1
|
Chiu J, Bova D, Spear G, Ecanow J, Choate A, Besson P, Caluser C. Improving Lesion Location Reproducibility in Handheld Breast Ultrasound. Diagnostics (Basel) 2024; 14:1602. [PMID: 39125478 PMCID: PMC11311286 DOI: 10.3390/diagnostics14151602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2024] [Revised: 07/14/2024] [Accepted: 07/16/2024] [Indexed: 08/12/2024] Open
Abstract
Interoperator variability in the reproducibility of breast lesions found by handheld ultrasound (HHUS) can significantly interfere with clinical care. This study analyzed the features associated with breast mass position differences during HHUS. The ability of operators to reproduce the position of small masses and the time required to generate annotations with and without a computer-assisted scanning device (DEVICE) were also evaluated. This prospective study included 28 patients with 34 benign or probably benign small breast masses. Two operators generated manual and automated position annotations for each mass. The probe and body positions were systematically varied during scanning with the DEVICE, and the features describing mass movement were used in three logistic regression models trained to discriminate small from large breast mass displacements (cutoff: 10 mm). All models successfully discriminated small from large breast mass displacements (areas under the curve: 0.78 to 0.82). The interoperator localization precision was 6.6 ± 2.8 mm with DEVICE guidance and 19.9 ± 16.1 mm with manual annotations. Computer-assisted scanning reduced the time to annotate and reidentify a mass by 33 and 46 s on average, respectively. The results demonstrated that breast mass location reproducibility and exam efficiency improved by controlling operator actionable features with computer-assisted HHUS.
Collapse
Affiliation(s)
- James Chiu
- Department of Radiology, Endeavor Health, 2650 Ridge Ave, Evanston, IL 60201, USA
| | - Davide Bova
- Dacia Medical Clinic, 917 S Oak Park Ave, Suite B, Oak Park, IL 60304, USA
- Department of Radiology, Loyola University Medical Center, 2160 S First Ave, Maywood, IL 60153, USA
| | - Georgia Spear
- Department of Radiology, Endeavor Health, 2650 Ridge Ave, Evanston, IL 60201, USA
| | - Jacob Ecanow
- Department of Radiology, Endeavor Health, 2650 Ridge Ave, Evanston, IL 60201, USA
| | - Alyssa Choate
- Department of Radiology, Endeavor Health, 2650 Ridge Ave, Evanston, IL 60201, USA
| | - Pierre Besson
- MetriTrack Inc., 4415 Harrison St., #243, Hillside, IL 60162, USA
| | - Calin Caluser
- MetriTrack Inc., 4415 Harrison St., #243, Hillside, IL 60162, USA
- Midwest Center for Advanced Imaging, Rush University Medical System, 4355 Montgomery Rd, Naperville, IL 60564, USA
| |
Collapse
|
2
|
Upadhyay N, Wolska J. Imaging the dense breast. J Surg Oncol 2024; 130:29-35. [PMID: 38685673 DOI: 10.1002/jso.27661] [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: 04/08/2024] [Accepted: 04/09/2024] [Indexed: 05/02/2024]
Abstract
The sensitivity of mammography reduces as breast density increases, which impacts breast screening and locoregional staging in breast cancer. Supplementary imaging with other modalities can offer improved cancer detection, but this often comes at the cost of more false positives. Magnetic resonance imaging and contrast-enhanced mammography, which assess tumour enhancement following contrast administration, are more sensitive than digital breast tomosynthesis and ultrasound, which predominantly rely on the assessment of tumour morphology.
Collapse
Affiliation(s)
- Neil Upadhyay
- Faculty of Medicine, Imperial College London, London, UK
- Imaging Department, Imperial College Healthcare NHS Trust, London, UK
| | - Joanna Wolska
- Imaging Department, Imperial College Healthcare NHS Trust, London, UK
| |
Collapse
|
3
|
Sorin V, Bufman H, Bernstein-Molho R, Faermann R, Friedman E, Raskin D, Balint Lahat N, Sklair-Levy M. Breast cancer screening in BRCA1/2 pathogenic sequence variant carriers during pregnancy and lactation. Clin Imaging 2024; 111:110189. [PMID: 38759599 DOI: 10.1016/j.clinimag.2024.110189] [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: 03/08/2024] [Revised: 05/09/2024] [Accepted: 05/09/2024] [Indexed: 05/19/2024]
Abstract
OBJECTIVES Women harboring germline BRCA1/BRCA2 pathogenic sequence variants (PSVs) are at an increased risk for breast cancer. There are no established guidelines for screening during pregnancy and lactation in BRCA carriers. The aim of this study was to evaluate the utility of whole-breast ultrasound (US) screening in pregnant and lactating BRCA PSV carriers. METHODS Data were retrospectively collected from medical records of BRCA PSV carriers between 2014 and 2020, with follow-up until 2021. Associations between imaging intervals, number of examinations performed and pregnancy-associated breast cancers (PABCs) were examined. PABCs and cancers diagnosed at follow-up were evaluated and characteristics were compared between the two groups. RESULTS Overall 212 BRCA PSV carriers were included. Mean age was 33.6 years (SD 3.93, range 25-43 years). During 274 screening periods at pregnancy and lactation, eight (2.9 %) PABCs were diagnosed. An additional eight cancers were diagnosed at follow-up. Three out of eight (37.5 %) PABCs were diagnosed by US, whereas clinical breast examination (n = 3), mammography (n = 1) and MRI (n = 1) accounted for the other PACB diagnoses. One PABC was missed by US. The interval from negative imaging to cancer diagnosis was significantly shorter for PABCs compared with cancers diagnosed at follow-up (3.96 ± 2.14 vs. 11.2 ± 4.46 months, P = 0.002). CONCLUSION In conclusion, pregnant BRCA PSV carriers should not delay screening despite challenges like altered breast tissue and hesitancy towards mammography. If no alternatives exist, whole-breast ultrasound can be used. For lactating and postpartum women, a regular screening routine alternating between mammography and MRI is recommended.
Collapse
Affiliation(s)
- Vera Sorin
- Department of Diagnostic Imaging, Chaim Sheba Medical Center, Tel Hashomer, Israel; The Faculty of Medicine, Tel-Aviv University, Israel.
| | - Hila Bufman
- Department of Diagnostic Imaging, Chaim Sheba Medical Center, Tel Hashomer, Israel; The Faculty of Medicine, Tel-Aviv University, Israel
| | - Rinat Bernstein-Molho
- The Faculty of Medicine, Tel-Aviv University, Israel; Department of Oncology, Chaim Sheba Medical Center, Tel Hashomer, Israel; Oncogenetics Unit, Institute of Human Genetics, Chaim Sheba Medical Center, Tel Hashomer, Israel
| | - Renata Faermann
- Department of Diagnostic Imaging, Chaim Sheba Medical Center, Tel Hashomer, Israel; The Faculty of Medicine, Tel-Aviv University, Israel
| | - Eitan Friedman
- The Faculty of Medicine, Tel-Aviv University, Israel; Oncogenetics Unit, Institute of Human Genetics, Chaim Sheba Medical Center, Tel Hashomer, Israel; The Meirav High Risk Clinic, Chaim Sheba Medical Center, Tel-Hashomer, Israel
| | - Daniel Raskin
- Department of Diagnostic Imaging, Chaim Sheba Medical Center, Tel Hashomer, Israel; The Faculty of Medicine, Tel-Aviv University, Israel
| | - Nora Balint Lahat
- The Faculty of Medicine, Tel-Aviv University, Israel; Department of Pathology, Chaim Sheba Medical Center, Tel Hashomer, Israel
| | - Miri Sklair-Levy
- Department of Diagnostic Imaging, Chaim Sheba Medical Center, Tel Hashomer, Israel; The Faculty of Medicine, Tel-Aviv University, Israel
| |
Collapse
|
4
|
Liu S, Zheng S, Qin M, Xie Y, Yang K, Liu X. Knowledge, attitude, and practice toward ultrasound screening for breast cancer among women. Front Public Health 2024; 12:1309797. [PMID: 38855455 PMCID: PMC11160319 DOI: 10.3389/fpubh.2024.1309797] [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: 10/08/2023] [Accepted: 05/06/2024] [Indexed: 06/11/2024] Open
Abstract
Background Several obstacles can hinder breast cancer screening. This study aimed to investigate the knowledge, attitude, and practice (KAP) toward ultrasound screening for breast cancer in women. Methods This cross-sectional study recruited women who visited the breast specialist clinic of Zhongshan City People's Hospital (a tertiary hospital) between August 2022 and April 2023 through convenience sampling. KAP scores ≥70% were considered adequate. Results This study enrolled 501 participants. The mean knowledge, attitude, and practice levels were 8.56 ± 1.81/12 (possible range 0-12, 71.33%), 29.80 ± 2.71 (possible range 8-40, 74.50%), and 32.04 ± 3.09 (possible range 8-40, 80.10%). Senior high school education (vs. junior high school and below, coefficient = 1.531, 95%CI: 1.013-2.312, p = 0.044), bachelor's education and above (vs. junior high school and below, coefficient = 5.315, 95%CI: 3.546-7.966, p < 0.001), housewife or unemployed (vs. employed, coefficient = 0.671, 95%CI: 0.466-0.966, p = 0.032), and a history of breast ultrasound (vs. no, coefficient = 1.466, 95%CI: 1.121-1.917, p = 0.005) were independently and positively associated with knowledge. Knowledge (coefficient = 1.303, 95%CI: 1.100-1.544, p = 0.002) and monthly income >10,000 (vs. <5,000, coefficient = 4.364, 95%CI: 1.738-10.956, p = 0.002) were independently and positively associated with attitude. Only attitude (coefficient = 1.212, 95%CI: 1.096-1.340, p < 0.001) was independently and positively associated with the practice. A structural equation modeling (SEM) analysis was used to estimate causality among KAP dimensions, showing that knowledge directly influenced attitude (β = -1.090, p = 0.015), knowledge did not directly influence practice (β = -0.117, p = 0.681) but had an indirect influence (β = 0.826, p = 0.028), and attitude directly influenced practice (β = -0.757, p = 0.016). Conclusion Women in Zhongshan City had good knowledge, favorable attitudes, and active practice toward breast ultrasound screening for breast cancer. Women's characteristics associated with a poorer KAP were identified, allowing for more targeted interventions.
Collapse
Affiliation(s)
- Shaozhong Liu
- Department of Ultrasound Imaging, Zhongshan City People’s Hospital, Zhongshan, China
| | - Shukai Zheng
- Department of Breast Surgery, Zhongshan City People’s Hospital, Zhongshan, China
| | - Mengzhen Qin
- Department of Ultrasound Imaging, Zhongshan City People’s Hospital, Zhongshan, China
| | - Yifeng Xie
- Department of Ultrasound Imaging, Zhongshan City People’s Hospital, Zhongshan, China
| | - Kun Yang
- Department of Ultrasound Imaging, Zhongshan City People’s Hospital, Zhongshan, China
| | - Xiaozhen Liu
- Department of Ultrasound Imaging, Zhongshan City People’s Hospital, Zhongshan, China
| |
Collapse
|
5
|
Ma S, Li Y, Yin J, Niu Q, An Z, Du L, Li F, Gu J. Prospective study of AI-assisted prediction of breast malignancies in physical health examinations: role of off-the-shelf AI software and comparison to radiologist performance. Front Oncol 2024; 14:1374278. [PMID: 38756651 PMCID: PMC11096442 DOI: 10.3389/fonc.2024.1374278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2024] [Accepted: 04/19/2024] [Indexed: 05/18/2024] Open
Abstract
Objective In physical health examinations, breast sonography is a commonly used imaging method, but it can lead to repeated exams and unnecessary biopsy due to discrepancies among radiologists and health centers. This study explores the role of off-the-shelf artificial intelligence (AI) software in assisting radiologists to classify incidentally found breast masses in two health centers. Methods Female patients undergoing breast ultrasound examinations with incidentally discovered breast masses were categorized according to the 5th edition of the Breast Imaging Reporting and Data System (BI-RADS), with categories 3 to 5 included in this study. The examinations were conducted at two municipal health centers from May 2021 to May 2023.The final pathological results from surgical resection or biopsy served as the gold standard for comparison. Ultrasonographic images were obtained in longitudinal and transverse sections, and two junior radiologists and one senior radiologist independently assessed the images without knowing the pathological findings. The BI-RADS classification was adjusted following AI assistance, and diagnostic performance was compared using receiver operating characteristic curves. Results A total of 196 patients with 202 breast masses were included in the study, with pathological results confirming 107 benign and 95 malignant masses. The receiver operating characteristic curve showed that experienced breast radiologists had higher diagnostic performance in BI-RADS classification than junior radiologists, similar to AI classification (AUC = 0.936, 0.806, 0.896, and 0.950, p < 0.05). The AI software improved the accuracy, sensitivity, and negative predictive value of the adjusted BI-RADS classification for the junior radiologists' group (p< 0.05), while no difference was observed in the senior radiologist group. Furthermore, AI increased the negative predictive value for BI-RADS 4a masses and the positive predictive value for 4b masses among radiologists (p < 0.05). AI enhances the sensitivity of invasive breast cancer detection more effectively than ductal carcinoma in situ and rare subtypes of breast cancer. Conclusions The AI software enhances diagnostic efficiency for breast masses, reducing the performance gap between junior and senior radiologists, particularly for BI-RADS 4a and 4b masses. This improvement reduces unnecessary repeat examinations and biopsies, optimizing medical resource utilization and enhancing overall diagnostic effectiveness.
Collapse
Affiliation(s)
- Sai Ma
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yanfang Li
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jun Yin
- Department of Ultrasound, Shanghai Fourth People’s Hospital, Shanghai, China
| | - Qinghua Niu
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zichen An
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lianfang Du
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Fan Li
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jiying Gu
- Department of Ultrasound, Shidong Hospital, Yangpu District, Shanghai, China
| |
Collapse
|
6
|
Mizzi D, Allely CS, Zarb F, Mercer CE. Implementing supplementary breast cancer screening in women with dense breasts: Insights from European radiographers and radiologists. Radiography (Lond) 2024; 30:908-919. [PMID: 38615593 DOI: 10.1016/j.radi.2024.04.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 03/28/2024] [Accepted: 04/02/2024] [Indexed: 04/16/2024]
Abstract
INTRODUCTION In response to the critical need for enhancing breast cancer screening for women with dense breasts, this study explored the understanding of challenges and requirements for implementing supplementary breast cancer screening for such women among clinical radiographers and radiologists in Europe. METHOD Fourteen (14) semi-structured online interviews were conducted with European clinical radiologists (n = 5) and radiographers (n = 9) specializing in breast cancer screening from 8 different countries: Denmark, Finland, Greece, Italy, Malta, the Netherlands, Switzerland, United Kingdom. The interview schedule comprised questions regarding professional background and demographics and 13 key questions divided into six subgroups, namely Supplementary Imaging, Training, Resources and Guidelines, Challenges, Implementing supplementary screening and Women's Perspective. Data analysis followed the six phases of reflexive thematic analysis. RESULTS Six significant themes emerged from the data analysis: Understanding and experiences of supplementary imaging for women with dense breasts; Challenges and requirements related to training among clinical radiographers and radiologists; Awareness among radiographers and radiologists of guidelines on imaging women with dense breasts; Challenges to implement supplementary screening; Predictors of Implementing Supplementary screening; Views of radiologists and radiographers on women's perception towards supplementary screening. CONCLUSION The interviews with radiographers and radiologists provided valuable insights into the challenges and potential strategies for implementing supplementary breast cancer screening. These challenges included patient and staff related challenges. Implementing multifaceted solutions such as Artificial Intelligence integration, specialized training and resource investment can address these challenges and promote the successful implementation of supplementary screening. Further research and collaboration are needed to refine and implement these strategies effectively. IMPLICATIONS FOR PRACTICE This study highlights the urgent need for specialized training programs and dedicated resources to enhance supplementary breast cancer screening for women with dense breasts in Europe. These resources include advanced imaging technologies, such as MRI or ultrasound, and specialized software for image analysis. Moreover, further research is imperative to refine screening protocols and evaluate their efficacy and cost-effectiveness, based on the findings of this study.
