Computer aided diagnosis in mammograms for breast cancer screening
Diagnóstico asistido por computadora en mamografías para detección del cáncer de mama | 乳腺癌筛查中乳房 X 光片的计算机辅助诊断
DOI:
https://doi.org/10.25176/RFMH.v24i4.6554Abstract
Securing access to quality healthcare services, particularly for essential screening tests like mammograms, presents a significant challenge in developing nations. Women often encounter extensive waiting periods, sometimes extending for several months, to undergo a mammogram. This crucial test plays a pivotal role in the early detection of breast cancer, where timely diagnosis is crucial for effective treatment and enhanced survival prospects. Delays in obtaining a diagnosis can significantly impact the health of patients and their well-being, underscoring the importance of early detection. Compounding these challenges is the scarcity of resources and healthcare professionals, which hinders swift and efficient access to preventive care. Such constraints underscore the pressing need for improvements in the interpretation of radiological studies and a reduction in the workload of imaging specialists. These improvements would not only optimize interdisciplinary collaboration but also enhance patient care, particularly for critical screenings like mammograms (3).
Technological advancements, particularly in artificial intelligence (AI), have significantly influenced numerous sectors, including healthcare (4). Yet, in areas such as Latin America, where the healthcare infrastructure and resources may not fully support the integration of advanced diagnostic techniques based on informatic tools, the potential of these innovations seems to be underutilized. A promising application of technology is in Computer-Aided Diagnosis (CAD) for breast cancer screening with mammograms. CAD leverages vast datasets and pattern recognition algorithms to detect anomalies within mammograms, potentially facilitating the early identification of lesions. Given that breast cancer ranks among the most prevalent diseases affecting women globally, early detection is vital for enhancing patient survival rates and quality of life. Given these challenges, our study aims to examine the latest developments in artificial intelligence technology as a supplementary tool in CAD strategies for mammograms. Our goal is to foster better interdisciplinary collaboration among clinicians, radiologists, and pathologists through a state of the art (SOTA) review of the topic. By doing so, we anticipate streamlining the diagnostic workflow and elevating the efficiency of breast cancer detection and treatment processes.
Downloads
References
Henriksen EL, Carlsen JF, Vejborg IM, Nielsen MB, Lauridsen CA. The efficacy of using computer-aided detection (CAD) for detection of breast cancer in mammography screening: a systematic review. Acta Radiol. 2019 Jan;60(1):13–8.
Watanabe AT, Lim V, Vu HX, Chim R, Weise E, Liu J, et al. Improved Cancer Detection Using Artificial Intelligence: a Retrospective Evaluation of Missed Cancers on Mammography. J Digit Imaging. 2019 Aug;32(4):625–37.
Jairam MP, Ha R. A review of artificial intelligence in mammography. Clin Imaging. 2022 Aug;88:36–44.
Jones MA, Islam W, Faiz R, Chen X, Zheng B. Applying artificial intelligence technology to assist with breast cancer diagnosis and prognosis prediction. Front Oncol. 2022 Aug 31;12:980793.
Boumaraf S, Liu X, Ferkous C, Ma X. A New Computer-Aided Diagnosis System with Modified Genetic Feature Selection for BI-RADS Classification of Breast Masses in Mammograms. BioMed Res Int. 2020 May 11;2020:1–17.
Chougrad H, Zouaki H, Alheyane O. Deep Convolutional Neural Networks for breast cancer screening. Comput Methods Programs Biomed. 2018 Apr;157:19–30.
Al-antari MA, Al-masni MA, Choi MT, Han SM, Kim TS. A fully integrated computer-aided diagnosis system for digital X-ray mammograms via deep learning detection, segmentation, and classification. Int J Med Inf. 2018 Sep;117:44–54.
Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Syst Rev. 2021 Dec;10(1):89.
Assari Z, Mahloojifar A, Ahmadinejad N. A bimodal BI-RADS-guided GoogLeNet-based CAD system for solid breast masses discrimination using transfer learning. Comput Biol Med. 2022 Mar;142:105160.
Ramadan SZ. Methods Used in Computer-Aided Diagnosis for Breast Cancer Detection Using Mammograms: A Review. J Healthc Eng. 2020 Mar 12;2020:1–21.
He Z, Li Y, Zeng W, Xu W, Liu J, Ma X, et al. Can a Computer-Aided Mass Diagnosis Model Based on Perceptive Features Learned From Quantitative Mammography Radiology Reports Improve Junior Radiologists’ Diagnosis Performance? An Observer Study. Front Oncol. 2021 Dec 17;11:773389.
Patel BK, Ranjbar S, Wu T, Pockaj BA, Li J, Zhang N, et al. Computer-aided diagnosis of contrast-enhanced spectral mammography: A feasibility study. Eur J Radiol. 2018 Jan;98:207–13.
