Generic placeholder image

Current Drug Discovery Technologies

Editor-in-Chief

ISSN (Print): 1570-1638
ISSN (Online): 1875-6220

Mini-Review Article

Artificial Intelligence for the Management of Breast Cancer: An Overview

Author(s): Harshita Gandhi and Kapil Kumar*

Volume 21, Issue 4, 2024

Published on: 01 December, 2023

Article ID: e031123223115 Pages: 19

DOI: 10.2174/0115701638262066231030052520

Price: $65

Abstract

Breast cancer is a severe global health problem, and early detection, accurate diagnosis, and personalized treatment is the key to improving patient outcomes. Artificial intelligence (AI) and machine learning (ML) have emerged as promising breast cancer research and clinical practice tools in recent years. Various projects are underway in early detection, diagnosis, prognosis, drug discovery, advanced image analysis, precision medicine, predictive modeling, and personalized treatment planning using artificial intelligence and machine learning. These projects use different algorithms, including convolutional neural networks (CNNs), support vector machines (SVMs), decision trees, and deep learning methods, to analyze and improve different types of data, such as clinical, genomic, and imaging data for breast cancer management. The success of these projects has the potential to transform breast cancer care, and continued research and development in this area is likely to lead to more accurate and personalized breast cancer diagnosis, treatment, and outcomes.

