Artificial Intelligence in Breast Screening: A Systematic Literature Review
DOI:
https://doi.org/10.48111/2021.03.09Keywords:
Convolutional neural networks, Artificial intelligence, deep neural network, Neoadjuvant chemotherapy, Breast cancerAbstract
Breast cancer is the most prevalent cancer in women worldwide. Early presentation, detection and prompt treatment limit morbidity and mortality due to breast cancer. Conventionally, breast cancer screening techniques and diagnosis have relied upon interpretation of radiologists and pathologists. However, advancement in artificial intelligence can lead to further enhancement in accuracy and efficiency of these diagnostic techniques, thereby, reducing incidence of morbidity and mortality.
AIM This systemic literature review is conducted to ascertain whether artificial intelligence (AI) can be used to complement existing breast cancer screening techniques. Objective of this review is to determine whether AI can enhance sensitivity of screening techniques, enable more accurate classification of benign and malignant tumors and improve assessment of response to neoadjuvant therapy
METHODS This systemic review is conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines. A comprehensive computer literature search in database of PubMed was performed using a combination of search terms: ‘Artificial intelligence’ OR ‘AI’ OR ‘Machine learning’ AND ‘Screening’ AND ‘Breast cancer’. 843 articles were identified through search in PubMed database. Following removal of 4 duplicate papers, titles and abstracts of 839 articles were reviewed. 115 articles with relevant titles and abstracts were analyzed. Following thorough analysis, 15 papers were included in this literature review.
RESULTS AI algorithms exhibited capability in classifying breast lesions and identification of malignancy in otherwise suspicious lesions across different imaging techniques. The integration and assistance of AI algorithms in interpretation of MRI, mammography and thermography has led to significant improvement in diagnostic accuracy and classification of breast lesions. AI complements radiologists and aids in improving performance, thereby, generating better results. AI has the capability to predict response to neoadjuvant chemotherapy in breast cancer patients, leading to safer, more effective and more cost-effective treatment for breast cancer patients.
CONCLUSIONS Artificial intelligence has the potential to revolutionize medicine in 21st century. Artificial intelligence has widespread potential in breast cancer screening. It can aid in improving radiologists’ ability to detect cancer on radiograms, classifying breast lesions, and predicting response to neoadjuvant therapy.
References
Gulzar F, Akhtar MS, Sadiq R, Bashir S, Jamil S, Baig SM. Identifying the reasons for delayed presentation of Pakistani breast cancer patients at a tertiary care hospital. Cancer Management and Research. 2019;11:1087-1096. doi:10.2147/CMAR.S180388
Sharma PH. Identification of factors influencing delayed presentation of cancer patients. International Journal of Community Medicine. 2020;7(5). doi:10.18203/2394-6040.ijcmph20201709
Siegel RL, Miller KD, Jemal A. Cancer statistics, 2016. CA: A Cancer Journal for Clinicians. 2016;66(1):7-30. doi:10.3322/caac.21332
Nelson HD, Fu R, Cantor A, Pappas M, Daeges M, Humphrey L. Effectiveness of Breast Cancer Screening: Systematic Review and Meta-analysis to Update the 2009 U.S. Preventive Services Task Force Recommendation. Annals of internal medicine. 2016;164(4):244-255. doi:10.7326/M15-0969
Ogbuanya AU, Anyanwu SN, Iyare EF, Nwigwe CG. The Role of Fine Needle Aspiration Cytology in Triple Assessment of Patients with Malignant Breast Lumps. Nigerian journal of surgery?: official publication of the Nigerian Surgical Research Society. 2020;26(1):35. doi:10.4103/NJS.NJS_50_19
Liu MZ, Mutasa S, Chang P, Siddique M, Jambawalikar S, Ha R. A novel CNN algorithm for pathological complete response prediction using an I-SPY TRIAL breast MRI database. Magnetic resonance imaging. 2020;73:148-151. doi:10.1016/J.MRI.2020.08.021
Turing AM. Computing Machinery and Intelligence. Parsing the Turing Test: Philosophical and Methodological Issues in the Quest for the Thinking Computer. Published online 2009:23-65. doi:10.1007/978-1-4020-6710-5_3
Keskinbora KH. Medical ethics considerations on artificial intelligence. Journal of clinical neuroscience?: official journal of the Neurosurgical Society of Australasia. 2019;64:277-282. doi:10.1016/J.JOCN.2019.03.001
Hopgood AA. The State of Artificial Intelligence. Advances in Computers. 2005;65:1-75. doi:10.1016/S0065-2458(05)65001-2
Das S, Dey A, Pal A, Roy N. Applications of Artificial Intelligence in Machine Learning: Review and Prospect. International Journal of Computer Applications. 2015;115(9):31-41. doi:10.5120/20182-2402
Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639). doi:10.1038/NATURE21056
Rannen Triki A, Blaschko MB, Jung YM, et al. Intraoperative margin assessment of human breast tissue in optical coherence tomography images using deep neural networks. Computerized medical imaging and graphics?: the official journal of the Computerized Medical Imaging Society. 2018;69:21-32. doi:10.1016/J.COMPMEDIMAG.2018.06.002
Bennani-Baiti B, Bennani-Baiti N, Baltzer PA. Diagnostic Performance of Breast Magnetic Resonance Imaging in Non-Calcified Equivocal Breast Findings: Results from a Systematic Review and Meta-Analysis. PloS one. 2016;11(8). doi:10.1371/JOURNAL.PONE.0160346
Garud H, Karri SPK, Sheet D, et al. High-Magnification Multi-Views Based Classification of Breast Fine Needle Aspiration Cytology Cell Samples Using Fusion of Decisions From Deep Convolutional Networks. Published online 2017:76-81.
