) for researcher 1 and 0.72 (p<0.001) for researcher 2. The area of the ROC curve for the automated method was 0. 69 (p=0.001) for researcher 1 and 0.78 (p<0.001) for researcher 2. Conclusion We observed less time devoted by the physician to automated ultrasound compared to conventional ultrasound, maintaining accuracy. There was substantial or strong to perfect interobserver agreement and substantial or strong to almost perfect agreement between the methods.
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Affiliation(s)
- Katyane Larissa Alves
- Universidade Federal de GoiásGoiâniaGOBrazilUniversidade Federal de Goiás, Goiânia, GO, Brazil.
| | - Ruffo Freitas
- Universidade Federal de GoiásGoiâniaGOBrazilUniversidade Federal de Goiás, Goiânia, GO, Brazil.
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Al Muhaisen S, Safi O, Ulayan A, Aljawamis S, Fakhoury M, Baydoun H, Abuquteish D. Artificial Intelligence-Powered Mammography: Navigating the Landscape of Deep Learning for Breast Cancer Detection. Cureus 2024; 16:e56945. [PMID: 38665752 PMCID: PMC11044525 DOI: 10.7759/cureus.56945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/26/2024] [Indexed: 04/28/2024] Open
Abstract
Worldwide, breast cancer (BC) is one of the most commonly diagnosed malignancies in women. Early detection is key to improving survival rates and health outcomes. This literature review focuses on how artificial intelligence (AI), especially deep learning (DL), can enhance the ability of mammography, a key tool in BC detection, to yield more accurate results. Artificial intelligence has shown promise in reducing diagnostic errors and increasing early cancer detection chances. Nevertheless, significant challenges exist, including the requirement for large amounts of high-quality data and concerns over data privacy. Despite these hurdles, AI and DL are advancing the field of radiology, offering better ways to diagnose, detect, and treat diseases. The U.S. Food and Drug Administration (FDA) has approved several AI diagnostic tools. Yet, the full potential of these technologies, especially for more advanced screening methods like digital breast tomosynthesis (DBT), depends on further clinical studies and the development of larger databases. In summary, this review highlights the exciting potential of AI in BC screening. It calls for more research and validation to fully employ the power of AI in clinical practice, ensuring that these technologies can help save lives by improving diagnosis accuracy and efficiency.
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Affiliation(s)
| | - Omar Safi
- Medicine, Faculty of Medicine, The Hashemite University, Zarqa, JOR
| | - Ahmad Ulayan
- Medicine, Faculty of Medicine, The Hashemite University, Zarqa, JOR
| | - Sara Aljawamis
- Medicine, Faculty of Medicine, The Hashemite University, Zarqa, JOR
| | - Maryam Fakhoury
- Medicine, Faculty of Medicine, The Hashemite University, Zarqa, JOR
| | - Haneen Baydoun
- Diagnostic Radiology, King Hussein Cancer Center, Amman, JOR
| | - Dua Abuquteish
- Microbiology, Pathology and Forensic Medicine, Faculty of Medicine, The Hashemite University, Zarqa, JOR
- Pathology and Laboratory Medicine, King Hussein Cancer Center, Amman, JOR
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7
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Kwon MR, Youn I, Lee MY, Lee HA. Diagnostic Performance of Artificial Intelligence-Based Computer-Aided Detection Software for Automated Breast Ultrasound. Acad Radiol 2024; 31:480-491. [PMID: 37813703 DOI: 10.1016/j.acra.2023.09.013] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 08/25/2023] [Accepted: 09/12/2023] [Indexed: 10/11/2023]
Abstract
RATIONALE AND OBJECTIVES This study aimed to evaluate the diagnostic performance of radiologists following the utilization of artificial intelligence (AI)-based computer-aided detection software (CAD) in detecting suspicious lesions in automated breast ultrasounds (ABUS). MATERIALS AND METHODS ABUS-detected 262 breast lesions (histopathological verification; January 2020 to December 2022) were included. Two radiologists reviewed the images and assigned a Breast Imaging Reporting and Data System (BI-RADS) category. ABUS images were classified as positive or negative using AI-CAD. The BI-RADS category was readjusted in four ways: the radiologists modified the BI-RADS category using the AI results (AI-aided 1), upgraded or downgraded based on AI results (AI-aided 2), only upgraded for positive results (AI-aided 3), or only downgraded for negative results (AI-aided 4). The AI-aided diagnostic performances were compared to radiologists. The AI-CAD-positive and AI-CAD-negative cancer characteristics were compared. RESULTS For 262 lesions (145 malignant and 117 benign) in 231 women (mean age, 52.2 years), the area under the receiver operator characteristic curve (AUC) of radiologists was 0.870 (95% confidence interval [CI], 0.832-0.908). The AUC significantly improved to 0.919 (95% CI, 0.890-0.947; P = 0.001) using AI-aided 1, whereas it improved without significance to 0.884 (95% CI, 0.844-0.923), 0.890 (95% CI, 0.852-0.929), and 0.890 (95% CI, 0.853-0.928) using AI-aided 2, 3, and 4, respectively. AI-CAD-negative cancers were smaller, less frequently exhibited retraction phenomenon, and had lower BI-RADS category. Among nonmass lesions, AI-CAD-negative cancers showed no posterior shadowing. CONCLUSION AI-CAD implementation significantly improved the radiologists' diagnostic performance and may serve as a valuable diagnostic tool.
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Affiliation(s)
- Mi-Ri Kwon
- Department of Radiology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, 29 Saemunan-ro, Jongno-gu, Seoul, 03181, Republic of Korea (M.K., I.Y., H.-A.L.)
| | - Inyoung Youn
- Department of Radiology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, 29 Saemunan-ro, Jongno-gu, Seoul, 03181, Republic of Korea (M.K., I.Y., H.-A.L.).
| | - Mi Yeon Lee
- Division of Biostatistics, Department of R&D Management, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea (M.Y.L.)
| | - Hyun-Ah Lee
- Department of Radiology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, 29 Saemunan-ro, Jongno-gu, Seoul, 03181, Republic of Korea (M.K., I.Y., H.-A.L.)
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Irmici G, Cè M, Pepa GD, D'Ascoli E, De Berardinis C, Giambersio E, Rabiolo L, La Rocca L, Carriero S, Depretto C, Scaperrotta G, Cellina M. Exploring the Potential of Artificial Intelligence in Breast Ultrasound. Crit Rev Oncog 2024; 29:15-28. [PMID: 38505878 DOI: 10.1615/critrevoncog.2023048873] [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: 03/21/2024]
Abstract
Breast ultrasound has emerged as a valuable imaging modality in the detection and characterization of breast lesions, particularly in women with dense breast tissue or contraindications for mammography. Within this framework, artificial intelligence (AI) has garnered significant attention for its potential to improve diagnostic accuracy in breast ultrasound and revolutionize the workflow. This review article aims to comprehensively explore the current state of research and development in harnessing AI's capabilities for breast ultrasound. We delve into various AI techniques, including machine learning, deep learning, as well as their applications in automating lesion detection, segmentation, and classification tasks. Furthermore, the review addresses the challenges and hurdles faced in implementing AI systems in breast ultrasound diagnostics, such as data privacy, interpretability, and regulatory approval. Ethical considerations pertaining to the integration of AI into clinical practice are also discussed, emphasizing the importance of maintaining a patient-centered approach. The integration of AI into breast ultrasound holds great promise for improving diagnostic accuracy, enhancing efficiency, and ultimately advancing patient's care. By examining the current state of research and identifying future opportunities, this review aims to contribute to the understanding and utilization of AI in breast ultrasound and encourage further interdisciplinary collaboration to maximize its potential in clinical practice.
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Affiliation(s)
- Giovanni Irmici
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Maurizio Cè
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Gianmarco Della Pepa
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Elisa D'Ascoli
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Claudia De Berardinis
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Emilia Giambersio
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Lidia Rabiolo
- Dipartimento di Biomedicina, Neuroscienze e Diagnostica Avanzata, Policlinico Università di Palermo, Palermo, Italy
| | - Ludovica La Rocca
- Postgraduation School in Radiodiagnostics, Università degli Studi di Napoli
| | - Serena Carriero
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Catherine Depretto
- Breast Radiology Unit, Fondazione IRCCS, Istituto Nazionale Tumori, Milano, Italy
| | | | - Michaela Cellina
- Radiology Department, Fatebenefratelli Hospital, ASST Fatebenefratelli Sacco, Milano, Piazza Principessa Clotilde 3, 20121, Milan, Italy
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9
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Huppe AI, Inciardi MF, Aripoli AM, Peterson JK, Smith CB, Winblad OD. Pearls and Pitfalls of Interpretation of Automated Breast US. Radiographics 2023; 43:e230023. [PMID: 37792592 DOI: 10.1148/rg.230023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/06/2023]
Abstract
Dense breast tissue is an independent risk factor for breast cancer and reduces the sensitivity of mammography. Patients with dense breast tissue are more likely to present with interval cancers and higher-stage disease. Successful breast cancer screening outcomes rely on detection of early-stage breast cancers; therefore, several supplemental screening modalities have been developed to improve cancer detection in dense breast tissue. US is the most widely used supplemental screening modality worldwide and has been proven to demonstrate additional mammographically occult cancers that are predominantly invasive and node negative. According to the American College of Radiology, intermediate-risk women with dense breast tissue may benefit from adjunctive screening US due to the limitations of mammography. Several studies have demonstrated handheld US (HHUS) and automated breast US (AUS) to be comparable in the screening setting. The advantages of AUS over HHUS include lack of operator dependence and a formal training requirement, image reproducibility, and ability for temporal comparison. However, AUS exhibits unique features that can result in high false-positive rates and long interpretation times for new users. Familiarity with the common appearance of benign mammographic findings and artifacts, technical challenges, and unique AUS features is essential for fast, efficient, and accurate interpretation. The goals of this article are to (a) examine the role of AUS as a supplemental screening modality and (b) review the pearls and pitfalls of AUS interpretation. ©RSNA, 2023 Quiz questions for this article are available in the supplemental material.
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Affiliation(s)
- Ashley I Huppe
- From the Department of Radiology, The University of Kansas Health System, 4000 Cambridge St, Kansas City, KS 66160
| | - Marc F Inciardi
- From the Department of Radiology, The University of Kansas Health System, 4000 Cambridge St, Kansas City, KS 66160
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You C, Shen Y, Sun S, Zhou J, Li J, Su G, Michalopoulou E, Peng W, Gu Y, Guo W, Cao H. Artificial intelligence in breast imaging: Current situation and clinical challenges. EXPLORATION (BEIJING, CHINA) 2023; 3:20230007. [PMID: 37933287 PMCID: PMC10582610 DOI: 10.1002/exp.20230007] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 04/30/2023] [Indexed: 11/08/2023]
Abstract
Breast cancer ranks among the most prevalent malignant tumours and is the primary contributor to cancer-related deaths in women. Breast imaging is essential for screening, diagnosis, and therapeutic surveillance. With the increasing demand for precision medicine, the heterogeneous nature of breast cancer makes it necessary to deeply mine and rationally utilize the tremendous amount of breast imaging information. With the rapid advancement of computer science, artificial intelligence (AI) has been noted to have great advantages in processing and mining of image information. Therefore, a growing number of scholars have started to focus on and research the utility of AI in breast imaging. Here, an overview of breast imaging databases and recent advances in AI research are provided, the challenges and problems in this field are discussed, and then constructive advice is further provided for ongoing scientific developments from the perspective of the National Natural Science Foundation of China.