Collapse
Affiliation(s)
- D Mizzi
- Department of Radiography, Faculty of Health Sciences, University of Malta, Msida, MSD 2080, Malta.
| | - C S Allely
- School of Health and Society, University of Salford, Manchester, M5 4WT, United Kingdom.
| | - F Zarb
- Department of Radiography, Faculty of Health Sciences, University of Malta, Msida, MSD 2080, Malta.
| | - C E Mercer
- School of Health and Society, University of Salford, Manchester, M5 4WT, United Kingdom.
| |
Collapse
|
7
|
Iacob R, Iacob ER, Stoicescu ER, Ghenciu DM, Cocolea DM, Constantinescu A, Ghenciu LA, Manolescu DL. Evaluating the Role of Breast Ultrasound in Early Detection of Breast Cancer in Low- and Middle-Income Countries: A Comprehensive Narrative Review. Bioengineering (Basel) 2024; 11:262. [PMID: 38534536 DOI: 10.3390/bioengineering11030262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Revised: 03/04/2024] [Accepted: 03/06/2024] [Indexed: 03/28/2024] Open
Abstract
Breast cancer, affecting both genders, but mostly females, exhibits shifting demographic patterns, with an increasing incidence in younger age groups. Early identification through mammography, clinical examinations, and breast self-exams enhances treatment efficacy, but challenges persist in low- and medium-income countries due to limited imaging resources. This review assesses the feasibility of employing breast ultrasound as the primary breast cancer screening method, particularly in resource-constrained regions. Following the PRISMA guidelines, this study examines 52 publications from the last five years. Breast ultrasound, distinct from mammography, offers advantages like radiation-free imaging, suitability for repeated screenings, and preference for younger populations. Real-time imaging and dense breast tissue evaluation enhance sensitivity, accessibility, and cost-effectiveness. However, limitations include reduced specificity, operator dependence, and challenges in detecting microcalcifications. Automatic breast ultrasound (ABUS) addresses some issues but faces constraints like potential inaccuracies and limited microcalcification detection. The analysis underscores the need for a comprehensive approach to breast cancer screening, emphasizing international collaboration and addressing limitations, especially in resource-constrained settings. Despite advancements, notably with ABUS, the primary goal is to contribute insights for optimizing breast cancer screening globally, improving outcomes, and mitigating the impact of this debilitating disease.
Collapse
Affiliation(s)
- Roxana Iacob
- Department of Anatomy and Embriology, 'Victor Babeș' University of Medicine and Pharmacy, 300041 Timișoara, Romania
- Doctoral School, 'Victor Babeș' University of Medicine and Pharmacy, 300041 Timișoara, Romania
- Faculty of Mechanics, Field of Applied Engineering Sciences, Specialization Statistical Methods and Techniques in Health and Clinical Research, 'Politehnica' University Timișoara, Mihai Viteazul Boulevard No. 1, 300222 Timisoara, Romania
| | - Emil Radu Iacob
- Department of Pediatric Surgery, 'Victor Babeș' University of Medicine and Pharmacy, 300041 Timișoara, Romania
| | - Emil Robert Stoicescu
- Doctoral School, 'Victor Babeș' University of Medicine and Pharmacy, 300041 Timișoara, Romania
- Faculty of Mechanics, Field of Applied Engineering Sciences, Specialization Statistical Methods and Techniques in Health and Clinical Research, 'Politehnica' University Timișoara, Mihai Viteazul Boulevard No. 1, 300222 Timisoara, Romania
- Department of Radiology and Medical Imaging, 'Victor Babeș' University of Medicine and Pharmacy, 300041 Timișoara, Romania
- Research Center for Pharmaco-Toxicological Evaluations, 'Victor Babeș' University of Medicine and Pharmacy, 300041 Timișoara, Romania
| | - Delius Mario Ghenciu
- Doctoral School, 'Victor Babeș' University of Medicine and Pharmacy, 300041 Timișoara, Romania
| | - Daiana Marina Cocolea
- Doctoral School, 'Victor Babeș' University of Medicine and Pharmacy, 300041 Timișoara, Romania
| | - Amalia Constantinescu
- Department of Radiology and Medical Imaging, 'Victor Babeș' University of Medicine and Pharmacy, 300041 Timișoara, Romania
| | - Laura Andreea Ghenciu
- Discipline of Pathophysiology, 'Victor Babeș' University of Medicine and Pharmacy, 300041 Timișoara, Romania
| | - Diana Luminita Manolescu
- Department of Radiology and Medical Imaging, 'Victor Babeș' University of Medicine and Pharmacy, 300041 Timișoara, Romania
- Center for Research and Innovation in Precision Medicine of Respiratory Diseases (CRIPMRD), 'Victor Babeș' University of Medicine and Pharmacy, 300041 Timișoara, Romania
| |
Collapse
|
8
|
Obeagu EI, Obeagu GU. Breast cancer: A review of risk factors and diagnosis. Medicine (Baltimore) 2024; 103:e36905. [PMID: 38241592 PMCID: PMC10798762 DOI: 10.1097/md.0000000000036905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Accepted: 12/18/2023] [Indexed: 01/21/2024] Open
Abstract
Breast cancer remains a complex and prevalent health concern affecting millions of individuals worldwide. This review paper presents a comprehensive analysis of the multifaceted landscape of breast cancer, elucidating the diverse spectrum of risk factors contributing to its occurrence and exploring advancements in diagnostic methodologies. Through an extensive examination of current literature, various risk factors have been identified, encompassing genetic predispositions such as BRCA mutations, hormonal influences, lifestyle factors, and reproductive patterns. Age, family history, and environmental factors further contribute to the intricate tapestry of breast cancer etiology. Moreover, this review delineates the pivotal role of diagnostic tools in the early detection and management of breast cancer. Mammography, the cornerstone of breast cancer screening, is augmented by emerging technologies like magnetic resonance imaging and molecular testing, enabling improved sensitivity and specificity in diagnosing breast malignancies. Despite these advancements, challenges persist in ensuring widespread accessibility to screening programs, particularly in resource-limited settings. In conclusion, this review underscores the importance of understanding diverse risk factors in the development of breast cancer and emphasizes the critical role of evolving diagnostic modalities in enhancing early detection. The synthesis of current knowledge in this review aims to contribute to a deeper comprehension of breast cancer's multifactorial nature and inform future directions in research, screening strategies, and preventive interventions.
Collapse
|
9
|
Gatta G, Somma F, Sardu C, De Chiara M, Massafra R, Fanizzi A, La Forgia D, Cuccurullo V, Iovino F, Clemente A, Marfella R, Grezia GD. Automated 3D Ultrasound as an Adjunct to Screening Mammography Programs in Dense Breast: Literature Review and Metanalysis. J Pers Med 2023; 13:1683. [PMID: 38138910 PMCID: PMC10744838 DOI: 10.3390/jpm13121683] [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: 09/13/2023] [Revised: 11/10/2023] [Accepted: 11/23/2023] [Indexed: 12/24/2023] Open
Abstract
Purpose: The purpose of this meta-analysis is to investigate the effectiveness of supplementing screening mammography with three-dimensional automated breast ultrasonography (3D ABUS) in improving breast cancer detection rates in asymptomatic women with dense breasts. Materials and Methods: We conducted a thorough review of scientific publications comparing 3D ABUS and mammography. Articles for inclusion were sourced from peer-reviewed journal databases, namely MEDLINE (PubMed) and Scopus, based on an initial screening of their titles and abstracts. To ensure a sufficient sample size for meaningful analysis, only studies evaluating a minimum of 20 patients were retained. Eligibility for evaluation was further limited to articles written in English. Additionally, selected studies were required to have participants aged 18 or above at the time of the study. We analyzed 25 studies published between 2000 and 2021, which included a total of 31,549 women with dense breasts. Among these women, 229 underwent mammography alone, while 347 underwent mammography in combination with 3D ABUS. The average age of the women was 50.86 years (±10 years standard deviation), with a range of 40-56 years. In our efforts to address and reduce bias, we applied a range of statistical analyses. These included assessing study variation through heterogeneity assessment, accounting for potential study variability using a random-effects model, exploring sources of bias via meta-regression analysis, and checking for publication bias through funnel plots and the Egger test. These methods ensured the reliability of our study findings. Results: According to the 25 studies included in this metanalysis, out of the total number of women, 27,495 were diagnosed with breast cancer. Of these, 211 were diagnosed through mammography alone, while an additional 329 women were diagnosed through the combination of full-field digital mammography (FFDSM) and 3D ABUS. This represents an increase of 51.5%. The rate of cancers detected per 1000 women screened was 23.25‱ (95% confidence interval [CI]: 21.20, 25.60; p < 0.001) with mammography alone. In contrast, the addition of 3D ABUS to mammography increased the number of tumors detected to 20.95‱ (95% confidence interval [CI]: 18.50, 23; p < 0.001) per 1000 women screened. Discussion: Even though variability in study results, lack of long-term outcomes, and selection bias may be present, this systematic review and meta-analysis confirms that supplementing mammography with 3D ABUS increases the accuracy of breast cancer detection in women with ACR3 to ACR4 breasts. Our findings suggest that the combination of mammography and 3D ABUS should be considered for screening women with dense breasts. Conclusions: Our research confirms that adding 3D automated breast ultrasound to mammography-only screening in patients with dense breasts (ACR3 and ACR4) significantly (p < 0.05) increases the cancer detection rate.
Collapse
Affiliation(s)
- Gianluca Gatta
- Department of Precision Medicine, Università della Campania “Luigi Vanvitelli”, 80131 Napoli, Italy; (G.G.); (M.D.C.); (A.C.)
| | - Francesco Somma
- U.O.C. Neurodiology, ASL Center Naples 1, P.O. Ospedale del Mare, 80147 Naples, Italy;
| | - Celestino Sardu
- Department of Advanced Medical and Surgical Sciences, University of Campania Luigi Vanvitelli, Piazza Luigi Miraglia 2, 80138 Naples, Italy; (C.S.); (R.M.)
| | - Marco De Chiara
- Department of Precision Medicine, Università della Campania “Luigi Vanvitelli”, 80131 Napoli, Italy; (G.G.); (M.D.C.); (A.C.)
| | - Raffaella Massafra
- Department of Breast Radiology, Giovanni Paolo II/I.R.C.C.S. Cancer Institute, 70124 Bari, Italy; (R.M.); (A.F.); (D.L.F.)
| | - Annarita Fanizzi
- Department of Breast Radiology, Giovanni Paolo II/I.R.C.C.S. Cancer Institute, 70124 Bari, Italy; (R.M.); (A.F.); (D.L.F.)
| | - Daniele La Forgia
- Department of Breast Radiology, Giovanni Paolo II/I.R.C.C.S. Cancer Institute, 70124 Bari, Italy; (R.M.); (A.F.); (D.L.F.)
| | - Vincenzo Cuccurullo
- Department of Precision Medicine, Università della Campania “Luigi Vanvitelli”, 80131 Napoli, Italy; (G.G.); (M.D.C.); (A.C.)
| | - Francesco Iovino
- Department of Translational Medical Science, School of Medicine, University of Campania Luigi Vanvitelli, 80138 Naples, Italy;
| | - Alfredo Clemente
- Department of Precision Medicine, Università della Campania “Luigi Vanvitelli”, 80131 Napoli, Italy; (G.G.); (M.D.C.); (A.C.)
| | - Raffaele Marfella
- Department of Advanced Medical and Surgical Sciences, University of Campania Luigi Vanvitelli, Piazza Luigi Miraglia 2, 80138 Naples, Italy; (C.S.); (R.M.)
| | | |
Collapse
|
10
|
Gegios AR, Peterson MS, Fowler AM. Breast Cancer Screening and Diagnosis: Recent Advances in Imaging and Current Limitations. PET Clin 2023; 18:459-471. [PMID: 37296043 DOI: 10.1016/j.cpet.2023.04.003] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Breast cancer detection has a significant impact on population health. Although there are many breast imaging modalities, mammography is the predominant tool for breast cancer screening. The introduction of digital breast tomosynthesis to mammography has contributed to increased cancer detection rates and decreased recall rates. In average-risk women, starting annual screening mammography at age 40 years has demonstrated the highest mortality reduction. In intermediate- and high-risk women as well as in those with dense breasts, additional modalities, including MRI, ultrasound, and molecular breast imaging, can also be considered for adjunct screening to improve the detection of mammographically occult malignancy.
Collapse
Affiliation(s)
- Alison R Gegios
- Section of Breast Imaging and Intervention, Department of Radiology, University of Wisconsin School of Medicine and Public Health, 600 Highland Avenue, Madison, WI 53792-3252, USA
| | - Molly S Peterson
- Section of Breast Imaging and Intervention, Department of Radiology, University of Wisconsin School of Medicine and Public Health, 600 Highland Avenue, Madison, WI 53792-3252, USA
| | - Amy M Fowler
- Section of Breast Imaging and Intervention, Department of Radiology, University of Wisconsin School of Medicine and Public Health, 600 Highland Avenue, Madison, WI 53792-3252, USA; University of Wisconsin Carbone Cancer Center, Madison, WI, USA; Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, USA.
| |
Collapse
|
11
|
Brown AL, Vijapura C, Patel M, De La Cruz A, Wahab R. Breast Cancer in Dense Breasts: Detection Challenges and Supplemental Screening Opportunities. Radiographics 2023; 43:e230024. [PMID: 37792590 DOI: 10.1148/rg.230024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/06/2023]
Abstract
Dense breast tissue at mammography is associated with higher breast cancer incidence and mortality rates, which have prompted new considerations for breast cancer screening in women with dense breasts. The authors review the definition and classification of breast density, density assessment methods, breast cancer risk, current legislation, and future efforts and summarize trials and key studies that have affected the existing guidelines for supplemental screening. Cases of breast cancer in dense breasts are presented, highlighting a variety of modalities and specific imaging findings that can aid in cancer detection and staging. Understanding the current state of breast cancer screening in patients with dense breasts and its challenges is important to shape future considerations for care. Shifting the paradigm of breast cancer detection toward early diagnosis for women with dense breasts may be the answer to reducing the number of deaths from this common disease. ©RSNA, 2023 Online supplemental material is available for this article. Quiz questions for this article are available through the Online Learning Center. See the invited commentary by Yeh in this issue.
