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Yu ZH, Hong YT, Chou CP. Enhancing Breast Cancer Diagnosis: A Nomogram Model Integrating AI Ultrasound and Clinical Factors. ULTRASOUND IN MEDICINE & BIOLOGY 2024; 50:1372-1380. [PMID: 38897841 DOI: 10.1016/j.ultrasmedbio.2024.05.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 04/16/2024] [Accepted: 05/12/2024] [Indexed: 06/21/2024]
Abstract
PURPOSE A novel nomogram incorporating artificial intelligence (AI) and clinical features for enhanced ultrasound prediction of benign and malignant breast masses. MATERIALS AND METHODS This study analyzed 340 breast masses identified through ultrasound in 308 patients. The masses were divided into training (n = 260) and validation (n = 80) groups. The AI-based analysis employed the Samsung Ultrasound AI system (S-detect). Univariate and multivariate analyses were conducted to construct nomograms using logistic regression. The AI-Nomogram was based solely on AI results, while the ClinAI- Nomogram incorporated additional clinical factors. Both nomograms underwent internal validation with 1000 bootstrap resamples and external validation using the independent validation group. Performance was evaluated by analyzing the area under the receiver operating characteristic (ROC) curve (AUC) and calibration curves. RESULTS The ClinAI-Nomogram, which incorporates patient age, AI-based mass size, and AI-based diagnosis, outperformed an existing AI-Nomogram in differentiating benign from malignant breast masses. The ClinAI-Nomogram surpassed the AI-Nomogram in predicting malignancy with significantly higher AUC scores in both training (0.873, 95% CI: 0.830-0.917 vs. 0.792, 95% CI: 0.748-0.836; p = 0.016) and validation phases (0.847, 95% CI: 0.763-0.932 vs. 0.770, 95% CI: 0.709-0.833; p < 0.001). Calibration curves further revealed excellent agreement between the ClinAI-Nomogram's predicted probabilities and actual observed risks of malignancy. CONCLUSION The ClinAI- Nomogram, combining AI alongside clinical data, significantly enhanced the differentiation of benign and malignant breast masses in clinical AI-facilitated ultrasound examinations.
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Affiliation(s)
- Zi-Han Yu
- Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan; Department of Radiology, Jiannren Hospital, Kaohsiung, Taiwan
| | - Yu-Ting Hong
- Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan
| | - Chen-Pin Chou
- Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan; Department of Medical Laboratory Science and Biotechnology, Fooyin University, Kaohsiung, Taiwan; Department of Pharmacy, College of Pharmacy, Tajen University, Pingtung, Taiwan.
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Allajbeu I, Morris K, Nanaa M, Payne N, Charnley K, Moyle PL, Taylor K, Sharma N, Lim Y, Gilbert FJ. Introduction of automated breast ultrasound as an additional screening tool for dense breasts in the UK: a practical approach from the BRAID trial. Clin Radiol 2024; 79:e641-e650. [PMID: 38413353 DOI: 10.1016/j.crad.2023.11.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Revised: 11/09/2023] [Accepted: 11/13/2023] [Indexed: 02/29/2024]
Affiliation(s)
- I Allajbeu
- Cambridge Breast Unit, Cambridge University Hospitals NHS Foundation Trust, Addenbrookes' Hospital, Hills Road, Cambridge CB2 0QQ, UK; Western Balkans University, 1001, Tirana, Albania; Department of Radiology, University of Cambridge, School of Clinical Medicine, Box 218, Level 5, Cambridge Biomedical Campus, Cambridge CB2 0QQ, UK
| | - K Morris
- Cambridge Breast Unit, Cambridge University Hospitals NHS Foundation Trust, Addenbrookes' Hospital, Hills Road, Cambridge CB2 0QQ, UK
| | - M Nanaa
- Department of Radiology, University of Cambridge, School of Clinical Medicine, Box 218, Level 5, Cambridge Biomedical Campus, Cambridge CB2 0QQ, UK
| | - N Payne
- Department of Radiology, University of Cambridge, School of Clinical Medicine, Box 218, Level 5, Cambridge Biomedical Campus, Cambridge CB2 0QQ, UK
| | - K Charnley
- Department of Radiology, University of Cambridge, School of Clinical Medicine, Box 218, Level 5, Cambridge Biomedical Campus, Cambridge CB2 0QQ, UK
| | - P L Moyle
- Cambridge Breast Unit, Cambridge University Hospitals NHS Foundation Trust, Addenbrookes' Hospital, Hills Road, Cambridge CB2 0QQ, UK
| | - K Taylor
- Cambridge Breast Unit, Cambridge University Hospitals NHS Foundation Trust, Addenbrookes' Hospital, Hills Road, Cambridge CB2 0QQ, UK
| | - N Sharma
- Breast Unit, Level 1 Chancellor Wing, St James Hospital, Beckett Street, Leeds LS9 7TF, UK
| | - Y Lim
- University Hospital of South Manchester, NHS Foundation Trust, Oxford Road, Manchester M13 9WL, UK
| | - F J Gilbert
- Cambridge Breast Unit, Cambridge University Hospitals NHS Foundation Trust, Addenbrookes' Hospital, Hills Road, Cambridge CB2 0QQ, UK; Department of Radiology, University of Cambridge, School of Clinical Medicine, Box 218, Level 5, Cambridge Biomedical Campus, Cambridge CB2 0QQ, UK.
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Fruchtman Brot H, Mango VL. Artificial intelligence in breast ultrasound: application in clinical practice. Ultrasonography 2024; 43:3-14. [PMID: 38109894 PMCID: PMC10766882 DOI: 10.14366/usg.23116] [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: 06/14/2023] [Revised: 08/14/2023] [Accepted: 08/29/2023] [Indexed: 12/20/2023] Open
Abstract
Ultrasound (US) is a widely accessible and extensively used tool for breast imaging. It is commonly used as an additional screening tool, especially for women with dense breast tissue. Advances in artificial intelligence (AI) have led to the development of various AI systems that assist radiologists in identifying and diagnosing breast lesions using US. This article provides an overview of the background and supporting evidence for the use of AI in hand held breast US. It discusses the impact of AI on clinical workflow, covering breast cancer detection, diagnosis, prediction of molecular subtypes, evaluation of axillary lymph node status, and response to neoadjuvant chemotherapy. Additionally, the article highlights the potential significance of AI in breast US for low and middle income countries.
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Slanetz PJ. The Promise of AI in Advancing Global Radiology. Radiology 2023; 307:e230895. [PMID: 37129489 DOI: 10.1148/radiol.230895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Affiliation(s)
- Priscilla J Slanetz
- From the Division of Breast Imaging, Department of Radiology, FGH-4, Boston Medical Center, 820 Harrison Ave, Boston, MA 02118; and Boston University Chobanian & Avedisian School of Medicine, Boston, Mass
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Harvey JA. Broadening Our Perspective of Health Care Inequities. JOURNAL OF BREAST IMAGING 2023; 5:1-2. [PMID: 38416966 DOI: 10.1093/jbi/wbac082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Indexed: 03/01/2024]
Affiliation(s)
- Jennifer A Harvey
- University of Rochester, Department of Imaging Sciences, Rochester, NY, USA
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