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Affiliation(s)
- Chao You
- Department of RadiologyFudan University Shanghai Cancer CenterShanghaiChina
- Department of OncologyShanghai Medical CollegeFudan UniversityShanghaiChina
| | - Yiyuan Shen
- Department of RadiologyFudan University Shanghai Cancer CenterShanghaiChina
- Department of OncologyShanghai Medical CollegeFudan UniversityShanghaiChina
| | - Shiyun Sun
- Department of RadiologyFudan University Shanghai Cancer CenterShanghaiChina
- Department of OncologyShanghai Medical CollegeFudan UniversityShanghaiChina
| | - Jiayin Zhou
- Department of RadiologyFudan University Shanghai Cancer CenterShanghaiChina
- Department of OncologyShanghai Medical CollegeFudan UniversityShanghaiChina
| | - Jiawei Li
- Department of RadiologyFudan University Shanghai Cancer CenterShanghaiChina
- Department of OncologyShanghai Medical CollegeFudan UniversityShanghaiChina
| | - Guanhua Su
- Department of OncologyShanghai Medical CollegeFudan UniversityShanghaiChina
- Department of Breast SurgeryKey Laboratory of Breast Cancer in ShanghaiFudan University Shanghai Cancer CenterShanghaiChina
| | | | - Weijun Peng
- Department of RadiologyFudan University Shanghai Cancer CenterShanghaiChina
- Department of OncologyShanghai Medical CollegeFudan UniversityShanghaiChina
| | - Yajia Gu
- Department of RadiologyFudan University Shanghai Cancer CenterShanghaiChina
- Department of OncologyShanghai Medical CollegeFudan UniversityShanghaiChina
| | - Weisheng Guo
- Department of Minimally Invasive Interventional RadiologyKey Laboratory of Molecular Target and Clinical PharmacologySchool of Pharmaceutical Sciences and The Second Affiliated HospitalGuangzhou Medical UniversityGuangzhouChina
| | - Heqi Cao
- Department of Health SciencesNational Natural Science Foundation of ChinaBeijingChina
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Lawson MB, Partridge SC, Hippe DS, Rahbar H, Lam DL, Lee CI, Lowry KP, Scheel JR, Parsian S, Li I, Biswas D, Bryant ML, Lee JM. Comparative Performance of Contrast-enhanced Mammography, Abbreviated Breast MRI, and Standard Breast MRI for Breast Cancer Screening. Radiology 2023; 308:e230576. [PMID: 37581498 PMCID: PMC10481328 DOI: 10.1148/radiol.230576] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 06/12/2023] [Accepted: 06/15/2023] [Indexed: 08/16/2023]
Abstract
Background Contrast-enhanced mammography (CEM) and abbreviated breast MRI (ABMRI) are emerging alternatives to standard MRI for supplemental breast cancer screening. Purpose To compare the diagnostic performance of CEM, ABMRI, and standard MRI. Materials and Methods This single-institution, prospective, blinded reader study included female participants referred for breast MRI from January 2018 to June 2021. CEM was performed within 14 days of standard MRI; ABMRI was produced from standard MRI images. Two readers independently interpreted each CEM and ABMRI after a washout period. Examination-level performance metrics calculated were recall rate, cancer detection, and false-positive biopsy recommendation rates per 1000 examinations and sensitivity, specificity, and positive predictive value of biopsy recommendation. Bootstrap and permutation tests were used to calculate 95% CIs and compare modalities. Results Evaluated were 492 paired CEM and ABMRI interpretations from 246 participants (median age, 51 years; IQR, 43-61 years). On 49 MRI scans with lesions recommended for biopsy, nine lesions showed malignant pathology. No differences in ABMRI and standard MRI performance were identified. Compared with standard MRI, CEM demonstrated significantly lower recall rate (14.0% vs 22.8%; difference, -8.7%; 95% CI: -14.0, -3.5), lower false-positive biopsy recommendation rate per 1000 examinations (65.0 vs 162.6; difference, -97.6; 95% CI: -146.3, -50.8), and higher specificity (87.8% vs 80.2%; difference, 7.6%; 95% CI: 2.3, 13.1). Compared with standard MRI, CEM had significantly lower cancer detection rate (22.4 vs 36.6; difference, -14.2; 95% CI: -28.5, -2.0) and sensitivity (61.1% vs 100%; difference, -38.9%; 95% CI: -66.7, -12.5). The performance differences between CEM and ABMRI were similar to those observed between CEM and standard MRI. Conclusion ABMRI had comparable performance to standard MRI and may support more efficient MRI screening. CEM had lower recall and higher specificity compared with standard MRI or ABMRI, offset by lower cancer detection rate and sensitivity compared with standard MRI. These trade-offs warrant further consideration of patient population characteristics before widespread screening with CEM. Clinical trial registration no. NCT03517813 © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Chang in this issue.
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Affiliation(s)
- Marissa B. Lawson
- From the Department of Radiology, University of Washington, Seattle,
Wash (M.B.L., S.C.P., H.R., D.L.L., C.I.L., K.P.L., I.L., D.B., M.L.B., J.M.L.);
Department of Radiology (M.B.L., S.C.P., H.R., D.L.L., C.I.L., K.P.L., I.L.,
D.B., M.L.B., J.M.L.) and Clinical Research Division (D.S.H.), Fred Hutchinson
Cancer Center, 825 Eastlake Eve E, LG-200, Seattle, WA 98109; Department of
Radiology, Vanderbilt University, Nashville, Tenn (J.R.S.); and Department of
Radiology, Kaiser Permanente, Seattle, Wash (S.P.)
| | - Savannah C. Partridge
- From the Department of Radiology, University of Washington, Seattle,
Wash (M.B.L., S.C.P., H.R., D.L.L., C.I.L., K.P.L., I.L., D.B., M.L.B., J.M.L.);
Department of Radiology (M.B.L., S.C.P., H.R., D.L.L., C.I.L., K.P.L., I.L.,
D.B., M.L.B., J.M.L.) and Clinical Research Division (D.S.H.), Fred Hutchinson
Cancer Center, 825 Eastlake Eve E, LG-200, Seattle, WA 98109; Department of
Radiology, Vanderbilt University, Nashville, Tenn (J.R.S.); and Department of
Radiology, Kaiser Permanente, Seattle, Wash (S.P.)
| | - Daniel S. Hippe
- From the Department of Radiology, University of Washington, Seattle,
Wash (M.B.L., S.C.P., H.R., D.L.L., C.I.L., K.P.L., I.L., D.B., M.L.B., J.M.L.);
Department of Radiology (M.B.L., S.C.P., H.R., D.L.L., C.I.L., K.P.L., I.L.,
D.B., M.L.B., J.M.L.) and Clinical Research Division (D.S.H.), Fred Hutchinson
Cancer Center, 825 Eastlake Eve E, LG-200, Seattle, WA 98109; Department of
Radiology, Vanderbilt University, Nashville, Tenn (J.R.S.); and Department of
Radiology, Kaiser Permanente, Seattle, Wash (S.P.)
| | - Habib Rahbar
- From the Department of Radiology, University of Washington, Seattle,
Wash (M.B.L., S.C.P., H.R., D.L.L., C.I.L., K.P.L., I.L., D.B., M.L.B., J.M.L.);
Department of Radiology (M.B.L., S.C.P., H.R., D.L.L., C.I.L., K.P.L., I.L.,
D.B., M.L.B., J.M.L.) and Clinical Research Division (D.S.H.), Fred Hutchinson
Cancer Center, 825 Eastlake Eve E, LG-200, Seattle, WA 98109; Department of
Radiology, Vanderbilt University, Nashville, Tenn (J.R.S.); and Department of
Radiology, Kaiser Permanente, Seattle, Wash (S.P.)
| | - Diana L. Lam
- From the Department of Radiology, University of Washington, Seattle,
Wash (M.B.L., S.C.P., H.R., D.L.L., C.I.L., K.P.L., I.L., D.B., M.L.B., J.M.L.);
Department of Radiology (M.B.L., S.C.P., H.R., D.L.L., C.I.L., K.P.L., I.L.,
D.B., M.L.B., J.M.L.) and Clinical Research Division (D.S.H.), Fred Hutchinson
Cancer Center, 825 Eastlake Eve E, LG-200, Seattle, WA 98109; Department of
Radiology, Vanderbilt University, Nashville, Tenn (J.R.S.); and Department of
Radiology, Kaiser Permanente, Seattle, Wash (S.P.)
| | - Christoph I. Lee
- From the Department of Radiology, University of Washington, Seattle,
Wash (M.B.L., S.C.P., H.R., D.L.L., C.I.L., K.P.L., I.L., D.B., M.L.B., J.M.L.);
Department of Radiology (M.B.L., S.C.P., H.R., D.L.L., C.I.L., K.P.L., I.L.,
D.B., M.L.B., J.M.L.) and Clinical Research Division (D.S.H.), Fred Hutchinson
Cancer Center, 825 Eastlake Eve E, LG-200, Seattle, WA 98109; Department of
Radiology, Vanderbilt University, Nashville, Tenn (J.R.S.); and Department of
Radiology, Kaiser Permanente, Seattle, Wash (S.P.)
| | - Kathryn P. Lowry
- From the Department of Radiology, University of Washington, Seattle,
Wash (M.B.L., S.C.P., H.R., D.L.L., C.I.L., K.P.L., I.L., D.B., M.L.B., J.M.L.);
Department of Radiology (M.B.L., S.C.P., H.R., D.L.L., C.I.L., K.P.L., I.L.,
D.B., M.L.B., J.M.L.) and Clinical Research Division (D.S.H.), Fred Hutchinson
Cancer Center, 825 Eastlake Eve E, LG-200, Seattle, WA 98109; Department of
Radiology, Vanderbilt University, Nashville, Tenn (J.R.S.); and Department of
Radiology, Kaiser Permanente, Seattle, Wash (S.P.)