Collapse
Affiliation(s)
- Ann L Brown
- From the Department of Radiology, University of Cincinnati Medical Center, 3188 Bellevue Ave, Cincinnati, OH 45219-0772 (A.L.B., C.V., A.D.L.C., R.W.); and Department of Radiology, Ohio State University Medical Center, Columbus, Ohio (M.P.)
| | - Charmi Vijapura
- From the Department of Radiology, University of Cincinnati Medical Center, 3188 Bellevue Ave, Cincinnati, OH 45219-0772 (A.L.B., C.V., A.D.L.C., R.W.); and Department of Radiology, Ohio State University Medical Center, Columbus, Ohio (M.P.)
| | - Mitva Patel
- From the Department of Radiology, University of Cincinnati Medical Center, 3188 Bellevue Ave, Cincinnati, OH 45219-0772 (A.L.B., C.V., A.D.L.C., R.W.); and Department of Radiology, Ohio State University Medical Center, Columbus, Ohio (M.P.)
| | - Alexis De La Cruz
- From the Department of Radiology, University of Cincinnati Medical Center, 3188 Bellevue Ave, Cincinnati, OH 45219-0772 (A.L.B., C.V., A.D.L.C., R.W.); and Department of Radiology, Ohio State University Medical Center, Columbus, Ohio (M.P.)
| | - Rifat Wahab
- From the Department of Radiology, University of Cincinnati Medical Center, 3188 Bellevue Ave, Cincinnati, OH 45219-0772 (A.L.B., C.V., A.D.L.C., R.W.); and Department of Radiology, Ohio State University Medical Center, Columbus, Ohio (M.P.)
| |
Collapse
|
12
|
Morozova A, Cotes C, Aran S, Singh H. Challenges in Interpretation of US Breast Findings in the Emergency Setting. Radiographics 2023; 43:e230020. [PMID: 37733621 DOI: 10.1148/rg.230020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/23/2023]
Abstract
Emergencies in breast imaging are infrequent but not rare. Although infectious conditions such as mastitis and breast abscess are the most common breast diseases encountered in acute care settings, other entities that may require additional imaging or different treatment approaches are also seen and include traumatic injury and breast cancer. While mammography is widely available for breast evaluation in outpatient facilities, most emergency departments do not have mammography units. This makes evaluation of patients with breast disease incomplete in the acute care setting and emphasizes the role of appropriate US techniques for interpretation. It also highlights the importance of effective sonographer-to-radiologist communication to ensure patient safety and diagnostic accuracy, especially in an era of increasing adoption of teleradiology. The authors discuss the challenges in image acquisition and remote interpretation that are commonly faced by radiologists when they assess breast anomalies in the emergency setting. They present strategies to overcome these challenges by describing techniques for proper US evaluation, highlighting the importance of sonographer-radiologist communication, defining the goals of the evaluation, reviewing common differential diagnoses, and providing appropriate follow-up recommendations. ©RSNA, 2023 Quiz questions for this article are available in the supplemental material.
Collapse
Affiliation(s)
- Anastasiia Morozova
- From the Department of Radiology, University of Texas Health Science Center at Houston, 6431 Fannin St, Suite 2.010, Houston, TX 77030
| | - Claudia Cotes
- From the Department of Radiology, University of Texas Health Science Center at Houston, 6431 Fannin St, Suite 2.010, Houston, TX 77030
| | - Shima Aran
- From the Department of Radiology, University of Texas Health Science Center at Houston, 6431 Fannin St, Suite 2.010, Houston, TX 77030
| | - Harnoor Singh
- From the Department of Radiology, University of Texas Health Science Center at Houston, 6431 Fannin St, Suite 2.010, Houston, TX 77030
| |
Collapse
|
13
|
Park CKS, Trumpour T, Aziz A, Bax JS, Tessier D, Gardi L, Fenster A. Cost-effective, portable, patient-dedicated three-dimensional automated breast ultrasound for point-of-care breast cancer screening. Sci Rep 2023; 13:14390. [PMID: 37658125 PMCID: PMC10474273 DOI: 10.1038/s41598-023-41424-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 08/26/2023] [Indexed: 09/03/2023] Open
Abstract
Breast cancer screening has substantially reduced mortality across screening populations. However, a clinical need persists for more accessible, cost-effective, and robust approaches for increased-risk and diverse patient populations, especially those with dense breasts where screening mammography is suboptimal. We developed and validated a cost-effective, portable, patient-dedicated three-dimensional (3D) automated breast ultrasound (ABUS) system for point-of-care breast cancer screening. The 3D ABUS system contains a wearable, rapid-prototype 3D-printed dam assembly, a compression assembly, and a computer-driven 3DUS scanner, adaptable to any commercially available US machine and transducer. Acquisition is operator-agnostic, involves a 40-second scan time, and provides multiplanar 3D visualization for whole-breast assessment. Geometric reconstruction accuracy was evaluated with a 3D grid phantom and tissue-mimicking breast phantoms, demonstrating linear measurement and volumetric reconstruction errors < 0.2 mm and < 3%, respectively. The system's capability was demonstrated in a healthy male volunteer and two healthy female volunteers, representing diverse patient geometries and breast sizes. The system enables comfortable ultrasonic coupling and tissue stabilization, with adjustable compression to improve image quality while alleviating discomfort. Moreover, the system effectively mitigates breathing and motion, since its assembly affixes directly onto the patient. While future studies are still required to evaluate the impact on current clinical practices and workflow, the 3D ABUS system shows potential for adoption as an alternative, cost-effective, dedicated point-of-care breast cancer screening approach for increased-risk populations and limited-resource settings.
Collapse
Affiliation(s)
- Claire Keun Sun Park
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, ON, N6A 3K7, Canada.
- Robarts Research Institute, 1151 Richmond St. N., London, ON, N6A 5B7, Canada.
| | - Tiana Trumpour
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, ON, N6A 3K7, Canada
- Robarts Research Institute, 1151 Richmond St. N., London, ON, N6A 5B7, Canada
| | - Amal Aziz
- Robarts Research Institute, 1151 Richmond St. N., London, ON, N6A 5B7, Canada
- School of Biomedical Engineering, Faculty of Engineering, Western University, London, ON, N6A 3K7, Canada
| | - Jeffrey Scott Bax
- Robarts Research Institute, 1151 Richmond St. N., London, ON, N6A 5B7, Canada
| | - David Tessier
- Robarts Research Institute, 1151 Richmond St. N., London, ON, N6A 5B7, Canada
| | - Lori Gardi
- Robarts Research Institute, 1151 Richmond St. N., London, ON, N6A 5B7, Canada
| | - Aaron Fenster
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, ON, N6A 3K7, Canada
- Robarts Research Institute, 1151 Richmond St. N., London, ON, N6A 5B7, Canada
- Division of Imaging Sciences, Department of Medical Imaging, Schulich School of Medicine and Dentistry, Western University, London, ON, N6A 3K7, Canada
| |
Collapse
|
14
|
Santos R, Ribeiro AR, Marques D. Ultrasound as a Method for Early Diagnosis of Breast Pathology. J Pers Med 2023; 13:1156. [PMID: 37511769 PMCID: PMC10381720 DOI: 10.3390/jpm13071156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 07/04/2023] [Accepted: 07/11/2023] [Indexed: 07/30/2023] Open
Abstract
INTRODUCTION Ultrasound is a non-invasive, low-cost technique that does not use ionising radiation and provides a "real-time" image, and for these reasons, this method is ideal in several situations. PURPOSE To demonstrate breast ultrasound evaluation as a first-line diagnostic method and to evaluate the variation of breast characteristics with age. MATERIAL AND METHODS A total of 105 women with a mean age of 30 years participated and were divided into three age groups: 18-39, 40-59, and 60-79 years, excluding participants subject to mastectomy. After completing the informed consent, all participants answered personal and sociodemographic questions, such as personal and family history, menstrual cycle, pregnancy, ultrasound, and mammography, among others. They were then submitted to a bilateral breast ultrasound examination. Subsequently, all the images and their data were analysed, and a technical report of the examination was given to all the participants. RESULTS A total of 105 women with a mean age of 30 years participated, 58 of whom underwent the examination for the first time. In 31, changes (of which only 7 were known) were diagnosed. It was verified that, according to age group, the density of the breast stroma varied; older women have less breast density. CONCLUSIONS Ultrasound is a good method for breast evaluation and can be considered important for the early evaluation of breast pathology and follow-up of the pathology.
Collapse
Affiliation(s)
- Rute Santos
- Coimbra Health School, Polytechnic University of Coimbra, 3046-854 Coimbra, Portugal
- Laboratory for Applied Health Research (LabinSaúde), 3046-854 Coimbra, Portugal
| | - Ana Raquel Ribeiro
- Radiotherapy Department, Coimbra Hospital and University Center, 3004-561 Coimbra, Portugal
| | - Daniela Marques
- Joaquim Chaves Oncologia, S.A., 2790-225 Carnaxide, Portugal
| |
Collapse
|
15
|
Trepanier C, Huang A, Liu M, Ha R. Emerging uses of artificial intelligence in breast and axillary ultrasound. Clin Imaging 2023; 100:64-68. [PMID: 37243994 DOI: 10.1016/j.clinimag.2023.05.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 05/02/2023] [Indexed: 05/29/2023]
Abstract
Breast ultrasound is a valuable adjunctive tool to mammography in detecting breast cancer, especially in women with dense breasts. Ultrasound also plays an important role in staging breast cancer by assessing axillary lymph nodes. However, its utility is limited by operator dependence, high recall rate, low positive predictive value and low specificity. These limitations present an opportunity for artificial intelligence (AI) to improve diagnostic performance and pioneer novel uses of ultrasound. Research in developing AI for radiology has flourished over the past few years. A subset of AI, deep learning, uses interconnected computational nodes to form a neural network, which extracts complex visual features from image data to train itself into a predictive model. This review summarizes several key studies evaluating AI programs' performance in predicting breast cancer and demonstrates that AI can assist radiologists and address limitations of ultrasound by acting as a decision support tool. This review also touches on how AI programs allow for novel predictive uses of ultrasound, particularly predicting molecular subtypes of breast cancer and response to neoadjuvant chemotherapy, which have the potential to change how breast cancer is managed by providing non-invasive prognostic and treatment data from ultrasound images. Lastly, this review explores how AI programs demonstrate improved diagnostic accuracy in predicting axillary lymph node metastasis. The limitations and future challenges in developing and implementing AI for breast and axillary ultrasound will also be discussed.
Collapse
Affiliation(s)
- Christopher Trepanier
- Columbia University Irving Medical Center, 622 W 168th St, New York, NY 10032, United States of America.
| | - Alice Huang
- Columbia University Irving Medical Center, 622 W 168th St, New York, NY 10032, United States of America.
| | - Michael Liu
- Columbia University Irving Medical Center, 622 W 168th St, New York, NY 10032, United States of America.
| | - Richard Ha
- Columbia University Irving Medical Center, 622 W 168th St, New York, NY 10032, United States of America.
| |
Collapse
|
16
|
Dai C, Bao L, Yan H, Zhu L, Xu X, Tan Y, Yu L, Yang J, Jiang C, Shen Y. Efficiency and impact factors of anatomical intelligence for breast and hand-held ultrasound in lesion detection. JOURNAL OF CLINICAL ULTRASOUND : JCU 2023. [PMID: 37096417 DOI: 10.1002/jcu.23469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 03/20/2023] [Accepted: 04/07/2023] [Indexed: 05/03/2023]
Abstract
PURPOSE To investigate the efficiency and impact factors of anatomical intelligence for breast (AI-Breast) and hand-held ultrasound (HHUS) in lesion detection. METHODS A total of 172 outpatient women were randomly selected, underwent AI-Breast ultrasound (Group AI) once and HHUS twice. HHUS was performed by breast imaging radiologists (Group A) and general radiologists (Group B). For the AI-Breast examination, a trained technician performed the whole-breast scan and data acquisition, while other general radiologists performed image interpretation. The examination time and lesion detection rate were recorded. The impact factors for breast lesion detection, including breast cup size, number of lesions, and benign or malignant lesions were analyzed. RESULTS The detection rates of Group AI, A, and B were 92.8 ± 17.0%, 95.0 ± 13.6%, and 85.0 ± 22.9%, respectively. Comparable lesion detection rates were observed in Group AI and Group A (P > 0.05), but a significantly lower lesion detection rate was observed in Group B compared to the other two (both P < 0.05). Regarding missed diagnosis rates of malignant lesions, comparable performance was observed in Group AI, Group A, and Group B (8% vs. 4% vs. 14%, all P > 0.05). Scan times of Groups AI, A, and B were 262.15 ± 40.4 s, 237.5 ± 110.3 s, 281.2 ± 86.1 s, respectively. The scan time of Group AI was significantly higher than Group A (P < 0.01), but was slightly lower than Group B (P > 0.05). We found a strong linear correlation between scan time and cup size in Group AI (r = 0.745). No impacts of cup size and number of lesions were found on the lesion detection rate in Group AI (P > 0.05). CONCLUSIONS With the assist of AI-Breast system, the lesion detection rate of AI-Breast ultrasound was comparable to that of a breast imaging radiologist and superior to that of the general radiologist. AI-Breast ultrasound may be used as a potential approach for breast lesions surveillance.
