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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.
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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
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Dan Q, Xu Z, Burrows H, Bissram J, Stringer JSA, Li Y. Diagnostic performance of deep learning in ultrasound diagnosis of breast cancer: a systematic review. NPJ Precis Oncol 2024; 8:21. [PMID: 38280946 PMCID: PMC10821881 DOI: 10.1038/s41698-024-00514-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 12/08/2023] [Indexed: 01/29/2024] Open
Abstract
Deep learning (DL) has been widely investigated in breast ultrasound (US) for distinguishing between benign and malignant breast masses. This systematic review of test diagnosis aims to examine the accuracy of DL, compared to human readers, for the diagnosis of breast cancer in the US under clinical settings. Our literature search included records from databases including PubMed, Embase, Scopus, and Cochrane Library. Test accuracy outcomes were synthesized to compare the diagnostic performance of DL and human readers as well as to evaluate the assistive role of DL to human readers. A total of 16 studies involving 9238 female participants were included. There were no prospective studies comparing the test accuracy of DL versus human readers in clinical workflows. Diagnostic test results varied across the included studies. In 14 studies employing standalone DL systems, DL showed significantly lower sensitivities in 5 studies with comparable specificities and outperformed human readers at higher specificities in another 4 studies; in the remaining studies, DL models and human readers showed equivalent test outcomes. In 12 studies that assessed assistive DL systems, no studies proved the assistive role of DL in the overall diagnostic performance of human readers. Current evidence is insufficient to conclude that DL outperforms human readers or enhances the accuracy of diagnostic breast US in a clinical setting. Standardization of study methodologies is required to improve the reproducibility and generalizability of DL research, which will aid in clinical translation and application.
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Affiliation(s)
- Qing Dan
- Department of Ultrasound, Nanfang Hospital, Southern Medical University, 510515, Guangzhou, China
- Global Women's Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Ziting Xu
- Department of Ultrasound, Nanfang Hospital, Southern Medical University, 510515, Guangzhou, China
| | - Hannah Burrows
- Health Sciences Library, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Jennifer Bissram
- Health Sciences Library, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Jeffrey S A Stringer
- Global Women's Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
| | - Yingjia Li
- Department of Ultrasound, Nanfang Hospital, Southern Medical University, 510515, Guangzhou, China.
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Afrin H, Larson NB, Fatemi M, Alizad A. Deep Learning in Different Ultrasound Methods for Breast Cancer, from Diagnosis to Prognosis: Current Trends, Challenges, and an Analysis. Cancers (Basel) 2023; 15:3139. [PMID: 37370748 PMCID: PMC10296633 DOI: 10.3390/cancers15123139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 06/02/2023] [Accepted: 06/08/2023] [Indexed: 06/29/2023] Open
Abstract
Breast cancer is the second-leading cause of mortality among women around the world. Ultrasound (US) is one of the noninvasive imaging modalities used to diagnose breast lesions and monitor the prognosis of cancer patients. It has the highest sensitivity for diagnosing breast masses, but it shows increased false negativity due to its high operator dependency. Underserved areas do not have sufficient US expertise to diagnose breast lesions, resulting in delayed management of breast lesions. Deep learning neural networks may have the potential to facilitate early decision-making by physicians by rapidly yet accurately diagnosing and monitoring their prognosis. This article reviews the recent research trends on neural networks for breast mass ultrasound, including and beyond diagnosis. We discussed original research recently conducted to analyze which modes of ultrasound and which models have been used for which purposes, and where they show the best performance. Our analysis reveals that lesion classification showed the highest performance compared to those used for other purposes. We also found that fewer studies were performed for prognosis than diagnosis. We also discussed the limitations and future directions of ongoing research on neural networks for breast ultrasound.
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Affiliation(s)
- Humayra Afrin
- Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA
| | - Nicholas B. Larson
- Department of Quantitative Health Sciences, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA
| | - Mostafa Fatemi
- Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA
| | - Azra Alizad
- Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA
- Department of Radiology, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA
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