| | - John R. Scheel
- From the Department of Radiology, University of Washington, Seattle,
Wash (M.B.L., S.C.P., H.R., D.L.L., C.I.L., K.P.L., I.L., D.B., M.L.B., J.M.L.);
Department of Radiology (M.B.L., S.C.P., H.R., D.L.L., C.I.L., K.P.L., I.L.,
D.B., M.L.B., J.M.L.) and Clinical Research Division (D.S.H.), Fred Hutchinson
Cancer Center, 825 Eastlake Eve E, LG-200, Seattle, WA 98109; Department of
Radiology, Vanderbilt University, Nashville, Tenn (J.R.S.); and Department of
Radiology, Kaiser Permanente, Seattle, Wash (S.P.)
| | - Sana Parsian
- From the Department of Radiology, University of Washington, Seattle,
Wash (M.B.L., S.C.P., H.R., D.L.L., C.I.L., K.P.L., I.L., D.B., M.L.B., J.M.L.);
Department of Radiology (M.B.L., S.C.P., H.R., D.L.L., C.I.L., K.P.L., I.L.,
D.B., M.L.B., J.M.L.) and Clinical Research Division (D.S.H.), Fred Hutchinson
Cancer Center, 825 Eastlake Eve E, LG-200, Seattle, WA 98109; Department of
Radiology, Vanderbilt University, Nashville, Tenn (J.R.S.); and Department of
Radiology, Kaiser Permanente, Seattle, Wash (S.P.)
| | - Isabella Li
- From the Department of Radiology, University of Washington, Seattle,
Wash (M.B.L., S.C.P., H.R., D.L.L., C.I.L., K.P.L., I.L., D.B., M.L.B., J.M.L.);
Department of Radiology (M.B.L., S.C.P., H.R., D.L.L., C.I.L., K.P.L., I.L.,
D.B., M.L.B., J.M.L.) and Clinical Research Division (D.S.H.), Fred Hutchinson
Cancer Center, 825 Eastlake Eve E, LG-200, Seattle, WA 98109; Department of
Radiology, Vanderbilt University, Nashville, Tenn (J.R.S.); and Department of
Radiology, Kaiser Permanente, Seattle, Wash (S.P.)
| | - Debosmita Biswas
- From the Department of Radiology, University of Washington, Seattle,
Wash (M.B.L., S.C.P., H.R., D.L.L., C.I.L., K.P.L., I.L., D.B., M.L.B., J.M.L.);
Department of Radiology (M.B.L., S.C.P., H.R., D.L.L., C.I.L., K.P.L., I.L.,
D.B., M.L.B., J.M.L.) and Clinical Research Division (D.S.H.), Fred Hutchinson
Cancer Center, 825 Eastlake Eve E, LG-200, Seattle, WA 98109; Department of
Radiology, Vanderbilt University, Nashville, Tenn (J.R.S.); and Department of
Radiology, Kaiser Permanente, Seattle, Wash (S.P.)
| | - Mary Lynn Bryant
- From the Department of Radiology, University of Washington, Seattle,
Wash (M.B.L., S.C.P., H.R., D.L.L., C.I.L., K.P.L., I.L., D.B., M.L.B., J.M.L.);
Department of Radiology (M.B.L., S.C.P., H.R., D.L.L., C.I.L., K.P.L., I.L.,
D.B., M.L.B., J.M.L.) and Clinical Research Division (D.S.H.), Fred Hutchinson
Cancer Center, 825 Eastlake Eve E, LG-200, Seattle, WA 98109; Department of
Radiology, Vanderbilt University, Nashville, Tenn (J.R.S.); and Department of
Radiology, Kaiser Permanente, Seattle, Wash (S.P.)
| | - Janie M. Lee
- From the Department of Radiology, University of Washington, Seattle,
Wash (M.B.L., S.C.P., H.R., D.L.L., C.I.L., K.P.L., I.L., D.B., M.L.B., J.M.L.);
Department of Radiology (M.B.L., S.C.P., H.R., D.L.L., C.I.L., K.P.L., I.L.,
D.B., M.L.B., J.M.L.) and Clinical Research Division (D.S.H.), Fred Hutchinson
Cancer Center, 825 Eastlake Eve E, LG-200, Seattle, WA 98109; Department of
Radiology, Vanderbilt University, Nashville, Tenn (J.R.S.); and Department of
Radiology, Kaiser Permanente, Seattle, Wash (S.P.)
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Alves KL, Freitas-Junior R, Paulinelli RR, Borges MN. The Automation of Breast Ultrasonography and the Medical Time Dedicated to the Method. REVISTA BRASILEIRA DE GINECOLOGIA E OBSTETRÍCIA 2023; 45:e409-e414. [PMID: 37595598 PMCID: PMC10438963 DOI: 10.1055/s-0043-1772176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 02/12/2023] [Indexed: 08/20/2023] Open
Abstract
In this integrative review, we aimed to describe the records of time devoted by physicians to breast ultrasound in a review of articles in the literature, in order to observe whether the automation of the method enabled a reduction in these values. We selected articles from the Latin American and Caribbean Literature in Health Sciences (LILACS) and MEDLINE databases, through Virtual Health Library (BVS), SciELO (Scientific Electronic Library Online), PubMed, and Scopus. We obtained 561 articles, and, after excluding duplicates and screening procedures, 9 were selected, whose main information related to the guiding question of the research was synthesized and analyzed. It was concluded that the automation of breast ultrasound represents a possible strategy for optimization of the medical time dedicated to the method, but this needs to be better evaluated in comparative studies between both methods (traditional and automated), with methodology directed to the specific investigation of this potentiality.
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Tosaki T, Yamakawa M, Shiina T. A study on the optimal condition of ground truth area for liver tumor detection in ultrasound images using deep learning. J Med Ultrason (2001) 2023; 50:167-176. [PMID: 37014524 PMCID: PMC10182112 DOI: 10.1007/s10396-023-01301-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 02/16/2023] [Indexed: 04/05/2023]
Abstract
PURPOSE In recent years, efforts to apply artificial intelligence (AI) to the medical field have been growing. In general, a vast amount of high-quality training data is necessary to make great AI. For tumor detection AI, annotation quality is important. In diagnosis and detection of tumors using ultrasound images, humans use not only the tumor area but also the surrounding information, such as the back echo of the tumor. Therefore, we investigated changes in detection accuracy when changing the size of the region of interest (ROI, ground truth area) relative to liver tumors in the training data for the detection AI. METHODS We defined D/L as the ratio of the maximum diameter (D) of the liver tumor to the ROI size (L). We created training data by changing the D/L value, and performed learning and testing with YOLOv3. RESULTS Our results showed that the detection accuracy was highest when the training data were created with a D/L ratio between 0.8 and 1.0. In other words, it was found that the detection accuracy was improved by setting the ground true bounding box for detection AI training to be in contact with the tumor or slightly larger. We also found that when the D/L ratio was distributed in the training data, the wider the distribution, the lower the detection accuracy. CONCLUSIONS Therefore, we recommend that the detector be trained with the D/L value close to a certain value between 0.8 and 1.0 for liver tumor detection from ultrasound images.
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Affiliation(s)
- Taisei Tosaki
- Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Makoto Yamakawa
- Graduate School of Medicine, Kyoto University, Kyoto, Japan.
- SIT Research Laboratories, Shibaura Institute of Technology, 3-7-5 Toyosu, Koto-ku, Tokyo, 135-8548, Japan.
| | - Tsuyoshi Shiina
- Graduate School of Medicine, Kyoto University, Kyoto, Japan
- SIT Research Laboratories, Shibaura Institute of Technology, 3-7-5 Toyosu, Koto-ku, Tokyo, 135-8548, Japan
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14
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Villa-Camacho JC, Baikpour M, Chou SHS. Artificial Intelligence for Breast US. JOURNAL OF BREAST IMAGING 2023; 5:11-20. [PMID: 38416959 DOI: 10.1093/jbi/wbac077] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Indexed: 03/01/2024]
Abstract
US is a widely available, commonly used, and indispensable imaging modality for breast evaluation. It is often the primary imaging modality for the detection and diagnosis of breast cancer in low-resource settings. In addition, it is frequently employed as a supplemental screening tool via either whole breast handheld US or automated breast US among women with dense breasts. In recent years, a variety of artificial intelligence systems have been developed to assist radiologists with the detection and diagnosis of breast lesions on US. This article reviews the background and evidence supporting the use of artificial intelligence tools for breast US, describes implementation strategies and impact on clinical workflow, and discusses potential emerging roles and future directions.
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Affiliation(s)
| | - Masoud Baikpour
- Massachusetts General Hospital, Department of Radiology, Boston, MA, USA
| | - Shinn-Huey S Chou
- Massachusetts General Hospital, Department of Radiology, Boston, MA, USA
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15
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Tan T, Rodriguez-Ruiz A, Zhang T, Xu L, Beets-Tan RGH, Shen Y, Karssemeijer N, Xu J, Mann RM, Bao L. Multi-modal artificial intelligence for the combination of automated 3D breast ultrasound and mammograms in a population of women with predominantly dense breasts. Insights Imaging 2023; 14:10. [PMID: 36645507 PMCID: PMC9842825 DOI: 10.1186/s13244-022-01352-y] [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: 07/08/2022] [Accepted: 12/09/2022] [Indexed: 01/17/2023] Open
Abstract
OBJECTIVES To assess the stand-alone and combined performance of artificial intelligence (AI) detection systems for digital mammography (DM) and automated 3D breast ultrasound (ABUS) in detecting breast cancer in women with dense breasts. METHODS 430 paired cases of DM and ABUS examinations from a Asian population with dense breasts were retrospectively collected. All cases were analyzed by two AI systems, one for DM exams and one for ABUS exams. A selected subset (n = 152) was read by four radiologists. The performance of AI systems was based on analysis of the area under the receiver operating characteristic curve (AUC). The maximum Youden's index and its associated sensitivity and specificity were also reported for each AI systems. Detection performance of human readers in the subcohort of the reader study was measured in terms of sensitivity and specificity. RESULTS The performance of the AI systems in a multi-modal setting was significantly better when the weights of AI-DM and AI-ABUS were 0.25 and 0.75, respectively, than each system individually in a single-modal setting (AUC-AI-Multimodal = 0.865; AUC-AI-DM = 0.832, p = 0.026; AUC-AI-ABUS = 0.841, p = 0.041). The maximum Youden's index for AI-Multimodal was 0.707 (sensitivity = 79.4%, specificity = 91.2%). In the subcohort that underwent human reading, the panel of four readers achieved a sensitivity of 93.2% and specificity of 32.7%. AI-multimodal achieves superior or equal sensitivity as single human readers at the same specificity operating points on the ROC curve. CONCLUSION Multimodal (ABUS + DM) AI systems for detecting breast cancer in women with dense breasts are a potential solution for breast screening in radiologist-scarce regions.