Collapse
Affiliation(s)
- Chaochao Dai
- Department of Ultrasonography, Affiliated Hangzhou First People's Hosptital, Zhejiang University, School of Medicine, Zhejiang, Hangzhou, China
| | - Lingyun Bao
- Department of Ultrasonography, Affiliated Hangzhou First People's Hosptital, Zhejiang University, School of Medicine, Zhejiang, Hangzhou, China
| | - Hongju Yan
- Department of Ultrasonography, Affiliated Hangzhou First People's Hosptital, Zhejiang University, School of Medicine, Zhejiang, Hangzhou, China
| | - Luoxi Zhu
- Department of Ultrasonography, Affiliated Hangzhou First People's Hosptital, Zhejiang University, School of Medicine, Zhejiang, Hangzhou, China
| | - Xiaojing Xu
- Department of Ultrasonography, Affiliated Hangzhou First People's Hosptital, Zhejiang University, School of Medicine, Zhejiang, Hangzhou, China
| | - Yanjuan Tan
- Department of Ultrasonography, Affiliated Hangzhou First People's Hosptital, Zhejiang University, School of Medicine, Zhejiang, Hangzhou, China
| | - Lifang Yu
- Department of Ultrasonography, Affiliated Hangzhou First People's Hosptital, Zhejiang University, School of Medicine, Zhejiang, Hangzhou, China
| | - Jing Yang
- Department of Ultrasonography, Affiliated Hangzhou First People's Hosptital, Zhejiang University, School of Medicine, Zhejiang, Hangzhou, China
| | - Chenxiang Jiang
- Department of Ultrasonography, Affiliated Hangzhou First People's Hosptital, Zhejiang University, School of Medicine, Zhejiang, Hangzhou, China
| | - Yingzhao Shen
- Department of Ultrasonography, Affiliated Hangzhou First People's Hosptital, Zhejiang University, School of Medicine, Zhejiang, Hangzhou, China
| |
Collapse
|
17
|
Lobig F, Caleyachetty A, Forrester L, Morris E, Newstead G, Harris J, Blankenburg M. Performance of Supplemental Imaging Modalities for Breast Cancer in Women With Dense Breasts: Findings From an Umbrella Review and Primary Studies Analysis. Clin Breast Cancer 2023:S1526-8209(23)00088-5. [PMID: 37202338 DOI: 10.1016/j.clbc.2023.04.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 02/28/2023] [Accepted: 04/14/2023] [Indexed: 05/20/2023]
Abstract
Breast cancer screening performance of supplemental imaging modalities by breast density and breast cancer risk has not been widely studied, and the optimal choice of modality for women with dense breasts remains unclear in clinical practice and guidelines. This systematic review aimed to assess breast cancer screening performance of supplemental imaging modalities for women with dense breasts, by breast cancer risk. Systematic reviews (SRs) in 2000 to 2021, and primary studies in 2019 to 2021, on outcomes of supplemental screening modalities (digital breast tomography [DBT], MRI (full/abbreviated protocol), contrast enhanced mammography (CEM), ultrasound (hand-held [HHUS]/automated [ABUS]) in women with dense breasts (BI-RADS C&D) were identified. None of the SRs analyzed outcomes by cancer risk. Meta-analysis of the primary studies was not feasible due to lack of studies (MRI, CEM, DBT) or methodological heterogeneity (ultrasound); therefore, findings were summarized narratively. For average risk, a single MRI trial reported a superior screening performance (higher cancer detection rate [CDR] and lower interval cancer rate [ICR]) compared to HHUS, ABUS and DBT. For intermediate risk, ultrasound was the only modality assessed, but accuracy estimates ranged widely. For mixed risk, a single CEM study reported the highest CDR, but included a high proportion of women with intermediate risk. This systematic review does not allow a complete comparison of supplemental screening modalities for dense breast populations by breast cancer risk. However, the data suggest that MRI and CEM might generally offer superior screening performance versus other modalities. Further studies of screening modalities are urgently required.
Collapse
Affiliation(s)
| | | | | | - Elizabeth Morris
- University of California Davis, Department of Radiology, Sacramento, CA 95817, USA
| | | | | | | |
Collapse
|
18
|
Dan Q, Zheng T, Liu L, Sun D, Chen Y. Ultrasound for Breast Cancer Screening in Resource-Limited Settings: Current Practice and Future Directions. Cancers (Basel) 2023; 15:cancers15072112. [PMID: 37046773 PMCID: PMC10093585 DOI: 10.3390/cancers15072112] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 03/09/2023] [Accepted: 03/30/2023] [Indexed: 04/05/2023] Open
Abstract
Breast cancer (BC) is the most prevalent cancer among women globally. Cancer screening can reduce mortality and improve women’s health. In developed countries, mammography (MAM) has been primarily utilized for population-based BC screening for several decades. However, it is usually unavailable in low-resource settings due to the lack of equipment, personnel, and time necessary to conduct and interpret the examinations. Ultrasound (US) with high detection sensitivity for women of younger ages and with dense breasts has become a supplement to MAM for breast examination. Some guidelines suggest using US as the primary screening tool in certain settings where MAM is unavailable and infeasible, but global recommendations have not yet reached a unanimous consensus. With the development of smart devices and artificial intelligence (AI) in medical imaging, clinical applications and preclinical studies have shown the potential of US combined with AI in BC screening. Nevertheless, there are few comprehensive reviews focused on the role of US in screening BC in underserved conditions, especially in technological, economical, and global perspectives. This work presents the benefits, limitations, advances, and future directions of BC screening with technology-assisted and resource-appropriate strategies, which may be helpful to implement screening initiatives in resource-limited countries.
Collapse
Affiliation(s)
- Qing Dan
- Department of Ultrasound, Peking University Shenzhen Hospital, Shenzhen Peking University-The Hong Kong University of Science and Technology Medical Center, Shenzhen 518036, China
| | - Tingting Zheng
- Department of Ultrasound, Peking University Shenzhen Hospital, Shenzhen Peking University-The Hong Kong University of Science and Technology Medical Center, Shenzhen 518036, China
| | - Li Liu
- Department of Ultrasound, Peking University Shenzhen Hospital, Shenzhen Peking University-The Hong Kong University of Science and Technology Medical Center, Shenzhen 518036, China
| | - Desheng Sun
- Department of Ultrasound, Peking University Shenzhen Hospital, Shenzhen Peking University-The Hong Kong University of Science and Technology Medical Center, Shenzhen 518036, China
| | - Yun Chen
- Department of Ultrasound, Peking University Shenzhen Hospital, Shenzhen Peking University-The Hong Kong University of Science and Technology Medical Center, Shenzhen 518036, China
| |
Collapse
|
19
|
Hanafy MM, Ahmed AAH, Ali EA. Mammographically detected asymmetries in the era of artificial intelligence. THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2023. [DOI: 10.1186/s43055-023-00979-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/02/2023] Open
Abstract
Abstract
Background
Proper assessment of mammographically detected asymmetries is essential to avoid unnecessary biopsies and missed cancers as they may be of a benign or malignant cause. According to ACR BIRADS atlas 2013, mammographically detected asymmetries are classified into asymmetry, focal asymmetry, global asymmetry, and developing asymmetry. We aimed to assess the diagnostic performance of artificial intelligence in mammographically detected asymmetries compared to breast ultrasound as well as combined mammography and ultrasound.
Results
This study was a prospective study that comprised 51 women with breast asymmetry found on screening as well as diagnostic mammography. All participants conducted full-field digital mammography and ultrasound. Then the obtained mammographic images were processed by the artificial intelligence software system. Mammography had a sensitivity of 100%, specificity of 73%, a positive predictive value of 56.52%, a negative predictive value of 100%, and diagnostic accuracy of 80%. The results of Ultrasound revealed a sensitivity of 100.00%, a specificity of 89.47%, a positive predictive value of 76.47%, a negative predictive value of 100.00%, and an accuracy of 92.16%. Combined mammography and breast ultrasound showed a sensitivity of 100.00%, a specificity of 86.84%, a positive predictive value of 72.22%, a negative predictive value of 100.00%, and an accuracy of 90.20%. Artificial intelligence results demonstrated a sensitivity of 84.62%, a specificity of 94.74%, a positive predictive value of 48.26%, a negative predictive value of 94.47%, and an accuracy of 92.16%.
Conclusions
Adding breast ultrasound in the assessment of mammographically detected asymmetries led to better characterization, so it reduced the false-positive results and improved the specificity. Also, Artificial intelligence showed better specificity compared to mammography, breast ultrasound, and combined Mammography and ultrasound, so AI can be used to decrease unnecessary biopsies as it increases confidence in diagnosis, especially in cases with no definite ultrasound suspicious abnormality.
Collapse
|
20
|
Blood-Based mRNA Tests as Emerging Diagnostic Tools for Personalised Medicine in Breast Cancer. Cancers (Basel) 2023; 15:cancers15041087. [PMID: 36831426 PMCID: PMC9954278 DOI: 10.3390/cancers15041087] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 02/03/2023] [Accepted: 02/06/2023] [Indexed: 02/10/2023] Open
Abstract
Molecular diagnostic tests help clinicians understand the underlying biological mechanisms of their patients' breast cancer (BC) and facilitate clinical management. Several tissue-based mRNA tests are used routinely in clinical practice, particularly for assessing the BC recurrence risk, which can guide treatment decisions. However, blood-based mRNA assays have only recently started to emerge. This review explores the commercially available blood mRNA diagnostic assays for BC. These tests enable differentiation of BC from non-BC subjects (Syantra DX, BCtect), detection of small tumours <10 mm (early BC detection) (Syantra DX), detection of different cancers (including BC) from a single blood sample (multi-cancer blood test Aristotle), detection of BC in premenopausal and postmenopausal women and those with high breast density (Syantra DX), and improvement of diagnostic outcomes of DNA testing (variant interpretation) (+RNAinsight). The review also evaluates ongoing transcriptomic research on exciting possibilities for future assays, including blood transcriptome analyses aimed at differentiating lymph node positive and negative BC, distinguishing BC and benign breast disease, detecting ductal carcinoma in situ, and improving early detection further (expression changes can be detected in blood up to eight years before diagnosing BC using conventional approaches, while future metastatic and non-metastatic BC can be distinguished two years before BC diagnosis).
Collapse
|
21
|
Prediction of the Malignancy of a Breast Lesion Detected on Breast Ultrasound: Radiomics Applied to Clinical Practice. Cancers (Basel) 2023; 15:cancers15030964. [PMID: 36765921 PMCID: PMC9913654 DOI: 10.3390/cancers15030964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 01/23/2023] [Accepted: 01/27/2023] [Indexed: 02/05/2023] Open
Abstract
The study aimed to evaluate the performance of radiomics features and one ultrasound CAD (computer-aided diagnosis) in the prediction of the malignancy of a breast lesion detected with ultrasound and to develop a nomogram incorporating radiomic score and available information on CAD performance, conventional Breast Imaging Reporting and Data System evaluation (BI-RADS), and clinical information. Data on 365 breast lesions referred for breast US with subsequent histologic analysis between January 2020 and March 2022 were retrospectively collected. Patients were randomly divided into a training group (n = 255) and a validation test group (n = 110). A radiomics score was generated from the US image. The CAD was performed in a subgroup of 209 cases. The radiomics score included seven radiomics features selected with the LASSO logistic regression model. The multivariable logistic model incorporating CAD performance, BI-RADS evaluation, clinical information, and radiomic score as covariates showed promising results in the prediction of the malignancy of breast lesions: Area under the receiver operating characteristic curve, [AUC]: 0.914; 95% Confidence Interval, [CI]: 0.876-0.951. A nomogram was developed based on these results for possible future applications in clinical practice.
Collapse
|
22
|
de Jesus Aragão A, Carvalho D, Sanches B, Van Noije WA. Low-cost device for breast cancer screening: A dry setup IR-UWB proposal. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
|
23
|
Nguyen DL, Ambinder EB, Myers KS, Oluyemi E. Addressing Disparities Related to Access of Multimodality Breast Imaging Services Before and During the COVID-19 Pandemic. Acad Radiol 2022; 29:1852-1860. [PMID: 35562265 PMCID: PMC8947962 DOI: 10.1016/j.acra.2022.03.017] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 03/16/2022] [Accepted: 03/19/2022] [Indexed: 01/26/2023]
Abstract
Despite technological advancements focused on reducing breast cancer mortality through early detection, there have been reported disparities in the access to these imaging services with underserved patient populations (including racial minority groups and patients of low socioeconomic status) showing underutilization compared to other patient groups. These underserved populations tend to have more advanced breast cancer presentations, in part due to delays in diagnosis resulting in later stage of disease presentation. To make matters worse, the COVID-19 pandemic declared in March 2020 has resulted in significant healthcare disruptions leading to extensive delays in breast imaging services which are expected to negatively impact breast cancer mortality long-term. Given the worsening disparity in breast cancer mortality among racial/ethnic minorities and financially disadvantaged groups, it is vital to address these disparity gaps with the goal of reducing the barriers to timely breast cancer diagnosis and addressing breast cancer mortality differences among breast cancer patients. Therefore, this review aims to provide a discussion highlighting the disparities related to breast imaging access, the effects of the COVID-19 pandemic on these disparities, current targeted interventions implemented in breast imaging practices to reduce these disparities, and future directions on the journey to reducing disparity gaps for breast imaging patients. Tackling the root cause factors of the persistent breast cancer-related disparities is critical to meeting the needs of patients who are disadvantaged and can lead to continued improvement in the quality of individualized care for patients who have higher breast cancer morbidity and mortality risks.
Collapse
|
24
|
Wu Y, Zhang W, Shao X, Yang Y, Zhang T, Lei M, Wang Z, Gao B, Hu S. Research on the Multi-Element Synthetic Aperture Focusing Technique in Breast Ultrasound Imaging, Based on the Ring Array. MICROMACHINES 2022; 13:1753. [PMID: 36296106 PMCID: PMC9609697 DOI: 10.3390/mi13101753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Revised: 10/10/2022] [Accepted: 10/13/2022] [Indexed: 06/16/2023]
Abstract
As a widely clinical detection method, ultrasonography (US) has been applied to the diagnosis of breast cancer. In this paper, the multi-element synthetic aperture focusing (M-SAF) is applied to the ring array of breast ultrasonography (US) imaging, which addresses the problem of low imaging quality due to the single active element for each emission and the reception in the synthetic aperture focusing. In order to determine the optimal sub-aperture size, the formula is derived for calculating the internal sound pressure of the ring array with a 200 mm diameter, and the sound pressure distribution is analyzed. The ring array with 1024 elements (1024 ring array) is established in COMSOL Multiphysics 5.6, and the optimal sub-aperture size is 16 elements, according to the sound field beam simulation and the directivity research. Based on the existing experimental conditions, the ring array with 256 elements (256 ring array) is simulated and verified by experiments. The simulation has a spatial resolution evaluation in the k-Wave toolbox, and the experiment uses nylon rope and breast model imaging. The results show that if the sub-aperture size has four elements, the imaging quality is the highest. Specifically, the spatial resolution is the best, and the sound pressure amplitude and signal-to-noise ratio (SNR) are maintained at a high level in the reconstructed image. The optimal sub-aperture theory is verified by the two kinds of ring arrays, which also provide a theoretical basis for the application of the multi-element synthetic aperture focusing technology (M-SAF) in ring arrays.