Bahl M. Detecting Breast Cancers with Mammography: Will AI Succeed Where Traditional CAD Failed? Radiology. 2019 Feb;290(2):315–6.
Retson TA, Eghtedari M. Expanding Horizons: The Realities of CAD, the Promise of Artificial Intelligence, and Machine Learning’s Role in Breast Imaging beyond Screening Mammography. Diagnostics. 2023 Jun 21;13(13):2133.
Hamidinekoo A, Denton E, Rampun A, Honnor K, Zwiggelaar R. Deep learning in mammography and breast histology, an overview and future trends. Med Image Anal. 2018 Jul;47:45–67.
Arce S, Vijay A, Yim E, Spiguel LR, Hanna M. Evaluation of an Artificial Intelligence System for Detection of Invasive Lobular Carcinoma on Digital Mammography. Cureus [Internet]. 2023 May 9 [cited 2023 Oct 16]; Available from: https://www.cureus.com/articles/144590-evaluation-of-an-artificial-intelligence-system-for-detection-of-invasive-lobular-carcinoma-on-digital-mammography
Lee SE, Han K, Yoon JH, Youk JH, Kim EK. Depiction of breast cancers on digital mammograms by artificial intelligence-based computer-assisted diagnosis according to cancer characteristics. Eur Radiol. 2022 Apr 30;32(11):7400–8.
Al-masni MA, Al-antari MA, Park JM, Gi G, Kim TY, Rivera P, et al. Simultaneous detection and classification of breast masses in digital mammograms via a deep learning YOLO-based CAD system. Comput Methods Programs Biomed. 2018 Apr;157:85–94.
Baccouche A, Garcia-Zapirain B, Elmaghraby AS. An integrated framework for breast mass classification and diagnosis using stacked ensemble of residual neural networks. Sci Rep. 2022 Jul 18;12(1):12259.
James JJ, Giannotti E, Chen Y. Evaluation of a computer-aided detection (CAD)-enhanced 2D synthetic mammogram: comparison with standard synthetic 2D mammograms and conventional 2D digital mammography. Clin Radiol. 2018 Oct;73(10):886–92.
Guo Z, Xie J, Wan Y, Zhang M, Qiao L, Yu J, et al. A review of the current state of the computer-aided diagnosis (CAD) systems for breast cancer diagnosis. Open Life Sci. 2022 Dec 9;17(1):1600–11.
Yoon J, Lee HS, Kim MJ, Park VY, Kim EK, Yoon JH. AI-CAD for differentiating lesions presenting as calcifications only on mammography: outcome analysis incorporating the ACR BI-RADS descriptors for calcifications. Eur Radiol. 2022 Jun 24;32(10):6565–74.
Džoić Dominković M. What Can We Actually See Using Computer Aided Detection in Mammography? Acta Clin Croat [Internet]. 2020 [cited 2023 Oct 16]; Available from: https://hrcak.srce.hr/index.php?show=clanak&id_clanak_jezik=368628
Masud R, Al-Rei M, Lokker C. Computer-Aided Detection for Breast Cancer Screening in Clinical Settings: Scoping Review. JMIR Med Inform. 2019 Jul 18;7(3):e12660.
Kohli A, Jha S. Why CAD Failed in Mammography. J Am Coll Radiol. 2018 Mar;15(3):535–7.
Lee SE, Son NH, Kim MH, Kim EK. Mammographic Density Assessment by Artificial Intelligence-Based Computer-Assisted Diagnosis: A Comparison with Automated Volumetric Assessment. J Digit Imaging. 2022 Apr;35(2):173–9.
Kim H, Choi JS, Kim K, Ko ES, Ko EY, Han BK. Effect of artificial intelligence–based computer-aided diagnosis on the screening outcomes of digital mammography: a matched cohort study. Eur Radiol. 2023 May 16;33(10):7186–98.
Yoon JH, Han K, Suh HJ, Youk JH, Lee SE, Kim EK. Artificial intelligence-based computer-assisted detection/diagnosis (AI-CAD) for screening mammography: Outcomes of AI-CAD in the mammographic interpretation workflow. Eur J Radiol Open. 2023 Dec;11:100509.
Loizidou K, Skouroumouni G, Nikolaou C, Pitris C. A Review of Computer-Aided Breast Cancer Diagnosis Using Sequential Mammograms. Tomography. 2022 Dec 6;8(6):2874–92.
Loizidou K, Elia R, Pitris C. Computer-aided breast cancer detection and classification in mammography: A comprehensive review. Comput Biol Med. 2023 Feb;153:106554.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Revista de la Facultad de Medicina Humana
This work is licensed under a Creative Commons Attribution 4.0 International License.