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Affiliation(s)
- Tao Tan
- grid.430814.a0000 0001 0674 1393Department of Radiology, Netherlands Cancer Institute (NKI), Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands ,Faculty of Applied Science, Macao Polytechnic University, Macao, 999078 China
| | | | - Tianyu Zhang
- grid.430814.a0000 0001 0674 1393Department of Radiology, Netherlands Cancer Institute (NKI), Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands ,grid.5012.60000 0001 0481 6099GROW School for Oncology and Development Biology, Maastricht University, P. O. Box 616, 6200 MD Maastricht, The Netherlands
| | - Lin Xu
- grid.440637.20000 0004 4657 8879School of Information Science and Technology, ShanghaiTech University, Shanghai, 201210 China
| | - Regina G. H. Beets-Tan
- grid.430814.a0000 0001 0674 1393Department of Radiology, Netherlands Cancer Institute (NKI), Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands ,grid.5012.60000 0001 0481 6099GROW School for Oncology and Development Biology, Maastricht University, P. O. Box 616, 6200 MD Maastricht, The Netherlands
| | - Yingzhao Shen
- grid.13402.340000 0004 1759 700XAffiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Xueshi Road, Hubin Street, Shangcheng District, Hangzhou, 310006 Zhejiang China
| | - Nico Karssemeijer
- grid.10417.330000 0004 0444 9382Department of Diagnostic Imaging, Radboud University Medical Center, PO Box 9101, 6500 HB Nijmegen, The Netherlands
| | - Jun Xu
- grid.260478.f0000 0000 9249 2313Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, 210044 China
| | - Ritse M. Mann
- grid.430814.a0000 0001 0674 1393Department of Radiology, Netherlands Cancer Institute (NKI), Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands ,grid.10417.330000 0004 0444 9382Department of Diagnostic Imaging, Radboud University Medical Center, PO Box 9101, 6500 HB Nijmegen, The Netherlands
| | - Lingyun Bao
- grid.13402.340000 0004 1759 700XAffiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Xueshi Road, Hubin Street, Shangcheng District, Hangzhou, 310006 Zhejiang China
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Yin H, Yang X, Sun L, Pan P, Peng L, Li K, Zhang D, Cui F, Xia C, Huang H, Li Z. The value of artificial intelligence techniques in predicting pancreatic ductal adenocarcinoma with EUS images: A meta-analysis and systematic review. Endosc Ultrasound 2023; 12:50-58. [PMID: 35313419 PMCID: PMC10134944 DOI: 10.4103/eus-d-21-00131] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
Conventional EUS plays an important role in identifying pancreatic cancer. However, the accuracy of EUS is strongly influenced by the operator's experience in performing EUS. Artificial intelligence (AI) is increasingly being used in various clinical diagnoses, especially in terms of image classification. This study aimed to evaluate the diagnostic test accuracy of AI for the prediction of pancreatic cancer using EUS images. We searched the Embase, PubMed, and Cochrane Library databases to identify studies that used endoscopic ultrasound images of pancreatic cancer and AI to predict the diagnostic accuracy of pancreatic cancer. Two reviewers extracted the data independently. The risk of bias of eligible studies was assessed using a Deek funnel plot. The quality of the included studies was measured by the QUDAS-2 tool. Seven studies involving 1110 participants were included: 634 participants with pancreatic cancer and 476 participants with nonpancreatic cancer. The accuracy of the AI for the prediction of pancreatic cancer (area under the curve) was 0.95 (95% confidence interval [CI], 0.93-0.97), with a corresponding pooled sensitivity of 93% (95% CI, 0.90-0.95), specificity of 90% (95% CI, 0.8-0.95), positive likelihood ratio 9.1 (95% CI 4.4-18.6), negative likelihood ratio 0.08 (95% CI 0.06-0.11), and diagnostic odds ratio 114 (95% CI 56-236). The methodological quality in each study was found to be the source of heterogeneity in the meta-regression combined model, which was statistically significant (P = 0.01). There was no evidence of publication bias. The accuracy of AI in diagnosing pancreatic cancer appears to be reliable. Further research and investment in AI could lead to substantial improvements in screening and early diagnosis.
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Affiliation(s)
- Hua Yin
- Department of Gastroenterology, General Hospital of Ningxia Medical University, Yinchuan; Department of Gastroenterology, Changhai Hospital, Second Military Medical University, Shanghai; Postgraduate Training Base in Shanghai Gongli Hospital, Ningxia Medical University, Shanghai, China
| | - Xiaoli Yang
- Department of Gastroenterology, General Hospital of Ningxia Medical University, Yinchuan; Department of Gastroenterology, Changhai Hospital, Second Military Medical University, Shanghai; Postgraduate Training Base in Shanghai Gongli Hospital, Ningxia Medical University, Shanghai, China
| | - Liqi Sun
- Department of Gastroenterology, Changhai Hospital, Second Military Medical University, Shanghai, China
| | - Peng Pan
- Department of Gastroenterology, Changhai Hospital, Second Military Medical University, Shanghai, China
| | - Lisi Peng
- Department of Gastroenterology, Changhai Hospital, Second Military Medical University, Shanghai, China
| | - Keliang Li
- Department of Gastroenterology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China
| | - Deyu Zhang
- Department of Gastroenterology, Changhai Hospital, Second Military Medical University, Shanghai, China
| | - Fang Cui
- Department of Gastroenterology, Changhai Hospital, Second Military Medical University, Shanghai, China
| | - Chuanchao Xia
- Department of Gastroenterology, Changhai Hospital, Second Military Medical University, Shanghai, China
| | - Haojie Huang
- Department of Gastroenterology, Changhai Hospital, Second Military Medical University, Shanghai, China
| | - Zhaoshen Li
- Department of Gastroenterology, Changhai Hospital, Second Military Medical University, Shanghai, China
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Baughan N, Douglas L, Giger ML. Past, Present, and Future of Machine Learning and Artificial Intelligence for Breast Cancer Screening. JOURNAL OF BREAST IMAGING 2022; 4:451-459. [PMID: 38416954 DOI: 10.1093/jbi/wbac052] [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: 04/29/2022] [Indexed: 03/01/2024]
Abstract
Breast cancer screening has evolved substantially over the past few decades because of advancements in new image acquisition systems and novel artificial intelligence (AI) algorithms. This review provides a brief overview of the history, current state, and future of AI in breast cancer screening and diagnosis along with challenges involved in the development of AI systems. Although AI has been developing for interpretation tasks associated with breast cancer screening for decades, its potential to combat the subjective nature and improve the efficiency of human image interpretation is always expanding. The rapid advancement of computational power and deep learning has increased greatly in AI research, with promising performance in detection and classification tasks across imaging modalities. Most AI systems, based on human-engineered or deep learning methods, serve as concurrent or secondary readers, that is, as aids to radiologists for a specific, well-defined task. In the future, AI may be able to perform multiple integrated tasks, making decisions at the level of or surpassing the ability of humans. Artificial intelligence may also serve as a partial primary reader to streamline ancillary tasks, triaging cases or ruling out obvious normal cases. However, before AI is used as an independent, autonomous reader, various challenges need to be addressed, including explainability and interpretability, in addition to repeatability and generalizability, to ensure that AI will provide a significant clinical benefit to breast cancer screening across all populations.
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Affiliation(s)
- Natalie Baughan
- University of Chicago, Department of Radiology Committee on Medical Physics, Chicago, IL, USA
| | - Lindsay Douglas
- University of Chicago, Department of Radiology Committee on Medical Physics, Chicago, IL, USA
| | - Maryellen L Giger
- University of Chicago, Department of Radiology Committee on Medical Physics, Chicago, IL, USA
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Lai YC, Chen HH, Hsu JF, Hong YJ, Chiu TT, Chiou HJ. Evaluation of physician performance using a concurrent-read artificial intelligence system to support breast ultrasound interpretation. Breast 2022; 65:124-135. [PMID: 35944352 PMCID: PMC9379669 DOI: 10.1016/j.breast.2022.07.009] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 07/11/2022] [Accepted: 07/13/2022] [Indexed: 11/30/2022] Open
Abstract
Purpose The purpose of this study was to compare the diagnostic performance and the interpretation time of breast ultrasound examination between reading without and with the artificial intelligence (AI) system as a concurrent reading aid. Material and methods A fully crossed multi-reader and multi-case (MRMC) reader study was conducted. Sixteen participating physicians were recruited and retrospectively interpreted 172 breast ultrasound cases in two reading scenarios, once without and once with the AI system (BU-CAD™, TaiHao Medical Inc.) assistance for concurrent reading. Interpretations of any given case set with and without the AI system were separated by at least 5 weeks. These reading results were compared to the reference standard and the area under the LROC curve (AUCLROC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for performance evaluations. The interpretation time was also compared between the unaided and aided scenarios. Results With the help of the AI system, the readers had higher diagnostic performance with an increase in the average AUCLROC from 0.7582 to 0.8294 with statistically significant. The sensitivity, specificity, PPV, and NPV were also improved from 95.77%, 24.07%, 44.18%, and 93.50%–98.17%, 30.67%, 46.91%, and 96.10%, respectively. Of these, the improvement in specificity reached statistical significance. The average interpretation time was significantly reduced by approximately 40% when the readers were assisted by the AI system. Conclusion The concurrent-read AI system improves the diagnostic performance in detecting and diagnosing breast lesions on breast ultrasound images. In addition, the interpretation time is effectively reduced for the interpreting physicians. A reader study was conducted to compare the breast ultrasound interpreting performance without and with the aid of AI system. The performance of breast ultrasound interpretation was improved by the AI system as a concurrent reading aid. The breast ultrasound interpretation time is significantly reduced by the AI system as a concurrent reading aid. The reproducibility experiments of the same lesion cropped by different rectangular proved the robustness of the AI system.
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Affiliation(s)
- Yi-Chen Lai
- Comprehensive Breast Health Center, Taipei Veterans General Hospital, No.201, Sec. 2, Shipai Rd., Beitou District, Taipei, 11217, Taiwan, ROC; School of Medicine, National Yang Ming Chiao Tung University, No.155, Sec. 2, Linong St., Beitou District, Taipei, 112304, Taiwan, ROC.
| | - Hong-Hao Chen
- TaiHao Medical Inc., 6F.-1, No.100, Sec. 2, Heping E. Rd., Da'an District, Taipei, 10663, Taiwan, ROC.
| | - Jen-Feng Hsu
- TaiHao Medical Inc., 6F.-1, No.100, Sec. 2, Heping E. Rd., Da'an District, Taipei, 10663, Taiwan, ROC; Department of Computer Science and Information Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Da'an District, Taipei, 10617, Taiwan, ROC.
| | - Yi-Jun Hong
- TaiHao Medical Inc., 6F.-1, No.100, Sec. 2, Heping E. Rd., Da'an District, Taipei, 10663, Taiwan, ROC.
| | - Ting-Ting Chiu
- TaiHao Medical Inc., 6F.-1, No.100, Sec. 2, Heping E. Rd., Da'an District, Taipei, 10663, Taiwan, ROC.
| | - Hong-Jen Chiou
- School of Medicine, National Yang Ming Chiao Tung University, No.155, Sec. 2, Linong St., Beitou District, Taipei, 112304, Taiwan, ROC; Department of Radiology, Taipei Veterans General Hospital, No.201, Sec. 2, Shipai Rd., Beitou District, Taipei, 11217, Taiwan, ROC.