Collapse
Affiliation(s)
- Yang Wu
- State Key Laboratory of Dynamic Measurement Technology, North University of China, Taiyuan 030051, China
- National Key Laboratory for Electronic Measurement Technology, School of Instrument and Electronics, North University of China, Taiyuan 030051, China
| | - Wendong Zhang
- State Key Laboratory of Dynamic Measurement Technology, North University of China, Taiyuan 030051, China
- National Key Laboratory for Electronic Measurement Technology, School of Instrument and Electronics, North University of China, Taiyuan 030051, China
| | - Xingling Shao
- State Key Laboratory of Dynamic Measurement Technology, North University of China, Taiyuan 030051, China
- National Key Laboratory for Electronic Measurement Technology, School of Instrument and Electronics, North University of China, Taiyuan 030051, China
| | - Yuhua Yang
- State Key Laboratory of Dynamic Measurement Technology, North University of China, Taiyuan 030051, China
- National Key Laboratory for Electronic Measurement Technology, School of Instrument and Electronics, North University of China, Taiyuan 030051, China
| | - Tian Zhang
- State Key Laboratory of Dynamic Measurement Technology, North University of China, Taiyuan 030051, China
- National Key Laboratory for Electronic Measurement Technology, School of Instrument and Electronics, North University of China, Taiyuan 030051, China
| | - Miao Lei
- State Key Laboratory of Dynamic Measurement Technology, North University of China, Taiyuan 030051, China
- National Key Laboratory for Electronic Measurement Technology, School of Instrument and Electronics, North University of China, Taiyuan 030051, China
| | - Zhihao Wang
- State Key Laboratory of Dynamic Measurement Technology, North University of China, Taiyuan 030051, China
- National Key Laboratory for Electronic Measurement Technology, School of Instrument and Electronics, North University of China, Taiyuan 030051, China
| | - Bizhen Gao
- State Key Laboratory of Dynamic Measurement Technology, North University of China, Taiyuan 030051, China
- National Key Laboratory for Electronic Measurement Technology, School of Instrument and Electronics, North University of China, Taiyuan 030051, China
| | - Shumin Hu
- State Key Laboratory of Dynamic Measurement Technology, North University of China, Taiyuan 030051, China
- National Key Laboratory for Electronic Measurement Technology, School of Instrument and Electronics, North University of China, Taiyuan 030051, China
| |
Collapse
|
25
|
Kaplan E, Chan WY, Dogan S, Barua PD, Bulut HT, Tuncer T, Cizik M, Tan RS, Acharya UR. Automated BI-RADS classification of lesions using pyramid triple deep feature generator technique on breast ultrasound images. Med Eng Phys 2022; 108:103895. [DOI: 10.1016/j.medengphy.2022.103895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 09/09/2022] [Accepted: 09/13/2022] [Indexed: 10/14/2022]
|
26
|
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.
Collapse
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.
| |
Collapse
|
27
|
Navarro SM, Shaikh H, Abdi H, Keil EJ, Odusanya S, Stewart KA, Tuyishime E, Mazingi D, Tuttle TM. Surgical applications of ultrasound use in low‐ and
middle‐income
countries: A systematic review. Australas J Ultrasound Med 2022; 25:80-97. [DOI: 10.1002/ajum.12302] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Affiliation(s)
- Sergio M. Navarro
- Department of Surgery University of Minnesota 420 Delaware Street SE Minneapolis MN 55455 USA
| | - Hashim Shaikh
- Department of Orthopaedics University of Rochester 601 Elmwood Avenue Rochester NY 14642 USA
| | - Hodan Abdi
- Department of Surgery University of Minnesota 420 Delaware Street SE Minneapolis MN 55455 USA
| | - Evan J. Keil
- Department of Surgery University of Minnesota 420 Delaware Street SE Minneapolis MN 55455 USA
| | - Simisola Odusanya
- Department of Surgery University of Minnesota 420 Delaware Street SE Minneapolis MN 55455 USA
| | - Kelsey A. Stewart
- Department of Anaesthesia, Critical Care, and Emergency Medicine University of Rwanda KN 4 Ave Kigali Rwanda
| | - Eugene Tuyishime
- Department of Anaesthesia University of Toronto 123 Edward Street Toronto ON M5G 1E2 Canada
- Department of Obstetrics and Gyenecology University of Minnesota 420 Delaware Street SE Minneapolis MN 55455 USA
| | - Dennis Mazingi
- Department of Surgery University of Zimbabwe Mazowe Street A168 Harare Zimbabwe
| | - Todd M. Tuttle
- Department of Surgery University of Minnesota 420 Delaware Street SE Minneapolis MN 55455 USA
| |
Collapse
|
28
|
Examining the Effectiveness of Supplementary Imaging Modalities for Breast Cancer Screening in Women with Dense Breasts: A Systematic Review and Meta-analysis. Eur J Radiol 2022; 154:110416. [DOI: 10.1016/j.ejrad.2022.110416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 05/31/2022] [Accepted: 06/18/2022] [Indexed: 11/15/2022]
|
29
|
Kolchev A, Pasynkov D, Egoshin I, Kliouchkin I, Pasynkova O, Tumakov D. YOLOv4-Based CNN Model versus Nested Contours Algorithm in the Suspicious Lesion Detection on the Mammography Image: A Direct Comparison in the Real Clinical Settings. J Imaging 2022; 8:88. [PMID: 35448216 PMCID: PMC9031201 DOI: 10.3390/jimaging8040088] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 03/21/2022] [Accepted: 03/23/2022] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND We directly compared the mammography image processing results obtained with the help of the YOLOv4 convolutional neural network (CNN) model versus those obtained with the help of the NCA-based nested contours algorithm model. METHOD We used 1080 images to train the YOLOv4, plus 100 images with proven breast cancer (BC) and 100 images with proven absence of BC to test both models. RESULTS the rates of true-positive, false-positive and false-negative outcomes were 60, 10 and 40, respectively, for YOLOv4, and 93, 63 and 7, respectively, for NCA. The sensitivities for the YOLOv4 and the NCA were comparable to each other for star-like lesions, masses with unclear borders, round- or oval-shaped masses with clear borders and partly visualized masses. On the contrary, the NCA was superior to the YOLOv4 in the case of asymmetric density and of changes invisible on the dense parenchyma background. Radiologists changed their earlier decisions in six cases per 100 for NCA. YOLOv4 outputs did not influence the radiologists' decisions. CONCLUSIONS in our set, NCA clinically significantly surpasses YOLOv4.
Collapse
Affiliation(s)
- Alexey Kolchev
- Department of Applied Mathematics and Informatics, Mari State University, Ministry of Education and Science of Russian Federation, 1 Lenin Square, Yoshkar-Ola 424000, Russia; (A.K.); (D.P.); (O.P.)
- Department of Radiology and Oncology, Mari State University, Ministry of Education and Science of Russian Federation, 1 Lenin Square, Yoshkar-Ola 424000, Russia
- Department of Fundamental Medicine, Mari State University, Ministry of Education and Science of Russian Federation, 1 Lenin Square, Yoshkar-Ola 424000, Russia
- Institute of Computational Mathematics and Information Technologies, Kazan Federal University, 18 Kremlevskaya St., Kazan 420008, Russia;
| | - Dmitry Pasynkov
- Department of Applied Mathematics and Informatics, Mari State University, Ministry of Education and Science of Russian Federation, 1 Lenin Square, Yoshkar-Ola 424000, Russia; (A.K.); (D.P.); (O.P.)
- Department of Diagnostic Ultrasound, Kazan State Medical Academy—Branch Campus of the Federal State Budgetary Educational Institution of Further Professional Education “Russian Medical Academy of Continuous Professional Education”, Ministry of Healthcare of the Russian Federation, 36 Butlerov St., Kazan 420012, Russia
| | - Ivan Egoshin
- Department of Applied Mathematics and Informatics, Mari State University, Ministry of Education and Science of Russian Federation, 1 Lenin Square, Yoshkar-Ola 424000, Russia; (A.K.); (D.P.); (O.P.)
| | - Ivan Kliouchkin
- Department of General Surgery, Kazan Medical University, Ministry of Health of Russian Federation, 49 Butlerov St., Kazan 420012, Russia;
| | - Olga Pasynkova
- Department of Applied Mathematics and Informatics, Mari State University, Ministry of Education and Science of Russian Federation, 1 Lenin Square, Yoshkar-Ola 424000, Russia; (A.K.); (D.P.); (O.P.)
| | - Dmitrii Tumakov
- Institute of Computational Mathematics and Information Technologies, Kazan Federal University, 18 Kremlevskaya St., Kazan 420008, Russia;
| |
Collapse
|
30
|
Park CKS, Xing S, Papernick S, Orlando N, Knull E, Toit CD, Bax JS, Gardi L, Barker K, Tessier D, Fenster A. Spatially tracked whole-breast three-dimensional ultrasound system toward point-of-care breast cancer screening in high-risk women with dense breasts. Med Phys 2022; 49:3944-3962. [PMID: 35319105 DOI: 10.1002/mp.15632] [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: 11/25/2021] [Revised: 03/16/2022] [Accepted: 03/17/2022] [Indexed: 11/06/2022] Open
Abstract
BACKGROUND Mammographic screening has reduced mortality in women through the early detection of breast cancer. However, the sensitivity for breast cancer detection is significantly reduced in women with dense breasts, in addition to being an independent risk factor. Ultrasound (US) has been proven effective in detecting small, early-stage, and invasive cancers in women with dense breasts. PURPOSE To develop an alternative, versatile, and cost-effective spatially tracked three-dimensional (3D) US system for whole-breast imaging. This paper describes the design, development, and validation of the spatially tracked 3DUS system, including its components for spatial tracking, multi-image registration and fusion, feasibility for whole-breast 3DUS imaging and multi-planar visualization in tissue-mimicking phantoms, and a proof-of-concept healthy volunteer study. METHODS The spatially tracked 3DUS system contains (a) a six-axis manipulator and counterbalanced stabilizer, (b) an in-house quick-release 3DUS scanner, adaptable to any commercially available US system, and removable, allowing for handheld 3DUS acquisition and two-dimensional US imaging, and (c) custom software for 3D tracking, 3DUS reconstruction, visualization, and spatial-based multi-image registration and fusion of 3DUS images for whole-breast imaging. Spatial tracking of the 3D position and orientation of the system and its joints (J1-6 ) were evaluated in a clinically accessible workspace for bedside point-of-care (POC) imaging. Multi-image registration and fusion of acquired 3DUS images were assessed with a quadrants-based protocol in tissue-mimicking phantoms and the target registration error (TRE) was quantified. Whole-breast 3DUS imaging and multi-planar visualization were evaluated with a tissue-mimicking breast phantom. Feasibility for spatially tracked whole-breast 3DUS imaging was assessed in a proof-of-concept healthy male and female volunteer study. RESULTS Mean tracking errors were 0.87 ± 0.52, 0.70 ± 0.46, 0.53 ± 0.48, 0.34 ± 0.32, 0.43 ± 0.28, and 0.78 ± 0.54 mm for joints J1-6 , respectively. Lookup table (LUT) corrections minimized the error in joints J1 , J2 , and J5 . Compound motions exercising all joints simultaneously resulted in a mean tracking error of 1.08 ± 0.88 mm (N = 20) within the overall workspace for bedside 3DUS imaging. Multi-image registration and fusion of two acquired 3DUS images resulted in a mean TRE of 1.28 ± 0.10 mm. Whole-breast 3DUS imaging and multi-planar visualization in axial, sagittal, and coronal views were demonstrated with the tissue-mimicking breast phantom. The feasibility of the whole-breast 3DUS approach was demonstrated in healthy male and female volunteers. In the male volunteer, the high-resolution whole-breast 3DUS acquisition protocol was optimized without the added complexities of curvature and tissue deformations. With small post-acquisition corrections for motion, whole-breast 3DUS imaging was performed on the healthy female volunteer showing relevant anatomical structures and details. CONCLUSIONS Our spatially tracked 3DUS system shows potential utility as an alternative, accurate, and feasible whole-breast approach with the capability for bedside POC imaging. Future work is focused on reducing misregistration errors due to motion and tissue deformations, to develop a robust spatially tracked whole-breast 3DUS acquisition protocol, then exploring its clinical utility for screening high-risk women with dense breasts.
Collapse
Affiliation(s)
- Claire Keun Sun Park
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada.,Imaging Research Laboratories, Robarts Research Institute, London, Ontario, Canada
| | - Shuwei Xing
- Imaging Research Laboratories, Robarts Research Institute, London, Ontario, Canada.,School of Biomedical Engineering, Faculty of Engineering, Western University, London, Ontario, Canada
| | - Samuel Papernick
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada.,Imaging Research Laboratories, Robarts Research Institute, London, Ontario, Canada
| | - Nathan Orlando
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada.,Imaging Research Laboratories, Robarts Research Institute, London, Ontario, Canada
| | - Eric Knull
- Imaging Research Laboratories, Robarts Research Institute, London, Ontario, Canada.,School of Biomedical Engineering, Faculty of Engineering, Western University, London, Ontario, Canada
| | - Carla Du Toit
- Imaging Research Laboratories, Robarts Research Institute, London, Ontario, Canada.,School of Kinesiology, Faculty of Health Sciences, Western University, London, Ontario, Canada
| | - Jeffrey Scott Bax
- Imaging Research Laboratories, Robarts Research Institute, London, Ontario, Canada
| | - Lori Gardi
- Imaging Research Laboratories, Robarts Research Institute, London, Ontario, Canada
| | - Kevin Barker
- Imaging Research Laboratories, Robarts Research Institute, London, Ontario, Canada
| | - David Tessier
- Imaging Research Laboratories, Robarts Research Institute, London, Ontario, Canada
| | - Aaron Fenster
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada.,Imaging Research Laboratories, Robarts Research Institute, London, Ontario, Canada.,School of Biomedical Engineering, Faculty of Engineering, Western University, London, Ontario, Canada.,Division of Imaging Sciences, Department of Medical Imaging, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| |
Collapse
|
31
|
Ragab M, Albukhari A, Alyami J, Mansour RF. Ensemble Deep-Learning-Enabled Clinical Decision Support System for Breast Cancer Diagnosis and Classification on Ultrasound Images. BIOLOGY 2022; 11:439. [PMID: 35336813 PMCID: PMC8945718 DOI: 10.3390/biology11030439] [Citation(s) in RCA: 39] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 02/25/2022] [Accepted: 03/11/2022] [Indexed: 01/02/2023]
Abstract
Clinical Decision Support Systems (CDSS) provide an efficient way to diagnose the presence of diseases such as breast cancer using ultrasound images (USIs). Globally, breast cancer is one of the major causes of increased mortality rates among women. Computer-Aided Diagnosis (CAD) models are widely employed in the detection and classification of tumors in USIs. The CAD systems are designed in such a way that they provide recommendations to help radiologists in diagnosing breast tumors and, furthermore, in disease prognosis. The accuracy of the classification process is decided by the quality of images and the radiologist's experience. The design of Deep Learning (DL) models is found to be effective in the classification of breast cancer. In the current study, an Ensemble Deep-Learning-Enabled Clinical Decision Support System for Breast Cancer Diagnosis and Classification (EDLCDS-BCDC) technique was developed using USIs. The proposed EDLCDS-BCDC technique was intended to identify the existence of breast cancer using USIs. In this technique, USIs initially undergo pre-processing through two stages, namely wiener filtering and contrast enhancement. Furthermore, Chaotic Krill Herd Algorithm (CKHA) is applied with Kapur's entropy (KE) for the image segmentation process. In addition, an ensemble of three deep learning models, VGG-16, VGG-19, and SqueezeNet, is used for feature extraction. Finally, Cat Swarm Optimization (CSO) with the Multilayer Perceptron (MLP) model is utilized to classify the images based on whether breast cancer exists or not. A wide range of simulations were carried out on benchmark databases and the extensive results highlight the better outcomes of the proposed EDLCDS-BCDC technique over recent methods.