Schaffter T, Buist DSM, Lee CI, et al. Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms. JAMA network open. 2020;3(3):e200265. doi:10.1001/JAMANETWORKOPEN.2020.0265
Gore JC. Artificial intelligence in medical imaging. Magnetic resonance imaging. 2020;68:A1-A4. doi:10.1016/J.MRI.2019.12.006
Cire?an DC, Giusti A, Gambardella LM, Schmidhuber J. Mitosis detection in breast cancer histology images with deep neural networks. Medical image computing and computer-assisted intervention?: MICCAI . International Conference on Medical Image Computing and Computer-Assisted Intervention. 2013;16(Pt 2):411-418. doi:10.1007/978-3-642-40763-5_51
McKinney SM, Sieniek M, Godbole V, et al. International evaluation of an AI system for breast cancer screening. Nature. 2020;577(7788):89-94. doi:10.1038/S41586-019-1799-6
MammoScreen AI Tool Improves Diagnostic Performance of Radiologists in Detecting Breast Cancer. Accessed September 9, 2021. https://www.cancernetwork.com/view/mammoscreen-ai-tool-improves-diagnostic-performance-of-radiologists-in-detecting-breast-cancer
Ellmann S, Wenkel E, Dietzel M, et al. Implementation of machine learning into clinical breast MRI: Potential for objective and accurate decision-making in suspicious breast masses. PLOS ONE. 2020;15(1):e0228446. doi:10.1371/JOURNAL.PONE.0228446
Bennani-Baiti B, Baltzer PA. MR Imaging for Diagnosis of Malignancy in Mammographic Microcalcifications: A Systematic Review and Meta-Analysis. Radiology. 2017;283(3):692-701. doi:10.1148/RADIOL.2016161106
Herent P, Schmauch B, Jehanno P, et al. Detection and characterization of MRI breast lesions using deep learning. Diagnostic and Interventional Imaging. 2019;100(4):219-225. doi:10.1016/J.DIII.2019.02.008
Yousefi B, Akbari H, Maldague XPV. Detecting Vasodilation as Potential Diagnostic Biomarker in Breast Cancer Using Deep Learning-Driven Thermomics. Biosensors. 2020;10(11). doi:10.3390/BIOS10110164
Cai H, Huang Q, Rong W, et al. Breast Microcalcification Diagnosis Using Deep Convolutional Neural Network from Digital Mammograms. Computational and Mathematical Methods in Medicine. 2019;2019. doi:10.1155/2019/2717454
Watanabe, A.T. Lim, V., Vu, H.X. et al. Improved Cancer Detection Using Artificial Intelligence: a Retrospective Evaluation of Missed Cancers on Mammography. Journal of digital imaging. 2019;32(4):625-637. doi:10.1007/S10278-019-00192-5
Rodriguez-Ruiz A, Lång K, Gubern-Merida A, et al. Can we reduce the workload of mammographic screening by automatic identification of normal exams with artificial intelligence? A feasibility study. European radiology. 2019;29(9):4825-4832. doi:10.1007/S00330-019-06186-9
Fleury, E., Marcomini, K. Performance of machine learning software to classify breast lesions using BI-RADS radiomic features on ultrasound images. European radiology experimental. 2019;3(1):34. doi:10.1186/S41747-019-0112-7
Choi JS, Han BK, Ko ES, et al. Effect of a Deep Learning Framework-Based Computer-Aided Diagnosis System on the Diagnostic Performance of Radiologists in Differentiating between Malignant and Benign Masses on Breast Ultrasonography. Korean journal of radiology. 2019;20(5):749-758. doi:10.3348/KJR.2018.0530
Rodriguez-Ruiz A, Lång K, Gubern-Merida A, et al. Stand-Alone Artificial Intelligence for Breast Cancer Detection in Mammography: Comparison With 101 Radiologists. Journal of the National Cancer Institute. 2019;111(9):916-922. doi:10.1093/JNCI/DJY222
Tahmassebi A, Wengert GJ, Helbich TH, et al. Impact of Machine Learning With Multiparametric Magnetic Resonance Imaging of the Breast for Early Prediction of Response to Neoadjuvant Chemotherapy and Survival Outcomes in Breast Cancer Patients. Investigative radiology. 2019;54(2):110-117. doi:10.1097/RLI.0000000000000518
Cain EH, Saha A, Harowicz MR, Marks JR, Marcom PK, Mazurowski MA. Multivariate machine learning models for prediction of pathologic response to neoadjuvant therapy in breast cancer using MRI features: a study using an independent validation set. Breast cancer research and treatment. 2019;173(2):455-463. doi:10.1007/S10549-018-4990-9
Porter PL. Global trends in breast cancer incidence and mortality. Salud Publica de Mexico. 2009;51(SUPPL.2):s141-s146. doi:10.1590/S0036-36342009000800003
Ko NY, Hong S, Winn RA, Calip GS. Association of Insurance Status and Racial Disparities with the Detection of Early-Stage Breast Cancer. JAMA Oncology. 2020;6(3):385-392. doi:10.1001/jamaoncol.2019.5672
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