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Understanding Breast Cancers through Spatial and High-Resolution Visualization Using Imaging Technologies. Cancers (Basel) 2022; 14:cancers14174080. [PMID: 36077616 PMCID: PMC9454728 DOI: 10.3390/cancers14174080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 08/12/2022] [Accepted: 08/18/2022] [Indexed: 11/17/2022] Open
Abstract
Breast cancer is the most common cancer affecting women worldwide. Although many analyses and treatments have traditionally targeted the breast cancer cells themselves, recent studies have focused on investigating entire cancer tissues, including breast cancer cells. To understand the structure of breast cancer tissues, including breast cancer cells, it is necessary to investigate the three-dimensional location of the cells and/or proteins comprising the tissues and to clarify the relationship between the three-dimensional structure and malignant transformation or metastasis of breast cancers. In this review, we aim to summarize the methods for analyzing the three-dimensional structure of breast cancer tissue, paying particular attention to the recent technological advances in the combination of the tissue-clearing method and optical three-dimensional imaging. We also aimed to identify the latest methods for exploring the relationship between the three-dimensional cell arrangement in breast cancer tissues and the gene expression of each cell. Finally, we aimed to describe the three-dimensional imaging features of breast cancer tissues using noninvasive photoacoustic imaging methods.
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Mask Branch Network: Weakly Supervised Branch Network with a Template Mask for Classifying Masses in 3D Automated Breast Ultrasound. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12136332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
Abstract
Automated breast ultrasound (ABUS) is being rapidly utilized for screening and diagnosing breast cancer. Breast masses, including cancers shown in ABUS scans, often appear as irregular hypoechoic areas that are hard to distinguish from background shadings. We propose a novel branch network architecture incorporating segmentation information of masses in the training process. The branch network is integrated into neural network, providing the spatial attention effect. The branch network boosts the performance of existing classifiers, helping to learn meaningful features around the target breast mass. For the segmentation information, we leverage the existing radiology reports without additional labeling efforts. The reports, which is generated in medical image reading process, should include the characteristics of breast masses, such as shape and orientation, and a template mask can be created in a rule-based manner. Experimental results show that the proposed branch network with a template mask significantly improves the performance of existing classifiers. We also provide qualitative interpretation of the proposed method by visualizing the attention effect on target objects.
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Wang Q, Chen H, Luo G, Li B, Shang H, Shao H, Sun S, Wang Z, Wang K, Cheng W. Performance of novel deep learning network with the incorporation of the automatic segmentation network for diagnosis of breast cancer in automated breast ultrasound. Eur Radiol 2022; 32:7163-7172. [PMID: 35488916 DOI: 10.1007/s00330-022-08836-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 04/15/2022] [Accepted: 04/21/2022] [Indexed: 11/29/2022]
Abstract
OBJECTIVE To develop novel deep learning network (DLN) with the incorporation of the automatic segmentation network (ASN) for morphological analysis and determined the performance for diagnosis breast cancer in automated breast ultrasound (ABUS). METHODS A total of 769 breast tumors were enrolled in this study and were randomly divided into training set and test set at 600 vs. 169. The novel DLNs (Resent v2, ResNet50 v2, ResNet101 v2) added a new ASN to the traditional ResNet networks and extracted morphological information of breast tumors. The accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), area under the receiver operating characteristic (ROC) curve (AUC), and average precision (AP) were calculated. The diagnostic performances of novel DLNs were compared with those of two radiologists with different experience. RESULTS The ResNet34 v2 model had higher specificity (76.81%) and PPV (82.22%) than the other two, the ResNet50 v2 model had higher accuracy (78.11%) and NPV (72.86%), and the ResNet101 v2 model had higher sensitivity (85.00%). According to the AUCs and APs, the novel ResNet101 v2 model produced the best result (AUC 0.85 and AP 0.90) compared with the remaining five DLNs. Compared with the novice radiologist, the novel DLNs performed better. The F1 score was increased from 0.77 to 0.78, 0.81, and 0.82 by three novel DLNs. However, their diagnostic performance was worse than that of the experienced radiologist. CONCLUSIONS The novel DLNs performed better than traditional DLNs and may be helpful for novice radiologists to improve their diagnostic performance of breast cancer in ABUS. KEY POINTS • A novel automatic segmentation network to extract morphological information was successfully developed and implemented with ResNet deep learning networks. • The novel deep learning networks in our research performed better than the traditional deep learning networks in the diagnosis of breast cancer using ABUS images. • The novel deep learning networks in our research may be useful for novice radiologists to improve diagnostic performance.
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Affiliation(s)
- Qiucheng Wang
- Department of Ultrasound, Harbin Medical University Cancer Hospital, No. 150, Haping Road, Nangang District, Harbin, Heilongjiang Province, China
| | - He Chen
- Department of Ultrasound, Harbin Medical University Cancer Hospital, No. 150, Haping Road, Nangang District, Harbin, Heilongjiang Province, China
| | - Gongning Luo
- School of Computer Science and Technology, Harbin Institute of Technology, No. 92, Xidazhi Street, Nangang District, Harbin, Heilongjiang Province, China
| | - Bo Li
- Department of Ultrasound, Harbin Medical University Cancer Hospital, No. 150, Haping Road, Nangang District, Harbin, Heilongjiang Province, China
| | - Haitao Shang
- Department of Ultrasound, Harbin Medical University Cancer Hospital, No. 150, Haping Road, Nangang District, Harbin, Heilongjiang Province, China
| | - Hua Shao
- Department of Ultrasound, Harbin Medical University Cancer Hospital, No. 150, Haping Road, Nangang District, Harbin, Heilongjiang Province, China
| | - Shanshan Sun
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, No. 150, Haping Road, Nangang District, Harbin, Heilongjiang Province, China
| | - Zhongshuai Wang
- School of Computer Science and Technology, Harbin Institute of Technology, No. 92, Xidazhi Street, Nangang District, Harbin, Heilongjiang Province, China
| | - Kuanquan Wang
- School of Computer Science and Technology, Harbin Institute of Technology, No. 92, Xidazhi Street, Nangang District, Harbin, Heilongjiang Province, China
| | - Wen Cheng
- Department of Ultrasound, Harbin Medical University Cancer Hospital, No. 150, Haping Road, Nangang District, Harbin, Heilongjiang Province, China.
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Lee J, Kang BJ, Kim SH, Park GE. Evaluation of Computer-Aided Detection (CAD) in Screening Automated Breast Ultrasound Based on Characteristics of CAD Marks and False-Positive Marks. Diagnostics (Basel) 2022; 12:diagnostics12030583. [PMID: 35328136 PMCID: PMC8947351 DOI: 10.3390/diagnostics12030583] [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: 01/20/2022] [Revised: 02/12/2022] [Accepted: 02/23/2022] [Indexed: 02/04/2023] Open
Abstract
The present study evaluated the effectiveness of computer-aided detection (CAD) system in screening automated breast ultrasound (ABUS) and analyzed the characteristics of CAD marks and the causes of false-positive marks. A total of 846 women who underwent ABUS for screening from January 2017 to December 2017 were included. Commercial CAD was used in all ABUS examinations, and its diagnostic performance and efficacy in shortening the reading time (RT) were evaluated. In addition, we analyzed the characteristics of CAD marks and the causes of false-positive marks. A total of 1032 CAD marks were displayed based on the patient and 534 CAD marks on the lesion. Five cases of breast cancer were diagnosed. The sensitivity, specificity, PPV, and NPV of CAD were 60.0%, 59.0%, 0.9%, and 99.6% for 846 patients. In the case of a negative study, it was less time-consuming and easier to make a decision. Among 530 false-positive marks, 459 were identified clearly for pseudo-lesions; the most common cause was marginal shadowing, followed by Cooper’s ligament shadowing, peri-areolar shadowing, rib, and skin lesions. Even though CAD does not improve the performance of ABUS and a large number of false-positive marks were detected, the addition of CAD reduces RT, especially in the case of negative screening ultrasound.
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Abstract
This article gives a brief overview of the development of artificial intelligence in clinical breast imaging. For multiple decades, artificial intelligence (AI) methods have been developed and translated for breast imaging tasks such as detection, diagnosis, and assessing response to therapy. As imaging modalities arise to support breast cancer screening programs and diagnostic examinations, including full-field digital mammography, breast tomosynthesis, ultrasound, and MRI, AI techniques parallel the efforts with more complex algorithms, faster computers, and larger data sets. AI methods include human-engineered radiomics algorithms and deep learning methods. Examples of these AI-supported clinical tasks are given along with commentary on the future.
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Affiliation(s)
- Qiyuan Hu
- Committee on Medical Physics, Department of Radiology, The University of Chicago, 5841 S Maryland Avenue, MC2026, Chicago, IL 60637, USA
| | - Maryellen L Giger
- Committee on Medical Physics, Department of Radiology, The University of Chicago, 5841 S Maryland Avenue, MC2026, Chicago, IL 60637, USA.
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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: 7] [Impact Index Per Article: 2.3] [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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Pros and Cons for Automated Breast Ultrasound (ABUS): A Narrative Review. J Pers Med 2021; 11:jpm11080703. [PMID: 34442347 PMCID: PMC8400952 DOI: 10.3390/jpm11080703] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 07/21/2021] [Accepted: 07/22/2021] [Indexed: 12/15/2022] Open
Abstract
Automated breast ultrasound (ABUS) is an ultrasound technique that tends to be increasingly used as a supplementary technique in the evaluation of patients with dense glandular breasts. Patients with dense breasts have an increased risk of developing breast cancer compared to patients with fatty breasts. Furthermore, for this group of patients, mammography has a low sensitivity in detecting breast cancers, especially if it is not associated with architectural distortion or calcifications. ABUS is a standardized examination with many advantages in both screening and diagnostic settings: it increases the detection rate of breast cancer, improves the workflow, and reduces the examination time. On the other hand, like any imaging technique, ABUS has disadvantages and even some limitations. Many disadvantages can be diminished by additional attention and training. Disadvantages regarding image acquisition are the inability to assess the axilla, the vascularization, and the elasticity of a lesion, while concerning the interpretation, the disadvantages are the artifacts due to poor positioning, lack of contact, motion or lesion related. This article reviews and discusses the indications, the advantages, and disadvantages of the method and also the sources of error in the ABUS examination.