Collapse
Affiliation(s)
- Mahmoud Ragab
- Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Centre for Artificial Intelligence in Precision Medicines, King Abdulaziz University, Jeddah 21589, Saudi Arabia;
- Mathematics Department, Faculty of Science, Al-Azhar University, Cairo 11884, Egypt
| | - Ashwag Albukhari
- Centre for Artificial Intelligence in Precision Medicines, King Abdulaziz University, Jeddah 21589, Saudi Arabia;
- Biochemistry Department, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Jaber Alyami
- Diagnostic Radiology Department, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah 21589, Saudi Arabia;
- Imaging Unit, King Fahd Medical Research Center, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Romany F. Mansour
- Department of Mathematics, Faculty of Science, New Valley University, El-Kharga 72511, Egypt;
| |
Collapse
|
32
|
Different Types of Ultrasound Probes Usage for Multi-Angle Conventional 3D Ultrasound Compound Imaging: A Breast Phantom Study. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12052689] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Three-dimensional automated breast ultrasound (ABUS) systems seem to offer excellent results in breast cancer screening tests and its early detection, comparable to handheld ultrasound B-mode scanning, with the benefit of saving physician time and reducing handheld ultrasound issues. Nevertheless, the ABUS systems are not very popular, due to the cost and very narrow application. The multi-angle conventional 3D ultrasound compound imaging method (MACUI) is intended for use with standard B-mode scanners in order to reduce cost but preserve the advantages of ABUS systems. The rotational probe movement is utilized in order to collect images for the three-dimensional reconstruction of the scanned tissue’s anatomy. The authors evaluate the capabilities to increase the scanned volume and quality of reconstructions, which are limited in current MACUI implementations, with a probe tilt and shift. The study shows and discusses the results of the imaging using different probes available for SmartUs Telemed B-Mode scanner at different scanning geometry in order to determine the capabilities of such an ultrasound imaging system. The results discussed in the paper highlight the benefits in quality improvement and scanning area obtained with tilted and shifted probes, as well as the advantages of using a relatively simple convex probe that does not incorporate software beam steering over more advanced devices.
Collapse
|
33
|
Barsic Ostojic S, Grbanovic L, Tonklin A, Kovacevic L, Marusic Z, Prutki M. Diagnostic performance of digital breast tomosynthesis in female patients with nipple discharge. Cancer Rep (Hoboken) 2022; 5:e1602. [PMID: 35142103 PMCID: PMC9575504 DOI: 10.1002/cnr2.1602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Revised: 11/08/2021] [Accepted: 01/10/2022] [Indexed: 11/09/2022] Open
Abstract
Background Nipple discharge is one of the most common symptoms related to the breast, but it is a presenting feature of breast cancer in 5%–12% of women. Aims The purpose of this study was to determine the diagnostic performance of digital breast tomosynthesis (DBT) in the evaluation of patients with nipple discharge and to compare it with mammography (MMG), ultrasound (US), and magnetic resonance imaging (MRI). Methods and Results This retrospective study included 53 patients with nipple discharge. All patients underwent DBT, and results were compared to MMG, breast US, and MRI. Radiological findings for each method were categorized according to BI‐RADS classification: categories 1–2 were considered negative and categories 3–5 positive. If a tissue specimen was obtained, the final diagnosis was established based on the results of histopathological analysis; otherwise, a clinical follow‐up was required for at least 2 years to confirm benign radiological findings. Measures of diagnostic accuracy of DBT, MMG, US, and MRI were calculated and compared. Results Final histopathological analysis revealed six malignant breast lesions, all of which were detected in patients with pathologic nipple discharge. DBT and MRI exhibited high sensitivity (100%) and high negative predictive value (100%) for the detection of breast cancer in patients with nipple discharge. DBT showed higher specificity compared to MRI (82.9% vs. 61.9%). Sensitivity and specificity of MMG were 83.3% and 76.6%, respectively. Breast US was determined to have a sensitivity of 66.7% and specificity of 57.5%. Conclusion DBT exhibited higher specificity than MRI at the same level of sensitivity and negative predictive value. Therefore, the use of DBT should be considered as an alternative to MRI in the assessment of patients with nipple discharge.
Collapse
Affiliation(s)
- Sanja Barsic Ostojic
- Clinical Department of Diagnostic and Interventional Radiology, University Hospital Centre Zagreb, Zagreb, Croatia
| | - Lora Grbanovic
- Clinical Department of Diagnostic and Interventional Radiology, University Hospital Centre Zagreb, Zagreb, Croatia
| | - Ana Tonklin
- Department of Rheumathology and Rehabilitation, General Hospital Dr. Tomislav Bardek, Koprivnica, Croatia
| | - Lucija Kovacevic
- Clinical Department of Diagnostic and Interventional Radiology, University Hospital Centre Zagreb, Zagreb, Croatia
| | - Zlatko Marusic
- Clinical Department of Pathology and Cytology, University Hospital Centre Zagreb, Zagreb, Croatia.,School of Medicine, University of Zagreb, Zagreb, Croatia
| | - Maja Prutki
- Clinical Department of Diagnostic and Interventional Radiology, University Hospital Centre Zagreb, Zagreb, Croatia.,School of Medicine, University of Zagreb, Zagreb, Croatia
| |
Collapse
|
34
|
Comparison of the diagnostic performance of Magnetic Resonance Imaging (MRI), ultrasound and mammography for detection of breast cancer based on tumor type, breast density and patient's history: A review. Radiography (Lond) 2022; 28:848-856. [DOI: 10.1016/j.radi.2022.01.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 01/14/2022] [Accepted: 01/19/2022] [Indexed: 02/07/2023]
|
35
|
Shah SM, Khan RA, Arif S, Sajid U. Artificial intelligence for breast cancer analysis: Trends & directions. Comput Biol Med 2022; 142:105221. [PMID: 35016100 DOI: 10.1016/j.compbiomed.2022.105221] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Revised: 01/03/2022] [Accepted: 01/03/2022] [Indexed: 12/18/2022]
Abstract
Breast cancer is one of the leading causes of death among women. Early detection of breast cancer can significantly improve the lives of millions of women across the globe. Given importance of finding solution/framework for early detection and diagnosis, recently many AI researchers are focusing to automate this task. The other reasons for surge in research activities in this direction are advent of robust AI algorithms (deep learning), availability of hardware that can run/train those robust and complex AI algorithms and accessibility of large enough dataset required for training AI algorithms. Different imaging modalities that have been exploited by researchers to automate the task of breast cancer detection are mammograms, ultrasound, magnetic resonance imaging, histopathological images or any combination of them. This article analyzes these imaging modalities and presents their strengths and limitations. It also enlists resources from where their datasets can be accessed for research purpose. This article then summarizes AI and computer vision based state-of-the-art methods proposed in the last decade to detect breast cancer using various imaging modalities. Primarily, in this article we have focused on reviewing frameworks that have reported results using mammograms as it is the most widely used breast imaging modality that serves as the first test that medical practitioners usually prescribe for the detection of breast cancer. Another reason for focusing on mammogram imaging modalities is the availability of its labelled datasets. Datasets availability is one of the most important aspects for the development of AI based frameworks as such algorithms are data hungry and generally quality of dataset affects performance of AI based algorithms. In a nutshell, this research article will act as a primary resource for the research community working in the field of automated breast imaging analysis.
Collapse
Affiliation(s)
- Shahid Munir Shah
- Department of Computer Science, Faculty of Information Technology, Salim Habib University, Karachi, Pakistan
| | - Rizwan Ahmed Khan
- Department of Computer Science, Faculty of Information Technology, Salim Habib University, Karachi, Pakistan.
| | - Sheeraz Arif
- Department of Computer Science, Faculty of Information Technology, Salim Habib University, Karachi, Pakistan
| | - Unaiza Sajid
- Department of Computer Science, Faculty of Information Technology, Salim Habib University, Karachi, Pakistan
| |
Collapse
|
36
|
Naeim RM, Marouf RA, Nasr MA, Abd El-Rahman ME. Comparing the diagnostic efficacy of digital breast tomosynthesis with full-field digital mammography using BI-RADS scoring. THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2021. [DOI: 10.1186/s43055-021-00421-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
Mammography has been the mainstay for the detection of breast cancer over decades. It has gradually advanced from screen film to full-field digital mammography. Tomosynthesis has evolved as advanced imaging for early diagnosis of breast lesions with a promising role in both diagnostic and screening settings, particularly in dense and treated breasts.
Results
This study included 90 female patients according to our inclusion criteria. All patients perform full-field digital mammography (FFDM) and digital breast tomosynthesis (DBT) and were classified according to breast density and age groups. Breast imaging reporting and data system (BI-RADS) scoring was assigned for each case. This was correlated with the final diagnosis; the diagnostic indices of mammography were a sensitivity of 64.44%, a specificity of 77.78%, a positive predictive value (PPV) 74.63%, a negative predictive value (NPV) of 68.63%, and a diagnostic accuracy of 71.11%. Diagnostic indices of DBT were a sensitivity of 100%, a specificity of 97.77%, PPV 97.78%, NPV 100%, and diagnostic accuracy of 97.7%.
In patients with dense breasts American College of Radiology (ACR) (c and d), 61% of cases had changed their BIRADS scoring with the addition of tomosynthesis. Yet, in non-dense breast ACR (a and b), 45% of cases had changed BIRADS scoring with the addition of DBT to FFDM.
Conclusion
DBT is a promising imaging modality offering better detection and characterization of different breast abnormalities, especially in young females, and those with dense breasts with an increase of sensitivity and specificity than FFDM. This leads to a reduction in the recalled cases, negative biopsies, and assessing the efficacy of therapy as it enables improving detection of breast cancer and different breast lesions not visualized by conventional mammography
Collapse
|
37
|
O’Connell AM, Marini TJ, Kawakyu-O’Connor DT. Cone-Beam Breast Computed Tomography: Time for a New Paradigm in Breast Imaging. J Clin Med 2021; 10:jcm10215135. [PMID: 34768656 PMCID: PMC8584471 DOI: 10.3390/jcm10215135] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 10/24/2021] [Accepted: 10/28/2021] [Indexed: 01/02/2023] Open
Abstract
It is time to reconsider how we image the breast. Although the breast is a 3D structure, we have traditionally used 2D mammography to perform screening and diagnostic imaging. Mammography has been continuously modified and improved, most recently with tomosynthesis and contrast mammography, but it is still using modifications of compression 2D mammography. It is time to consider 3D imaging for this 3D structure. Cone-beam breast computed tomography (CBBCT) is a revolutionary modality that will assist in overcoming the limitations of current imaging for dense breast tissue and overlapping structures. It also allows easy administration of contrast material for functional imaging. With a radiation dose on par with diagnostic mammography, rapid 10 s acquisition, no breast compression, and true high-resolution isotropic imaging, CBBCT has the potential to usher in a new era in breast imaging. These advantages could translate into lower morbidity and mortality from breast cancer.
Collapse
|
38
|
Rana SP, Dey M, Loretoni R, Duranti M, Sani L, Vispa A, Ghavami M, Dudley S, Tiberi G. Radial Basis Function for Breast Lesion Detection from MammoWave Clinical Data. Diagnostics (Basel) 2021; 11:1930. [PMID: 34679628 PMCID: PMC8534354 DOI: 10.3390/diagnostics11101930] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Revised: 10/10/2021] [Accepted: 10/13/2021] [Indexed: 11/22/2022] Open
Abstract
Recently, a novel microwave apparatus for breast lesion detection (MammoWave), uniquely able to function in air with 2 antennas rotating in the azimuth plane and operating within the band 1-9 GHz has been developed. Machine learning (ML) has been implemented to understand information from the frequency spectrum collected through MammoWave in response to the stimulus, segregating breasts with and without lesions. The study comprises 61 breasts (from 35 patients), each one with the correspondent output of the radiologist's conclusion (i.e., gold standard) obtained from echography and/or mammography and/or MRI, plus pathology or 1-year clinical follow-up when required. The MammoWave examinations are performed, recording the frequency spectrum, where the magnitudes show substantial discrepancy and reveals dissimilar behaviours when reflected from tissues with/without lesions. Principal component analysis is implemented to extract the unique quantitative response from the frequency response for automated breast lesion identification, engaging the support vector machine (SVM) with a radial basis function kernel. In-vivo feasibility validation (now ended) of MammoWave was approved in 2015 by the Ethical Committee of Umbria, Italy (N. 6845/15/AV/DM of 14 October 2015, N. 10352/17/NCAV of 16 March 2017, N 13203/18/NCAV of 17 April 2018). Here, we used a set of 35 patients. According to the radiologists conclusions, 25 breasts without lesions and 36 breasts with lesions underwent a MammoWave examination. The proposed SVM model achieved the accuracy, sensitivity, and specificity of 91%, 84.40%, and 97.20%. The proposed ML augmented MammoWave can identify breast lesions with high accuracy.
Collapse
Affiliation(s)
- Soumya Prakash Rana
- School of Engineering, London South Bank University, London SE1 0AA, UK; (M.D.); (M.G.); (S.D.); (G.T.)
| | - Maitreyee Dey
- School of Engineering, London South Bank University, London SE1 0AA, UK; (M.D.); (M.G.); (S.D.); (G.T.)
| | - Riccardo Loretoni
- Breast Screening and Diagnostic Breast Cancer Unit, AUSL Umbria 2, 06034 Foligno, Italy;
| | - Michele Duranti
- Department of Diagnostic Imaging, Perugia Hospital, 06156 Perugia, Italy;
| | - Lorenzo Sani
- UBT-Umbria Bioengineering Technologies, 06081 Perugia, Italy; (L.S.); (A.V.)
| | - Alessandro Vispa
- UBT-Umbria Bioengineering Technologies, 06081 Perugia, Italy; (L.S.); (A.V.)
| | - Mohammad Ghavami
- School of Engineering, London South Bank University, London SE1 0AA, UK; (M.D.); (M.G.); (S.D.); (G.T.)
| | - Sandra Dudley
- School of Engineering, London South Bank University, London SE1 0AA, UK; (M.D.); (M.G.); (S.D.); (G.T.)
| | - Gianluigi Tiberi
- School of Engineering, London South Bank University, London SE1 0AA, UK; (M.D.); (M.G.); (S.D.); (G.T.)