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Lei YM, Yin M, Yu MH, Yu J, Zeng SE, Lv WZ, Li J, Ye HR, Cui XW, Dietrich CF. Artificial Intelligence in Medical Imaging of the Breast. Front Oncol 2021; 11:600557. [PMID: 34367938 PMCID: PMC8339920 DOI: 10.3389/fonc.2021.600557] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2020] [Accepted: 07/07/2021] [Indexed: 12/24/2022] Open
Abstract
Artificial intelligence (AI) has invaded our daily lives, and in the last decade, there have been very promising applications of AI in the field of medicine, including medical imaging, in vitro diagnosis, intelligent rehabilitation, and prognosis. Breast cancer is one of the common malignant tumors in women and seriously threatens women’s physical and mental health. Early screening for breast cancer via mammography, ultrasound and magnetic resonance imaging (MRI) can significantly improve the prognosis of patients. AI has shown excellent performance in image recognition tasks and has been widely studied in breast cancer screening. This paper introduces the background of AI and its application in breast medical imaging (mammography, ultrasound and MRI), such as in the identification, segmentation and classification of lesions; breast density assessment; and breast cancer risk assessment. In addition, we also discuss the challenges and future perspectives of the application of AI in medical imaging of the breast.
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Affiliation(s)
- Yu-Meng Lei
- Department of Medical Ultrasound, China Resources & Wisco General Hospital, Academic Teaching Hospital of Wuhan University of Science and Technology, Wuhan, China
| | - Miao Yin
- Department of Medical Ultrasound, China Resources & Wisco General Hospital, Academic Teaching Hospital of Wuhan University of Science and Technology, Wuhan, China
| | - Mei-Hui Yu
- Department of Medical Ultrasound, China Resources & Wisco General Hospital, Academic Teaching Hospital of Wuhan University of Science and Technology, Wuhan, China
| | - Jing Yu
- Department of Medical Ultrasound, China Resources & Wisco General Hospital, Academic Teaching Hospital of Wuhan University of Science and Technology, Wuhan, China
| | - Shu-E Zeng
- Department of Medical Ultrasound, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wen-Zhi Lv
- Department of Artificial Intelligence, Julei Technology, Wuhan, China
| | - Jun Li
- Department of Medical Ultrasound, The First Affiliated Hospital of Medical College, Shihezi University, Xinjiang, China
| | - Hua-Rong Ye
- Department of Medical Ultrasound, China Resources & Wisco General Hospital, Academic Teaching Hospital of Wuhan University of Science and Technology, Wuhan, China
| | - Xin-Wu Cui
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Christoph F Dietrich
- Department Allgemeine Innere Medizin (DAIM), Kliniken Beau Site, Salem und Permanence, Bern, Switzerland
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Allajbeu I, Hickman SE, Payne N, Moyle P, Taylor K, Sharma N, Gilbert FJ. Automated Breast Ultrasound: Technical Aspects, Impact on Breast Screening, and Future Perspectives. CURRENT BREAST CANCER REPORTS 2021. [DOI: 10.1007/s12609-021-00423-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Abstract
Purpose of Review
Automated breast ultrasound (ABUS) is a three-dimensional imaging technique, used as a supplemental screening tool in women with dense breasts. This review considers the technical aspects, pitfalls, and the use of ABUS in screening and clinical practice, together with new developments and future perspectives.
Recent Findings
ABUS has been approved in the USA and Europe as a screening tool for asymptomatic women with dense breasts in addition to mammography. Supplemental US screening has high sensitivity for cancer detection, especially early-stage invasive cancers, and reduces the frequency of interval cancers. ABUS has similar diagnostic performance to handheld ultrasound (HHUS) and is designed to overcome the drawbacks of operator dependence and poor reproducibility. Concerns with ABUS, like HHUS, include relatively high recall rates and lengthy reading time when compared to mammography. ABUS is a new technique with unique features; therefore, adequate training is required to improve detection and reduce false positives. Computer-aided detection may reduce reading times and improve cancer detection. Other potential applications of ABUS include local staging, treatment response evaluation, breast density assessment, and integration of radiomics.
Summary
ABUS provides an efficient, reproducible, and comprehensive supplemental imaging technique in breast screening. Developments with computer-aided detection may improve the sensitivity and specificity as well as radiologist confidence and reduce reading times, making this modality acceptable in large volume screening centers.
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Grimm LJ. Radiomics: A Primer for Breast Radiologists. JOURNAL OF BREAST IMAGING 2021; 3:276-287. [PMID: 38424774 DOI: 10.1093/jbi/wbab014] [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: 12/02/2020] [Indexed: 03/02/2024]
Abstract
Radiomics has a long-standing history in breast imaging with computer-aided detection (CAD) for screening mammography developed in the late 20th century. Although conventional CAD had widespread adoption, the clinical benefits for experienced breast radiologists were debatable due to high false-positive marks and subsequent increased recall rates. The dramatic growth in recent years of artificial intelligence-based analysis, including machine learning and deep learning, has provided numerous opportunities for improved modern radiomics work in breast imaging. There has been extensive radiomics work in mammography, digital breast tomosynthesis, MRI, ultrasound, PET-CT, and combined multimodality imaging. Specific radiomics outcomes of interest have been diverse, including CAD, prediction of response to neoadjuvant therapy, lesion classification, and survival, among other outcomes. Additionally, the radiogenomics subfield that correlates radiomics features with genetics has been very proliferative, in parallel with the clinical validation of breast cancer molecular subtypes and gene expression assays. Despite the promise of radiomics, there are important challenges related to image normalization, limited large unbiased data sets, and lack of external validation. Much of the radiomics work to date has been exploratory using single-institution retrospective series for analysis, but several promising lines of investigation have made the leap to clinical practice with commercially available products. As a result, breast radiologists will increasingly be incorporating radiomics-based tools into their daily practice in the near future. Therefore, breast radiologists must have a broad understanding of the scope, applications, and limitations of radiomics work.
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Affiliation(s)
- Lars J Grimm
- Duke University, Department of Radiology, Durham, NC, USA
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Hsu HH, Ko KH, Chou YC, Wu YC, Chiu SH, Chang CK, Chang WC. Performance and reading time of lung nodule identification on multidetector CT with or without an artificial intelligence-powered computer-aided detection system. Clin Radiol 2021; 76:626.e23-626.e32. [PMID: 34023068 DOI: 10.1016/j.crad.2021.04.006] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Accepted: 04/15/2021] [Indexed: 10/21/2022]
Abstract
AIM To compare the performance and reading time of different readers using automatic artificial intelligence (AI)-powered computer-aided detection (CAD) to detect lung nodules in different reading modes. MATERIALS AND METHODS One hundred and fifty multidetector computed tomography (CT) datasets containing 340 nodules ≤10 mm in diameter were collected retrospectively. A CAD with vessel-suppressed function was used to interpret the images. Three junior and three senior readers were assigned to read (1) CT images without CAD, (2) second-read using CAD in which CAD was applied only after initial unassisted assessment, and (3) a concurrent read with CAD in which CAD was applied at the start of assessment. Diagnostic performances and reading times were compared using analysis of variance. RESULTS For all readers, the mean sensitivity improved from 64% (95% confidence interval [CI]: 62%, 66%) for the without-CAD mode to 82% (95% CI: 80%, 84%) for the second-reading mode and to 80% (95% CI: 79%, 82%) for the concurrent-reading mode (p<0.001). There was no significant difference between the two modes in terms of the mean sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) for both junior and senior readers and all readers (p>0.05). The reading time of all readers was significantly shorter for the concurrent-reading mode (124 ± 25 seconds) compared to without CAD (156 ± 34 seconds; p<0.001) and the second-reading mode (197 ± 46 seconds; p<0.001). CONCLUSION In CAD for lung nodules at CT, the second-reading mode and concurrent-reading mode may improve detection performance for all readers in both screening and clinical routine practice. Concurrent use of CAD is more efficient for both junior and senior readers.
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Affiliation(s)
- H-H Hsu
- Department of Radiology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan.
| | - K-H Ko
- Department of Radiology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Y-C Chou
- School of Public Health, National Defense Medical Center, Taipei, Taiwan
| | - Y-C Wu
- Department of Radiology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - S-H Chiu
- Department of Radiology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - C-K Chang
- Department of Radiology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - W-C Chang
- Department of Radiology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
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Spear GG, Mendelson EB. Automated breast ultrasound: Supplemental screening for average-risk women with dense breasts. Clin Imaging 2020; 76:15-25. [PMID: 33548888 DOI: 10.1016/j.clinimag.2020.12.007] [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: 06/24/2020] [Revised: 11/24/2020] [Accepted: 12/17/2020] [Indexed: 11/25/2022]
Abstract
OBJECTIVE We review ultrasound (US) options for supplemental breast cancer screening of average risk women with dense breasts. CONCLUSION Performance data of physician-performed handheld US (HHUS), technologist-performed HHUS, and automated breast ultrasound (AUS) indicate that all are appropriate for adjunctive screening. Volumetric 3D acquisitions, reduced operator dependence, protocol standardization, reliable comparison with previous studies, independence of performance and interpretation, and whole breast depiction on coronal view may favor selection of AUS. Important considerations are workflow adjustments for physicians and staff.
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Affiliation(s)
- Georgia Giakoumis Spear
- NorthShore University HealthSystem, The University of Chicago Pritzker School of Medicine, United States of America.
| | - Ellen B Mendelson
- Feinberg School of Medicine, Northwestern University, Chicago, IL, United States of America
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Tonozuka R, Mukai S, Itoi T. The Role of Artificial Intelligence in Endoscopic Ultrasound for Pancreatic Disorders. Diagnostics (Basel) 2020; 11:diagnostics11010018. [PMID: 33374181 PMCID: PMC7824322 DOI: 10.3390/diagnostics11010018] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 12/21/2020] [Accepted: 12/22/2020] [Indexed: 02/07/2023] Open
Abstract
The use of artificial intelligence (AI) in various medical imaging applications has expanded remarkably, and several reports have focused on endoscopic ultrasound (EUS) images of the pancreas. This review briefly summarizes each report in order to help endoscopists better understand and utilize the potential of this rapidly developing AI, after a description of the fundamentals of the AI involved, as is necessary for understanding each study. At first, conventional computer-aided diagnosis (CAD) was used, which extracts and selects features from imaging data using various methods and introduces them into machine learning algorithms as inputs. Deep learning-based CAD utilizing convolutional neural networks has been used; in these approaches, the images themselves are used as inputs, and more information can be analyzed in less time and with higher accuracy. In the field of EUS imaging, although AI is still in its infancy, further research and development of AI applications is expected to contribute to the role of optical biopsy as an alternative to EUS-guided tissue sampling while also improving diagnostic accuracy through double reading with humans and contributing to EUS education.