- UBT-Umbria Bioengineering Technologies, 06081 Perugia, Italy; (L.S.); (A.V.)
| |
Collapse
|
39
|
Gatta G, Cappabianca S, La Forgia D, Massafra R, Fanizzi A, Cuccurullo V, Brunese L, Tagliafico A, Grassi R. Second-Generation 3D Automated Breast Ultrasonography (Prone ABUS) for Dense Breast Cancer Screening Integrated to Mammography: Effectiveness, Performance and Detection Rates. J Pers Med 2021; 11:875. [PMID: 34575652 PMCID: PMC8468126 DOI: 10.3390/jpm11090875] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 08/24/2021] [Accepted: 08/29/2021] [Indexed: 12/22/2022] Open
Abstract
In our study, we added a three-dimensional automated breast ultrasound (3D ABUS) to mammography to evaluate the performance and cancer detection rate of mammography alone or with the addition of 3D prone ABUS in women with dense breasts. Our prospective observational study was based on the screening of 1165 asymptomatic women with dense breasts who selected independent of risk factors. The results evaluated include the cancers detected between June 2017 and February 2019, and all surveys were subjected to a double reading. Mammography detected four cancers, while mammography combined with a prone Sofia system (3D ABUS) doubled the detection rate, with eight instances of cancer being found. The diagnostic yield difference was 3.4 per 1000. Mammography alone was subjected to a recall rate of 14.5 for 1000 women, while mammography combined with 3D prone ABUS resulted in a recall rate of 26.6 per 1000 women. We also observed an additional 12.1 recalls per 1000 women screened. Integrating full-field digital mammography (FFDM) with 3D prone ABUS in women with high breast density increases and improves breast cancer detection rates in a significant manner, including small and invasive cancers, and it has a tolerable impact on recall rate. Moreover, 3D prone ABUS performance results are comparable with the performance results of the supine 3D ABUS system.
Collapse
Affiliation(s)
- Gianluca Gatta
- Dipartimento di Medicina di Precisione Università della Campania “Luigi Vanvitelli”, 80131 Napoli, Italy; (G.G.); (S.C.); (V.C.); (R.G.)
| | - Salvatore Cappabianca
- Dipartimento di Medicina di Precisione Università della Campania “Luigi Vanvitelli”, 80131 Napoli, Italy; (G.G.); (S.C.); (V.C.); (R.G.)
| | - Daniele La Forgia
- IRCCS Istituto Tumori “Giovanni Paolo II”, 70124 Bari, Italy; (R.M.); (A.F.)
| | - Raffaella Massafra
- IRCCS Istituto Tumori “Giovanni Paolo II”, 70124 Bari, Italy; (R.M.); (A.F.)
| | - Annarita Fanizzi
- IRCCS Istituto Tumori “Giovanni Paolo II”, 70124 Bari, Italy; (R.M.); (A.F.)
| | - Vincenzo Cuccurullo
- Dipartimento di Medicina di Precisione Università della Campania “Luigi Vanvitelli”, 80131 Napoli, Italy; (G.G.); (S.C.); (V.C.); (R.G.)
| | - Luca Brunese
- Dipartimento di Medicina e Scienze della Salute “Vincenzo Tiberio”—Università degli Studi del Molise, 86100 Campobasso, Italy;
| | | | - Roberto Grassi
- Dipartimento di Medicina di Precisione Università della Campania “Luigi Vanvitelli”, 80131 Napoli, Italy; (G.G.); (S.C.); (V.C.); (R.G.)
| |
Collapse
|
40
|
Harada-Shoji N, Suzuki A, Ishida T, Zheng YF, Narikawa-Shiono Y, Sato-Tadano A, Ohta R, Ohuchi N. Evaluation of Adjunctive Ultrasonography for Breast Cancer Detection Among Women Aged 40-49 Years With Varying Breast Density Undergoing Screening Mammography: A Secondary Analysis of a Randomized Clinical Trial. JAMA Netw Open 2021; 4:e2121505. [PMID: 34406400 PMCID: PMC8374606 DOI: 10.1001/jamanetworkopen.2021.21505] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
IMPORTANCE Mammography has limited accuracy in breast cancer screening. Ultrasonography, when used in conjunction with mammography screening, is helpful to detect early-stage and invasive cancers for asymptomatic women with dense and nondense breasts. OBJECTIVE To evaluate the performance of adjunctive ultrasonography with mammography for breast cancer screening, according to differences in breast density. DESIGN, SETTING, AND PARTICIPANTS This study is a secondary analysis of the Japan Strategic Anti-cancer Randomized Trial. Between July 2007 and March 2011, asymptomatic women aged 40 to 49 years were enrolled in Japan. The present study used data from cases enrolled from the screening center in Miyagi prefecture during 2007 to 2020. Participants were randomly assigned in a 1:1 ratio to undergo either mammography with ultrasonography (intervention group) or mammography alone (control group). Data analysis was performed from February to March 2020. EXPOSURES Ultrasonography adjunctive to mammography for breast cancer screening regardless of breast density. MAIN OUTCOMES AND MEASURES Sensitivity, specificity, recall rates, biopsy rates, and characteristics of screen-detected cancers and interval breast cancers were evaluated between study groups and for each modality according to breast density. RESULTS A total of 76 119 women were enrolled, and data for 19 213 women (mean [SD] age, 44.5 [2.8] years) from the Miyagi prefecture were analyzed; 9705 were randomized to the intervention group and 9508 were randomized to the control group. A total of 11 390 women (59.3%) had heterogeneously or extremely dense breasts. Among the overall group, 130 cancers were found. Sensitivity was significantly higher in the intervention group than the control group (93.2% [95% CI, 87.4%-99.0%] vs 66.7% [95% CI, 54.4%-78.9%]; P < .001). Similar trends were observed in women with dense breasts (sensitivity in intervention vs control groups, 93.2% [95% CI, 85.7%-100.0%] vs 70.6% [95% CI, 55.3%-85.9%]; P < .001) and nondense breasts (sensitivity in intervention vs control groups, 93.1% [95% CI, 83.9%-102.3%] vs 60.9% [95% CI, 40.9%-80.8%]; P < .001). The rate of interval cancers per 1000 screenings was lower in the intervention group compared with the control group (0.5 cancers [95% CI, 0.1-1.0 cancers] vs 2.0 cancers [95% CI, 1.1-2.9 cancers]; P = .004). Within the intervention group, the rate of invasive cancers detected by ultrasonography alone was significantly higher than that for mammography alone in both dense (82.4% [95% CI, 56.6%-96.2%] vs 41.7% [95% CI, 15.2%-72.3%]; P = .02) and nondense (85.7% [95% CI, 42.1%-99.6%] vs 25.0% [95% CI, 5.5%-57.2%]; P = .02) breasts. However, sensitivity of mammography or ultrasonography alone did not exceed 80% across all breast densities in the 2 groups. Compared with the control group, specificity was significantly lower in the intervention group (91.8% [95% CI, 91.2%-92.3%] vs 86.8% [95% CI, 86.2%-87.5%]; P < .001). Recall rates (13.8% [95% CI, 13.1%-14.5%] vs 8.6% [95% CI, 8.0%-9.1%]; P < .001) and biopsy rates (5.5% [95% CI, 5.1%-6.0%] vs 2.1% [95% CI, 1.8%-2.4%]; P < .001) were significantly higher in the intervention group than the control group. CONCLUSIONS AND RELEVANCE In this secondary analysis of a randomized clinical trial, screening mammography alone demonstrated low sensitivity, whereas adjunctive ultrasonography was associated with increased sensitivity. These findings suggest that adjunctive ultrasonography has the potential to improve detection of early-stage and invasive cancers across both dense and nondense breasts. Supplemental ultrasonography should be considered as an appropriate imaging modality for breast cancer screening in asymptomatic women aged 40 to 49 years regardless of breast density. TRIAL REGISTRATION NIPH Clinical Trial Identifier: UMIN000000757.
Collapse
Affiliation(s)
- Narumi Harada-Shoji
- Department of Breast and Endocrine Surgical Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Akihiko Suzuki
- Department of Breast and Endocrine Surgery, Tohoku Medical and Pharmaceutical University, Sendai, Japan
| | - Takanori Ishida
- Department of Breast and Endocrine Surgical Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Ying-Fang Zheng
- Department of Breast and Endocrine Surgical Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Yoko Narikawa-Shiono
- Department of Breast and Endocrine Surgical Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Akiko Sato-Tadano
- Department of Breast and Endocrine Surgical Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Rie Ohta
- Department of Breast and Endocrine Surgical Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Noriaki Ohuchi
- Department of Breast and Endocrine Surgical Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
| |
Collapse
|
41
|
Dai X, Lei Y, Wang T, Axente M, Xu D, Patel P, Jani AB, Curran WJ, Liu T, Yang X. Self-supervised learning for accelerated 3D high-resolution ultrasound imaging. Med Phys 2021; 48:3916-3926. [PMID: 33993508 DOI: 10.1002/mp.14946] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2020] [Revised: 05/03/2021] [Accepted: 05/10/2021] [Indexed: 12/23/2022] Open
Abstract
PURPOSE Ultrasound (US) imaging has been widely used in diagnosis, image-guided intervention, and therapy, where high-quality three-dimensional (3D) images are highly desired from sparsely acquired two-dimensional (2D) images. This study aims to develop a deep learning-based algorithm to reconstruct high-resolution (HR) 3D US images only reliant on the acquired sparsely distributed 2D images. METHODS We propose a self-supervised learning framework using cycle-consistent generative adversarial network (cycleGAN), where two independent cycleGAN models are trained with paired original US images and two sets of low-resolution (LR) US images, respectively. The two sets of LR US images are obtained through down-sampling the original US images along the two axes, respectively. In US imaging, in-plane spatial resolution is generally much higher than through-plane resolution. By learning the mapping from down-sampled in-plane LR images to original HR US images, cycleGAN can generate through-plane HR images from original sparely distributed 2D images. Finally, HR 3D US images are reconstructed by combining the generated 2D images from the two cycleGAN models. RESULTS The proposed method was assessed on two different datasets. One is automatic breast ultrasound (ABUS) images from 70 breast cancer patients, the other is collected from 45 prostate cancer patients. By applying a spatial resolution enhancement factor of 3 to the breast cases, our proposed method achieved the mean absolute error (MAE) value of 0.90 ± 0.15, the peak signal-to-noise ratio (PSNR) value of 37.88 ± 0.88 dB, and the visual information fidelity (VIF) value of 0.69 ± 0.01, which significantly outperforms bicubic interpolation. Similar performances have been achieved using the enhancement factor of 5 in these breast cases and using the enhancement factors of 5 and 10 in the prostate cases. CONCLUSIONS We have proposed and investigated a new deep learning-based algorithm for reconstructing HR 3D US images from sparely acquired 2D images. Significant improvement on through-plane resolution has been achieved by only using the acquired 2D images without any external atlas images. Its self-supervision capability could accelerate HR US imaging.
Collapse
Affiliation(s)
- Xianjin Dai
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA
| | - Yang Lei
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA
| | - Tonghe Wang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA
| | - Marian Axente
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA
| | - Dong Xu
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA
| | - Pretesh Patel
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA
| | - Ashesh B Jani
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA
| | - Walter J Curran
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA
| | - Tian Liu
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA
| | - Xiaofeng Yang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA
| |
Collapse
|
42
|
Abstract
It may seem unlikely that the field of radiology perpetuates disparities in health care, as most radiologists never interact directly with patients, and racial bias is not an obvious factor when interpreting images. However, a closer look reveals that imaging plays an important role in the propagation of disparities. For example, many advanced and resource-intensive imaging modalities, such as MRI and PET/CT, are generally less available in the hospitals frequented by people of color, and when they are available, access is impeded due to longer travel and wait times. Furthermore, their images may be of lower quality, and their interpretations may be more error prone. The aggregate effect of these imaging acquisition and interpretation disparities in conjunction with social factors is insufficiently recognized as part of the wide variation in disease outcomes seen between races in America. Understanding the nature of disparities in radiology is important to effectively deploy the resources and expertise necessary to mitigate disparities through diversity and inclusion efforts, research, and advocacy. In this article, the authors discuss disparities in access to imaging, examine their causes, and propose solutions aimed at addressing these disparities.
Collapse
Affiliation(s)
- Stephen Waite
- From the Department of Radiology, SUNY Downstate Medical Center, 450 Clarkson Ave, Brooklyn, NY 11203 (S.W., J.M.S.); and Department of Psychiatry, Weill Cornell Medical College, New York, NY (D.C.)
| | - Jinel Scott
- From the Department of Radiology, SUNY Downstate Medical Center, 450 Clarkson Ave, Brooklyn, NY 11203 (S.W., J.M.S.); and Department of Psychiatry, Weill Cornell Medical College, New York, NY (D.C.)
| | - Daria Colombo
- From the Department of Radiology, SUNY Downstate Medical Center, 450 Clarkson Ave, Brooklyn, NY 11203 (S.W., J.M.S.); and Department of Psychiatry, Weill Cornell Medical College, New York, NY (D.C.)
| |
Collapse
|
43
|
Azzam H, Kamal RM, Hanafy MM, Youssef A, Hashem LMB. Comparative study between contrast-enhanced mammography, tomosynthesis, and breast ultrasound as complementary techniques to mammography in dense breast parenchyma. THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2020. [DOI: 10.1186/s43055-020-00268-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Abstract
Background
Mammography is accused of having low sensitivity and specificity in dense breast parenchyma. Also, women with dense breasts show an increased risk of developing breast cancer. Breast ultrasound has been used for several years for a better characterization of breast lesions. Contrast-enhanced mammography and tomosynthesis are relative novel imaging techniques that have been implicated in breast cancer detection and diagnosis. We aimed to compare breast tomosynthesis, contrast-enhanced mammography, and breast ultrasound as complementary techniques to mammography in dense breast parenchyma.
Results
The study included 37 patients with 63 inconclusive mammography breast lesions. They all performed contrast-enhanced mammography, single-view tomosynthesis, and breast ultrasound. Mammography had a sensitivity of 83%, a specificity of 48%, a positive predictive value of 68%, a negative predictive value of 68%, and a diagnostic accuracy of 68%. Contrast-enhanced mammography had a sensitivity of 89%, a specificity of 89%, a positive predictive value of 91%, a negative predictive value of 86%, and a diagnostic accuracy of 89%. Tomosynthesis had a sensitivity of 86%, a specificity of 81%, a positive predictive value of 86%, a negative predictive value of 81%, and a diagnostic accuracy of 84%. Breast ultrasound had a sensitivity of 97%, a specificity of 85%, a positive predictive value of 90%, a negative predictive value of 96%, and a diagnostic accuracy of 92%.
Conclusion
Breast ultrasound, tomosynthesis, and contrast-enhanced mammography showed better performance compared to mammography in dense breasts. However, ultrasound being safe with no radiation hazards should be the second step modality of choice after mammography in the assessment of mammography dense breasts. Adding tomosynthesis to mammography in screening increases its sensitivity. Contrast-enhanced mammography should be reserved for cases with inconclusive sonomammographic results.