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Fujioka T, Mori M, Kubota K, Oyama J, Yamaga E, Yashima Y, Katsuta L, Nomura K, Nara M, Oda G, Nakagawa T, Kitazume Y, Tateishi U. The Utility of Deep Learning in Breast Ultrasonic Imaging: A Review. Diagnostics (Basel) 2020; 10:diagnostics10121055. [PMID: 33291266 PMCID: PMC7762151 DOI: 10.3390/diagnostics10121055] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2020] [Revised: 12/04/2020] [Accepted: 12/05/2020] [Indexed: 12/13/2022] Open
Abstract
Breast cancer is the most frequently diagnosed cancer in women; it poses a serious threat to women's health. Thus, early detection and proper treatment can improve patient prognosis. Breast ultrasound is one of the most commonly used modalities for diagnosing and detecting breast cancer in clinical practice. Deep learning technology has made significant progress in data extraction and analysis for medical images in recent years. Therefore, the use of deep learning for breast ultrasonic imaging in clinical practice is extremely important, as it saves time, reduces radiologist fatigue, and compensates for a lack of experience and skills in some cases. This review article discusses the basic technical knowledge and algorithms of deep learning for breast ultrasound and the application of deep learning technology in image classification, object detection, segmentation, and image synthesis. Finally, we discuss the current issues and future perspectives of deep learning technology in breast ultrasound.
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Affiliation(s)
- Tomoyuki Fujioka
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Tokyo 113-8510, Japan; (T.F.); (K.K.); (J.O.); (E.Y.); (Y.Y.); (L.K.); (K.N.); (M.N.); (Y.K.); (U.T.)
| | - Mio Mori
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Tokyo 113-8510, Japan; (T.F.); (K.K.); (J.O.); (E.Y.); (Y.Y.); (L.K.); (K.N.); (M.N.); (Y.K.); (U.T.)
- Correspondence: ; Tel.: +81-3-5803-5311; Fax: +81-3-5803-0147
| | - Kazunori Kubota
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Tokyo 113-8510, Japan; (T.F.); (K.K.); (J.O.); (E.Y.); (Y.Y.); (L.K.); (K.N.); (M.N.); (Y.K.); (U.T.)
- Department of Radiology, Dokkyo Medical University, Tochigi 321-0293, Japan
| | - Jun Oyama
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Tokyo 113-8510, Japan; (T.F.); (K.K.); (J.O.); (E.Y.); (Y.Y.); (L.K.); (K.N.); (M.N.); (Y.K.); (U.T.)
| | - Emi Yamaga
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Tokyo 113-8510, Japan; (T.F.); (K.K.); (J.O.); (E.Y.); (Y.Y.); (L.K.); (K.N.); (M.N.); (Y.K.); (U.T.)
| | - Yuka Yashima
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Tokyo 113-8510, Japan; (T.F.); (K.K.); (J.O.); (E.Y.); (Y.Y.); (L.K.); (K.N.); (M.N.); (Y.K.); (U.T.)
| | - Leona Katsuta
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Tokyo 113-8510, Japan; (T.F.); (K.K.); (J.O.); (E.Y.); (Y.Y.); (L.K.); (K.N.); (M.N.); (Y.K.); (U.T.)
| | - Kyoko Nomura
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Tokyo 113-8510, Japan; (T.F.); (K.K.); (J.O.); (E.Y.); (Y.Y.); (L.K.); (K.N.); (M.N.); (Y.K.); (U.T.)
| | - Miyako Nara
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Tokyo 113-8510, Japan; (T.F.); (K.K.); (J.O.); (E.Y.); (Y.Y.); (L.K.); (K.N.); (M.N.); (Y.K.); (U.T.)
- Department of Breast Surgery, Tokyo Metropolitan Cancer and Infectious Diseases Center Komagome Hospital, Tokyo 113-8677, Japan
| | - Goshi Oda
- Department of Surgery, Breast Surgery, Tokyo Medical and Dental University, Tokyo 113-8510, Japan; (G.O.); (T.N.)
| | - Tsuyoshi Nakagawa
- Department of Surgery, Breast Surgery, Tokyo Medical and Dental University, Tokyo 113-8510, Japan; (G.O.); (T.N.)
| | - Yoshio Kitazume
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Tokyo 113-8510, Japan; (T.F.); (K.K.); (J.O.); (E.Y.); (Y.Y.); (L.K.); (K.N.); (M.N.); (Y.K.); (U.T.)
| | - Ukihide Tateishi
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Tokyo 113-8510, Japan; (T.F.); (K.K.); (J.O.); (E.Y.); (Y.Y.); (L.K.); (K.N.); (M.N.); (Y.K.); (U.T.)
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Jiang Y. Receiver Operating Characteristic (ROC) Analysis of Image Search-and-Localize Tasks. Acad Radiol 2020; 27:1742-1750. [PMID: 32033862 DOI: 10.1016/j.acra.2019.12.020] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Revised: 12/18/2019] [Accepted: 12/20/2019] [Indexed: 10/25/2022]
Abstract
RATIONALE AND OBJECTIVES Receiver operating characteristic (ROC) analysis for the common image search-and-localize task, in which readers search an image for lesion or lesions not knowing a priori any exists, has been studied for over four decades. However, a satisfactory solution seems elusive. MATERIALS AND METHODS We show that the ROC curve predictive of clinical outcomes where readers are penalized appropriately for not correctly localizing known lesions cannot be obtained because it is a missing data problem. Further, this ROC curve is between the case-based ROC curve where readers are not penalized and the lesion-based ROC curve where penalty applies. Moreover, the lesion-based ROC curve is the LROC curve proposed by Starr et al. We show maximum-likelihood (ML) estimation of the LROC curve, validation of this procedure with Monte Carlo simulations, and its application to reader ROC datasets. RESULTS Monte Carlo simulations validated ML estimation of area under the LROC curve (AUC) and its variance. Example applications showed that ML estimate of LROC curve fits experimental datasets. CONCLUSION The ROC curve predictive of clinical performance cannot be estimated from reader ROC data alone because it is a missing data problem, and is between the case-based ROC curve where readers are not penalized for not correctly identifying known lesions and the lesion-based ROC curve where penalty applies. The lesion-based ROC curve is the LROC curve proposed by Starr et al. and can be estimated via ML estimation.
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Kim SH, Kim HH, Moon WK. Automated Breast Ultrasound Screening for Dense Breasts. Korean J Radiol 2020; 21:15-24. [PMID: 31920025 PMCID: PMC6960307 DOI: 10.3348/kjr.2019.0176] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Accepted: 09/04/2019] [Indexed: 11/25/2022] Open
Abstract
Mammography is the primary screening method for breast cancers. However, the sensitivity of mammographic screening is lower for dense breasts, which are an independent risk factor for breast cancers. Automated breast ultrasound (ABUS) is used as an adjunct to mammography for screening breast cancers in asymptomatic women with dense breasts. It is an effective screening modality with diagnostic accuracy comparable to that of handheld ultrasound (HHUS). Radiologists should be familiar with the unique display mode, imaging features, and artifacts in ABUS, which differ from those in HHUS. The purpose of this study was to provide a comprehensive review of the clinical significance of dense breasts and ABUS screening, describe the unique features of ABUS, and introduce the method of use and interpretation of ABUS.
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Affiliation(s)
- Sung Hun Kim
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea.
| | - Hak Hee Kim
- Department of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Woo Kyung Moon
- Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea
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Jia M, Lin X, Zhou X, Yan H, Chen Y, Liu P, Bao L, Li A, Basu P, Qiao Y, Sankaranarayanan R. Diagnostic performance of automated breast ultrasound and handheld ultrasound in women with dense breasts. Breast Cancer Res Treat 2020; 181:589-597. [PMID: 32338323 DOI: 10.1007/s10549-020-05625-2] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Accepted: 04/01/2020] [Indexed: 10/24/2022]
Abstract
PURPOSE As an adjunct to mammography, ultrasound can improve the detection of breast cancer in women with dense breasts. We aimed to evaluate the diagnostic performance of automated breast ultrasound system (ABUS) and handheld ultrasound (HHUS) in Chinese women with dense breasts, both in combination with mammography and separately. METHODS This is a cross-sectional multicenter clinical research study. Nine hundred and thirty-seven women with dense breasts underwent ABUS, HHUS, and mammography at one of five tertiary-care hospitals. The diagnostic performance of ABUS and HHUS was evaluated in combination with mammography, or separately in women with mammography-negative dense breasts. The agreement between ABUS and HHUS in breast cancer detection was also assessed. RESULTS The sensitivity of the combination of ABUS or HHUS with mammography was 99.1% (219/221), and the specificities were 86.9% (622/716) and 84.9% (608/716), respectively. The area under the curve was 0.93 for ABUS combined with mammography and 0.92 for that of HHUS combined with mammography. Statistically significant agreement between ABUS and HHUS in breast cancer detection was observed (percent agreement = 0.94, κ = 0.85). The incremental cancer detection rate in mammography-negative dense breasts was 42.8 per 1000 ultrasound examinations. CONCLUSIONS Both ABUS and HHUS as adjuncts to mammography can significantly improve the breast cancer detection rate in women with dense breasts, and there is a strong correlation between them. Given the high prevalence of dense breasts and the multiple advantages of ABUS over HHUS, such as less operator dependence and reproducibility, ABUS showed great potential for use in breast cancer early detection, especially in resource-limited areas.
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Affiliation(s)
- Mengmeng Jia
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Xi Lin
- State Key Laboratory of Oncology in Southern China, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
| | - Xiang Zhou
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Huijiao Yan
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Yaqing Chen
- Xin Hua Hospital, Affiliated To Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China
| | - Peifang Liu
- Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, 300060, China
| | - Lingyun Bao
- The First People's Hospital of Hangzhou, Affiliated Hangzhou Hospital of Nanjing Medical University, Hangzhou, 310006, China
| | - Anhua Li
- State Key Laboratory of Oncology in Southern China, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
| | - Partha Basu
- Screening Group, Early Detection and Prevention Section, International Agency for Research On Cancer, WHO, 150 Cours Albert ThomasCedex 08, 69372, Lyon, France
| | - Youlin Qiao
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
| | - Rengaswamy Sankaranarayanan
- Research Triangle Institute, International-India, Commercial Tower, Pullman Hotel Aerocity, New Delhi, 100037, India
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Brunetti N, De Giorgis S, Zawaideh J, Rossi F, Calabrese M, Tagliafico AS. Comparison between execution and reading time of 3D ABUS versus HHUS. Radiol Med 2020; 125:1243-1248. [PMID: 32367322 DOI: 10.1007/s11547-020-01209-8] [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: 01/14/2020] [Accepted: 04/20/2020] [Indexed: 01/09/2023]
Abstract
BACKGROUND Breast density is an independent risk factor for breast cancer. Mammography is supplemented with handheld ultrasound (HHUS) to increase sensitivity. Automatic breast ultrasound (ABUS) is an alternative to HHUS. Our study wanted to assess the difference in execution and reading time between ABUS and HHUS. METHODS AND MATERIALS N = 221 women were evaluated consecutively between January 2019 and June 2019 (average age 53 years; range 24-89). The execution and reading time of ABUS and HHUS was calculated with an available stopwatch. Time started for both procedures when the patient was ready on the examination table to be examined to the end of image acquisition and interpretation. RESULTS No patients interrupted the exam due to pain or discomfort. N = 221 women underwent ABUS and HHUS; N = 11 patients refused to undergo both procedures due to time constraints and refused ABUS; therefore, 210 patients were enrolled with both ABUS and HHUS available. The average time to perform and read the exam was 5 min for HHUS (DS ± 1.5) with a maximum time of 11 min and a minimum of 2 min. The average time with ABUS was 17 min (DS ± 3.8, with a maximum time of 31 min and a minimum time of 9 min). The ABUS technique took longer to be performed in all patients, with an average difference of 11 min (range 3-23 min) per patient, P < 0,001. Separating ABUS execution from reading time we highlighted as ABUS execution is more time-consuming respect HHUS. In addition, we can underline that time required by radiologists is longer for ABUS even only considering the interpretation time of the exam. CONCLUSION A significant difference was observed in the execution and reading time of the two exams, where the HHUS method was more rapid and tolerated.
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Affiliation(s)
- Nicole Brunetti
- Department of Health Sciences (DISSAL)- Radiology Section, University of Genova, Via L.B. Alberti 2, 16132, Genoa, Italy.
| | - Sara De Giorgis
- Department of Health Sciences (DISSAL)- Radiology Section, University of Genova, Via L.B. Alberti 2, 16132, Genoa, Italy
| | - Jeries Zawaideh
- Department of Health Sciences (DISSAL)- Radiology Section, University of Genova, Via L.B. Alberti 2, 16132, Genoa, Italy
| | - Federica Rossi
- Department of Health Sciences (DISSAL)- Radiology Section, University of Genova, Via L.B. Alberti 2, 16132, Genoa, Italy
| | - Massimo Calabrese
- IRCCS - Ospedale Policlinico San Martino, Largo Rosanna Benzi. 10, 16132, Genoa, Italy
| | - Alberto Stefano Tagliafico
- Department of Health Sciences (DISSAL)- Radiology Section, University of Genova, Via L.B. Alberti 2, 16132, Genoa, Italy.,IRCCS - Ospedale Policlinico San Martino, Largo Rosanna Benzi. 10, 16132, Genoa, Italy
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Berg WA, Vourtsis A. Screening Breast Ultrasound Using Handheld or Automated Technique in Women with Dense Breasts. JOURNAL OF BREAST IMAGING 2019; 1:283-296. [PMID: 38424808 DOI: 10.1093/jbi/wbz055] [Citation(s) in RCA: 54] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2019] [Accepted: 08/01/2019] [Indexed: 03/02/2024]
Abstract
In women with dense breasts (heterogeneously or extremely dense), adding screening ultrasound to mammography increases detection of node-negative invasive breast cancer. Similar incremental cancer detection rates averaging 2.1-2.7 per 1000 have been observed for physician- and technologist-performed handheld ultrasound (HHUS) and automated ultrasound (AUS). Adding screening ultrasound (US) for women with dense breasts significantly reduces interval cancer rates. Training is critical before interpreting examinations for both modalities, and a learning curve to achieve optimal performance has been observed. On average, about 3% of women will be recommended for biopsy on the prevalence round because of screening US, with a wide range of 2%-30% malignancy rates for suspicious findings seen only on US. Breast Imaging Reporting and Data System 3 lesions identified only on screening HHUS can be safely followed at 1 year rather than 6 months. Computer-aided detection and diagnosis software can augment performance of AUS and HHUS; ongoing research on machine learning and deep learning algorithms will likely improve outcomes and workflow with screening US.
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Affiliation(s)
- Wendie A Berg
- University of Pittsburgh School of Medicine, Magee-Womens Hospital of the University of Pittsburgh School of Medicine, Department of Radiology, Pittsburgh, PA
| | - Athina Vourtsis
- Diagnostic Mammography Medical Diagnostic Imaging Unit, Athens, Greece
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Vourtsis A. Three-dimensional automated breast ultrasound: Technical aspects and first results. Diagn Interv Imaging 2019; 100:579-592. [DOI: 10.1016/j.diii.2019.03.012] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2019] [Revised: 03/08/2019] [Accepted: 03/15/2019] [Indexed: 12/29/2022]
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Yang S, Gao X, Liu L, Shu R, Yan J, Zhang G, Xiao Y, Ju Y, Zhao N, Song H. Performance and Reading Time of Automated Breast US with or without Computer-aided Detection. Radiology 2019; 292:540-549. [PMID: 31210612 DOI: 10.1148/radiol.2019181816] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
BackgroundComputer-aided detection (CAD) systems may be used to help radiologists interpret automated breast (AB) US images. However, the optimal use of CAD with AB US has, to the knowledge of the authors, not been determined.PurposeTo compare the performance and reading time of different readers by using AB US CAD system to detect breast cancer in different reading modes.Materials and MethodsIn this retrospective study, 1485 AB US images (282 with malignant lesions, 695 with benign lesions, and 508 healthy) in 1452 women (mean age, 43.7 years; age range, 19-82 years) including 529 (36.4%) women who were asymptomatic were collected between 2016 and 2017. A CAD system was used to interpret the images. Three novice readers with 1-3 years of US experience and three experienced readers with 5-10 years of US experience were assigned to read AB US images without CAD, at a second reading (after the reader completed a full unaided interpretation), and at concurrent reading (use of CAD at the start of the assessment). Diagnostic performances and reading times were compared by using analysis of variance.ResultsFor all readers, the mean area under the receiver operating characteristic curve improved from 0.88 (95% confidence interval [CI]: 0.85, 0.91) at without-CAD mode to 0.91 (95% CI: 0.89, 0.92; P < .001) at the second-reading mode and 0.90 (95% CI: 0.89, 0.92; P = .002) at the concurrent-reading mode. The mean sensitivity of novice readers in women who were asymptomatic improved from 67% (95% CI: 63%, 74%) at without-CAD mode to 88% (95% CI: 84%, 89%) at both the second-reading mode and the concurrent-reading mode (P = .003). Compared with the without-CAD and second-reading modes, the mean reading time per volume of concurrent reading was 16 seconds (95% CI: 11, 22; P < .001) and 27 seconds (95% CI: 21, 32; P < .001) shorter, respectively.ConclusionComputer-aided detection (CAD) was helpful for novice readers to improve cancer detection at automated breast US in women who were asymptomatic. CAD was more efficient when used concurrently for all readers.© RSNA, 2019Online supplemental material is available for this article.See also the editorial by Slanetz in this issue.
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Affiliation(s)
- Shanling Yang
- From the Department of Ultrasonic Medicine, Xijing Hospital of the Fourth Military Medical University, No. 127 Changle West Road, Xi'an, Shaanxi, China 710032
| | - Xican Gao
- From the Department of Ultrasonic Medicine, Xijing Hospital of the Fourth Military Medical University, No. 127 Changle West Road, Xi'an, Shaanxi, China 710032
| | - Liwen Liu
- From the Department of Ultrasonic Medicine, Xijing Hospital of the Fourth Military Medical University, No. 127 Changle West Road, Xi'an, Shaanxi, China 710032
| | - Rui Shu
- From the Department of Ultrasonic Medicine, Xijing Hospital of the Fourth Military Medical University, No. 127 Changle West Road, Xi'an, Shaanxi, China 710032
| | - Jingru Yan
- From the Department of Ultrasonic Medicine, Xijing Hospital of the Fourth Military Medical University, No. 127 Changle West Road, Xi'an, Shaanxi, China 710032
| | - Ge Zhang
- From the Department of Ultrasonic Medicine, Xijing Hospital of the Fourth Military Medical University, No. 127 Changle West Road, Xi'an, Shaanxi, China 710032
| | - Yao Xiao
- From the Department of Ultrasonic Medicine, Xijing Hospital of the Fourth Military Medical University, No. 127 Changle West Road, Xi'an, Shaanxi, China 710032
| | - Yan Ju
- From the Department of Ultrasonic Medicine, Xijing Hospital of the Fourth Military Medical University, No. 127 Changle West Road, Xi'an, Shaanxi, China 710032
| | - Ni Zhao
- From the Department of Ultrasonic Medicine, Xijing Hospital of the Fourth Military Medical University, No. 127 Changle West Road, Xi'an, Shaanxi, China 710032
| | - Hongping Song
- From the Department of Ultrasonic Medicine, Xijing Hospital of the Fourth Military Medical University, No. 127 Changle West Road, Xi'an, Shaanxi, China 710032
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Vourtsis A, Berg WA. Breast density implications and supplemental screening. Eur Radiol 2019; 29:1762-1777. [PMID: 30255244 PMCID: PMC6420861 DOI: 10.1007/s00330-018-5668-8] [Citation(s) in RCA: 104] [Impact Index Per Article: 20.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2018] [Revised: 06/21/2018] [Accepted: 07/13/2018] [Indexed: 12/14/2022]
Abstract
Digital breast tomosynthesis (DBT) has been widely implemented in place of 2D mammography, although it is less effective in women with extremely dense breasts. Breast ultrasound detects additional early-stage, invasive breast cancers when combined with mammography; however, its relevant limitations, including the shortage of trained operators, operator dependence and small field of view, have limited its widespread implementation. Automated breast sonography (ABS) is a promising technique but the time to interpret and false-positive rates need to be improved. Supplemental screening with contrast-enhanced magnetic resonance imaging (MRI) in high-risk women reduces late-stage disease; abbreviated MRI protocols may reduce cost and increase accessibility to women of average risk with dense breasts. Contrast-enhanced digital mammography (CEDM) and molecular breast imaging improve cancer detection but require further validation for screening and direct biopsy guidance should be implemented for any screening modality. This article reviews the status of screening women with dense breasts. KEY POINTS: • The sensitivity of mammography is reduced in women with dense breasts. Supplemental screening with US detects early-stage, invasive breast cancers. • Tomosynthesis reduces recall rate and increases cancer detection rate but is less effective in women with extremely dense breasts. • Screening MRI improves early diagnosis of breast cancer more than ultrasound and is currently recommended for women at high risk. Risk assessment is needed, to include breast density, to ascertain who should start early annual MRI screening.
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Affiliation(s)
- Athina Vourtsis
- "Diagnostic Mammography", Medical Diagnostic Imaging Unit, Founding President of the Hellenic Breast Imaging Society, Kifisias Ave 362, Chalandri, 15233, Athens, Greece.
| | - Wendie A Berg
- Department of Radiology, Magee-Womens Hospital of UPMC, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
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Classification of malignant and benign tissue with logistic regression. INFORMATICS IN MEDICINE UNLOCKED 2019. [DOI: 10.1016/j.imu.2019.100189] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
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