Collapse
|
44
|
Hashem LMB, Ali RHM, Helal MH, Gemeae EELEL, Moustafa AFI. Characterization of breast masses: a comparative study between automated breast ultrasound (ABUS) and digital breast tomosynthesis (DBT). THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2020. [DOI: 10.1186/s43055-020-00161-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
Automated three-dimensional (3D) breast ultrasound (US) systems and breast tomosynthesis are promising breast imaging modalities. The study aims to compare the diagnostic indices of the 3D imaging techniques: digital breast tomosynthesis (DBT) and automated breast ultrasound (ABUS) in the characterization of breast masses. This prospective study included 32 women with breast masses either detected by means of clinical examination or with the mammographic exam. All of them have been subjected to tomosynthesis and automatic breast ultrasound examinations. The images from both modalities were then analyzed one at a time by two experienced representative radiologists in consensus. Results were compared to each other and to pathology and follow-up of typically benign findings
Results
The masses statistically evaluated in this study were 37 in number, among which 16 were benign and 21 were malignant. The sensitivity and specificity of tomosynthesis in the characterization of breast masses were 100% and 81.25%, respectively, while automated breast ultrasound were 100% and 75%, respectively.
Conclusion
Tomosynthesis and automated breast ultrasound are two promising modalities in breast imaging. Their diagnostic indices in this study were very close to one another; therefore, they can be used as an adjunct modality to mammography for early diagnosis of breast cancer.
Collapse
|
45
|
Lei Y, He X, Yao J, Wang T, Wang L, Li W, Curran WJ, Liu T, Xu D, Yang X. Breast tumor segmentation in 3D automatic breast ultrasound using Mask scoring R-CNN. Med Phys 2020; 48:204-214. [PMID: 33128230 DOI: 10.1002/mp.14569] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Revised: 10/20/2020] [Accepted: 10/20/2020] [Indexed: 12/24/2022] Open
Abstract
PURPOSE Automatic breast ultrasound (ABUS) imaging has become an essential tool in breast cancer diagnosis since it provides complementary information to other imaging modalities. Lesion segmentation on ABUS is a prerequisite step of breast cancer computer-aided diagnosis (CAD). This work aims to develop a deep learning-based method for breast tumor segmentation using three-dimensional (3D) ABUS automatically. METHODS For breast tumor segmentation in ABUS, we developed a Mask scoring region-based convolutional neural network (R-CNN) that consists of five subnetworks, that is, a backbone, a regional proposal network, a region convolutional neural network head, a mask head, and a mask score head. A network block building direct correlation between mask quality and region class was integrated into a Mask scoring R-CNN based framework for the segmentation of new ABUS images with ambiguous regions of interest (ROIs). For segmentation accuracy evaluation, we retrospectively investigated 70 patients with breast tumor confirmed with needle biopsy and manually delineated on ABUS, of which 40 were used for fivefold cross-validation and 30 were used for hold-out test. The comparison between the automatic breast tumor segmentations and the manual contours was quantified by I) six metrics including Dice similarity coefficient (DSC), Jaccard index, 95% Hausdorff distance (HD95), mean surface distance (MSD), residual mean square distance (RMSD), and center of mass distance (CMD); II) Pearson correlation analysis and Bland-Altman analysis. RESULTS The mean (median) DSC was 85% ± 10.4% (89.4%) and 82.1% ± 14.5% (85.6%) for cross-validation and hold-out test, respectively. The corresponding HD95, MSD, RMSD, and CMD of the two tests was 1.646 ± 1.191 and 1.665 ± 1.129 mm, 0.489 ± 0.406 and 0.475 ± 0.371 mm, 0.755 ± 0.755 and 0.751 ± 0.508 mm, and 0.672 ± 0.612 and 0.665 ± 0.729 mm. The mean volumetric difference (mean and ± 1.96 standard deviation) was 0.47 cc ([-0.77, 1.71)) for the cross-validation and 0.23 cc ([-0.23 0.69]) for hold-out test, respectively. CONCLUSION We developed a novel Mask scoring R-CNN approach for the automated segmentation of the breast tumor in ABUS images and demonstrated its accuracy for breast tumor segmentation. Our learning-based method can potentially assist the clinical CAD of breast cancer using 3D ABUS imaging.
Collapse
Affiliation(s)
- Yang Lei
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA
| | - Xiuxiu He
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA
| | - Jincao Yao
- Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang Cancer Hospital.,Institute of Cancer and Basic Medicine (IBMC), Chinese Academy of Sciences, Hangzhou, 310022, China
| | - Tonghe Wang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA
| | - Lijing Wang
- Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang Cancer Hospital.,Institute of Cancer and Basic Medicine (IBMC), Chinese Academy of Sciences, Hangzhou, 310022, China
| | - Wei Li
- Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang Cancer Hospital.,Institute of Cancer and Basic Medicine (IBMC), Chinese Academy of Sciences, Hangzhou, 310022, China
| | - Walter J Curran
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA
| | - Tian Liu
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA
| | - Dong Xu
- Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang Cancer Hospital.,Institute of Cancer and Basic Medicine (IBMC), Chinese Academy of Sciences, Hangzhou, 310022, China
| | - Xiaofeng Yang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA
| |
Collapse
|
46
|
Ghieh D, Saade C, Najem E, El Zeghondi R, Rawashdeh MA, Berjawi G. Staying abreast of imaging - Current status of breast cancer detection in high density breast. Radiography (Lond) 2020; 27:229-235. [PMID: 32611494 DOI: 10.1016/j.radi.2020.06.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Revised: 05/26/2020] [Accepted: 06/08/2020] [Indexed: 11/18/2022]
Abstract
OBJECTIVES The aim of this paper is to illustrate the current status of imaging in high breast density as we enter a new decade of advancing medicine and technology to diagnose breast lesions. KEY FINDINGS Early detection of breast cancer has become the chief focus of research from governments to individuals. However, with varying breast densities across the globe, the explosion of breast density information related to imaging, phenotypes, diet, computer aided diagnosis and artificial intelligence has witnessed a dramatic shift in new screening recommendations in mammography, physical examination, screening younger women and women with comorbid conditions, screening women at high risk, and new screening technologies. Breast density is well known to be a risk factor in patients with suspected/known breast neoplasia. Extensive research in the field of qualitative and quantitative analysis on different tissue characteristics of the breast has rapidly become the chief focus of breast imaging. A summary of the available guidelines and modalities of breast imaging, as well as new emerging techniques under study that can potentially provide an augmentation or even a replacement of those currently available. CONCLUSION Despite all the advances in technology and all the research directed towards breast cancer, detection of breast cancer in dense breasts remains a dilemma. IMPLICATIONS FOR PRACTICE It is of utmost importance to develop highly sensitive screening modalities for early detection of breast cancer.
Collapse
Affiliation(s)
- D Ghieh
- Diagnostic Radiology Department, American University of Beirut Medical Center, P.O.Box: 11-0236, Riad El-Solh, Beirut, 1107 2020, Lebanon.
| | - C Saade
- Department of Medical Imaging Sciences, Faculty of Health Sciences, American University of Beirut, P.O.Box: 11-0236, Riad El-Solh, Beirut, 1107 2020, Lebanon.
| | - E Najem
- Diagnostic Radiology Department, American University of Beirut Medical Center, P.O.Box: 11-0236, Riad El-Solh, Beirut, 1107 2020, Lebanon.
| | - R El Zeghondi
- Department of Medical Imaging Sciences, Faculty of Health Sciences, American University of Beirut, P.O.Box: 11-0236, Riad El-Solh, Beirut, 1107 2020, Lebanon.
| | - M A Rawashdeh
- Department of Allied Medical Sciences, Jordan University of Science and Technology, P.O.Box: 3030, Irbid 22110, Jordan.
| | - G Berjawi
- Diagnostic Radiology Department, American University of Beirut Medical Center, P.O.Box: 11-0236, Riad El-Solh, Beirut, 1107 2020, Lebanon.
| |
Collapse
|
47
|
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.
Collapse
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
| |
Collapse
|
48
|
Al-Mousa DS, Alakhras M, Spuur KM, Alewaidat H, Rawashdeh M, Abdelrahman M, Brennan PC. Mammographic Breast Density Profile of Jordanian Women With Normal and Breast Cancer Findings. Breast Cancer (Auckl) 2020; 14:1178223420921381. [PMID: 32523348 PMCID: PMC7235662 DOI: 10.1177/1178223420921381] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Accepted: 04/01/2020] [Indexed: 12/09/2022] Open
Abstract
PURPOSE To document the mammographic breast density (MBD) distribution of Jordanian women and the relationship with MBD with age. Correlation between breast cancer diagnosis and density was also explored. METHODS A retrospective review of 660 screening mammograms from King Abdullah University Hospital was conducted. Mammograms were classified into 2 groups: normal (return to routine screening) and breast cancer and rated using the American College of Radiology (ACR) Breast Imaging-Reporting and Data System (BI-RADS) 5th edition for MBD. The association between MBD and age was assessed by descriptive analyses and Kruskal-Wallis test. To compare between normal and breast cancer groups, chi-square post hoc tests with Bonferroni adjustment was used. RESULTS Groups consisted of 73.9% (n = 488) normal group and 26.1% (n = 172) breast cancer group. A significant inverse relationship was demonstrated between age and MBD among the normal (r = -.319, P < .01) and breast cancer group (r = -.569, P < .01). In total, 69% (n = 336) of women in the normal group and 71% (n = 122) in the breast cancer group and 79.1% (n = 159) of the normal group and 100% (n = 48) of the breast cancer group aged 40 to 49 years reported high MBD (ACR BI-RADS c or d). CONCLUSIONS Most of women in both the normal and breast cancer groups evidenced increased MBD. Increased MBD was inversely proportional to age. As MBD has a known link to increased breast cancer risk and the decreased sensitivity of mammography and it is vital that future screening guidelines for Jordanian women consider the unique breast density distribution of this population.
Collapse
Affiliation(s)
- Dana S Al-Mousa
- Faculty of Applied Medical Sciences, Jordan University of Science and Technology, Irbid, Jordan
| | - Maram Alakhras
- Faculty of Applied Medical Sciences, Jordan University of Science and Technology, Irbid, Jordan
| | - Kelly M Spuur
- School of Dentistry & Health Sciences, Charles Sturt University, Wagga Wagga, NSW, Australia
| | - Haytham Alewaidat
- Faculty of Applied Medical Sciences, Jordan University of Science and Technology, Irbid, Jordan
| | - Mohammad Rawashdeh
- Faculty of Applied Medical Sciences, Jordan University of Science and Technology, Irbid, Jordan
| | - Mostafa Abdelrahman
- Faculty of Applied Medical Sciences, Jordan University of Science and Technology, Irbid, Jordan
| | - Patrick C Brennan
- Faculty of Health Sciences, The University of Sydney, Sydney, NSW, Australia
| |
Collapse
|
49
|
Quantitative analysis of breast tumours aided by three-dimensional photoacoustic/ultrasound functional imaging. Sci Rep 2020; 10:8047. [PMID: 32415203 PMCID: PMC7229157 DOI: 10.1038/s41598-020-64966-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2019] [Accepted: 04/27/2020] [Indexed: 12/11/2022] Open
Abstract
In this pilot study, we explored a quantitative method to analyse characteristics of breast tumours using 3D volumetric data obtained from a three-dimensional (3D) photoacoustic/ultrasound (PA/US) functional imaging system. Imaging results from 24 Asian patients with maximum tumour diameters less than 2 cm, including 8 benign tumours, 16 T1 stage invasive breast cancers (IBCs), and 22 normal breasts, were analysed. We found that the volumetric mean oxygenation saturation (SO2) in tumour regions of T1 stage IBCs was 7.7% lower than that of benign tumours (P = 0.016) and 3.9% lower than that of healthy breasts (P = 0.010). The volumetric mean SO2 in tumour surrounding regions of T1 stage IBCs was 4.9% lower than that of benign tumours (P = 0.009). For differentiating T1 stage IBCs and benign tumours, with a cut-off SO2 value of 78.2% inside tumours, we obtained a sensitivity of 100%, a specificity of 62.5%, and an AUC of 0.81; with a cut-off SO2 value of 77.9% in regions surrounding tumours, we obtained a sensitivity of 100%, a specificity of 75% and an AUC of 0.84. Our preliminary results demonstrate that 3D PA/US functional imaging has the potential to provide valuable quantitative physiological information that may be useful for the detection and evaluation of breast tumours.
Collapse
|
50
|
Sood R, Rositch AF, Shakoor D, Ambinder E, Pool KL, Pollack E, Mollura DJ, Mullen LA, Harvey SC. Ultrasound for Breast Cancer Detection Globally: A Systematic Review and Meta-Analysis. J Glob Oncol 2020; 5:1-17. [PMID: 31454282 PMCID: PMC6733207 DOI: 10.1200/jgo.19.00127] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
PURPOSE Mammography is not always available or feasible. The purpose of this systematic review and meta-analysis is to assess the diagnostic performance of ultrasound as a primary tool for early detection of breast cancer. MATERIALS AND METHODS For this systematic review and meta-analysis, we comprehensively searched PubMed and SCOPUS to identify articles from January 2000 to December 2018 that included data on the performance of ultrasound for detection of breast cancer. Studies evaluating portable, handheld ultrasound as an independent detection modality for breast cancer were included. Quality assessment and bias analysis were performed with the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Sensitivity analyses and meta-regression were used to explore heterogeneity. The study protocol has been registered with the international prospective register of systematic reviews (PROSPERO identifier: CRD42019127752). RESULTS Of the 526 identified studies, 26 were eligible for inclusion. Ultrasound had an overall pooled sensitivity and specificity of 80.1% (95% CI, 72.2% to 86.3%) and 88.4% (95% CI, 79.8% to 93.6%), respectively. When only low- and middle-income country data were considered, ultrasound maintained a diagnostic sensitivity of 89.2% and specificity of 99.1%. Meta-analysis of the included studies revealed heterogeneity. The high sensitivity of ultrasound for the detection of breast cancer was not statistically significantly different in subgroup analyses on the basis of mean age, risk, symptoms, study design, bias level, and study setting. CONCLUSION Given the increasing burden of breast cancer and infeasibility of mammography in certain settings, we believe these results support the potential use of ultrasound as an effective primary detection tool for breast cancer, which may be beneficial in low-resource settings where mammography is unavailable.
Collapse
Affiliation(s)
- Rupali Sood
- Johns Hopkins Medicine, Baltimore, MD.,RAD-AID International, Chevy Chase, MD
| | - Anne F Rositch
- The Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | | | | | - Kara-Lee Pool
- RAD-AID International, Chevy Chase, MD.,University of California, Los Angeles, CA
| | - Erica Pollack
- RAD-AID International, Chevy Chase, MD.,Denver Health Medical Center, Denver, CO
| | | | | | - Susan C Harvey
- Johns Hopkins Medicine, Baltimore, MD.,RAD-AID International, Chevy Chase, MD
| |
Collapse
|