Graphical Abstract

[1]
Nassif AB, Talib MA, Nasir Q, Afadar Y, Elgendy O. Breast cancer detection using artificial intelligence techniques: A systematic literature review. Artif Intell Med 2022; 127: 102276.
[http://dx.doi.org/10.1016/j.artmed.2022.102276] [PMID: 35430037]
[2]
Smart Breast AI: Transpara (R) from ScreenPoint Medical. Screen Point Available from: https://screenpoint-medical.com/
[3]
AI in radiology | QP - Precision ®. Quibim Website. Available from: https://quibim.com/products/quibim-precision/
[4]
PR Archives - Page 2 of 3 - Kheiron Medical. Kheiron Medical Available from: https://www.kheironmed.com/category/pr/
[5]
Aidoc Always On Healthcare AI. Healthcare AI | Aidoc Always-on AI Available from: https://www.aidoc.com/
[6]
Luh JY, Thompson RF, Lin S. Clinical documentation and patient care using artificial intelligence in radiation oncology. J Am Coll Radiol 2019; 16(9): 1343-6.
[http://dx.doi.org/10.1016/j.jacr.2019.05.044] [PMID: 31238022]
[7]
Esteva A, Robicquet A, Ramsundar B, et al. A guide to deep learning in healthcare. Nat Med 2019; 25(1): 24-9.
[http://dx.doi.org/10.1038/s41591-018-0316-z] [PMID: 30617335]
[8]
Xu J, Yang P, Xue S, et al. Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives. Hum Genet 2019; 138(2): 109-24.
[http://dx.doi.org/10.1007/s00439-019-01970-5] [PMID: 30671672]
[9]
Zhou X, Li C, Rahaman MM, et al. A comprehensive review for breast histopathology image analysis using classical and deep neural networks. IEEE Access 2020; 8: 90931-56.
[http://dx.doi.org/10.1109/ACCESS.2020.2993788]
[10]
Artificial Intelligence. National Cancer Institute 2020. Available from: https://www.cancer.gov/research/areas/diagnosis/artificial-intelligence (2020, August 31).
[11]
Kumar P, Chauhan S, Awasthi LK. Artificial intelligence in healthcare: Review, ethics, trust challenges & future research directions. Eng Appl Artif Intell 2023; 120: 105894.
[http://dx.doi.org/10.1016/j.engappai.2023.105894]
[12]
Jiang M, Zhang D, Tang SC, et al. Deep learning with convolutional neural network in the assessment of breast cancer molecular subtypes based on US images: A multicenter retrospective study. Eur Radiol 2021; 31(6): 3673-82.
[http://dx.doi.org/10.1007/s00330-020-07544-8] [PMID: 33226454]
[13]
Kim G, Bahl M. Assessing risk of breast cancer: A review of risk prediction models. J Breast Imaging 2021; 3(2): 144-55.
[http://dx.doi.org/10.1093/jbi/wbab001] [PMID: 33778488]
[14]
Ismail N, Cheab S. Breast cancer detection based on deep learning technique. 2019 International UNIMAS STEM 12th Engineering Conference (EnCon), Kuching, Malaysia. 2019; pp. 89-92.
[http://dx.doi.org/10.1109/EnCon.2019.8861256]
[15]
Halling-Brown MD, Warren LM, Ward D, et al. OPTIMAM mammography image database: A large-scale resource of mammography images and clinical data. Radiol Artif Intell 2020; 3(1): e200103.
[http://dx.doi.org/10.1148/ryai.2020200103] [PMID: 33937853]
[16]
Khairi SSM, Bakar MAA, Bakar SA, et al. Deep learning on histopathology images for breast cancer classification: A bibliometric analysis. Health Care 2021; 10(1): 10.
[http://dx.doi.org/10.3390/healthcare10010010]
[17]
Diagnostic Performance Benchmarks : BCSC. Available from: https://www.bcsc-research.org/statistics/diagnostic-performance-benchmarks
[18]
UK hospital first to use AI cancer treatment tool. Healthcare IT News 2020. Available from: https://www.healthcareitnews.com/news/emea/uk-hospital-first-use-ai-cancer-treatment-tool (2020, December 10).
[19]
Gastounioti A, Desai S, Ahluwalia VS, Conant EF, Kontos D. Artificial intelligence in mammographic phenotyping of breast cancer risk: A narrative review. Breast Cancer Res 2022; 24(1): 14.
[http://dx.doi.org/10.1186/s13058-022-01509-z] [PMID: 35184757]
[20]
IBM and Sage Bionetworks announce winners of first phase of DREAM Digital Mammography Challenge : Sage Bionetworks. 2019. Available from: https://sagebionetworks.org/in-the-news/ibm-and-sage-bionetworks-announce-winners-of-first-phase-of-dream-digital-mammography-challenge/
[21]
Troester M. Project 1. The Carolina Breast Cancer Study Available from: https://grantome.com/grant/NIH/
[22]
Using artificial intelligence to improve early breast cancer detection. 2017. Available from: https://news.mit.edu/2017/artificial-intelligence-early-breast-cancer-detection-1017
[23]
IIT Madras researchers develop AI tool for ‘Personalized Cancer Diagnosis’ : Times of India. Available from: https://timesofindia.indiatimes.com/life-style/health-fitness/health-news/iit-madras-researchers-develop-ai-tool-for-personalized-cancer-diagnosis/articleshow/92698337.cms
[24]
Sudhakar M, Rengaswamy R, Raman K. Multi-Omic Data Improve Prediction of Personalized Tumor Suppressors and Oncogenes Front Genet 2022; 13: 854190.
[http://dx.doi.org/10.3389/fgene.2022.854190]
[25]
Applying machine learning to mammography screening for breast cancer. Available from: https://www.deepmind.com/blog/applying-machine-learning-to-mammography-screening-for-breast-cancer
[26]
Deep Learning in mammography | Mémoire UCL. Available from: http://hdl.handle.net/2078.1/thesis:33168
[27]
Bresnick J. IBM Watson Health Teams Up with Hospitals for AI, EHR Research. HealthITAnalytics 2019. Available from: https://healthitanalytics.com/news/ibm-watson-health-teams-up-with-hospitals-for-ai-ehr-research (2019, June 17).
[28]
CAMELYON16. Grand Challenge Available from: https://camelyon16.grand-challenge.org/
[29]
Bejnordi BE, Veta M, Van Diest PJ, et al. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA 2017; 318(22): 2199-210.
[http://dx.doi.org/10.1001/jama.2017.14585]
[30]
Image analysis based on machine learning reliably identifies haematological malignancies challenging for the human eye. Available from: https://www.helsinki.fi/en/news/healthier-world/image-analysis-based-machine-learning-reliably-identifies-haematological-malignancies-challenging-human-eye
[31]
Elemento O, Leslie C, Lundin J, Tourassi G. Artificial intelligence in cancer research, diagnosis and therapy. Nat Rev Cancer 2021; 21(12): 747-52.
[http://dx.doi.org/10.1038/s41568-021-00399-1] [PMID: 34535775]
[32]
MQSA National Statistics. MQSA National Statistics | FDA 2023. Available from: https://www.fda.gov/radiation-emitting-products/mqsa-insights/mqsa-national-statistics
[33]
Hanis TM, Islam MA, Musa KI. Top 100 most-cited publications on breast cancer and machine learning research: A bibliometric analysis. Curr Med Chem 2022; 29(8): 1426-35.
[http://dx.doi.org/10.2174/0929867328666211108110731] [PMID: 34749608]
[34]
Artificial intelligence could yield more accurate breast cancer diagnoses. Available from: https://newsroom.ucla.edu/releases/artificial-intelligence-breast-cancer-diagnosis
[35]
Cui M, Zhang DY. Artificial intelligence and computational pathology. Lab Invest 2021; 101(4): 412-22.
[http://dx.doi.org/10.1038/s41374-020-00514-0]
[36]
Cline MS, Liao RG, Parsons MT, et al. BRCA challenge: BRCA exchange as a global resource for variants in BRCA1 and BRCA2. PLoS Genet 2018; 14(12): e1007752.
[http://dx.doi.org/10.1371/journal.pgen.1007752] [PMID: 30586411]
[37]
Janowczyk A, Zuo R, Gilmore H, Feldman M, Madabhushi A, Histo QC. An open-source quality control tool for digital pathology slides. JCO Clin Cancer Inform 2019; 3(3): 1-7.
[http://dx.doi.org/10.1200/CCI.18.00157] [PMID: 30990737]
[38]
McKinney SM, Sieniek M, Godbole V, et al. International evaluation of an AI system for breast cancer screening. Nature 2020; 577(7788): 89-94.
[http://dx.doi.org/10.1038/s41586-019-1799-6]
[39]
BD4BO PIONEER :ECPC. European Cancer Patient Coalition. 2022. Available from: https://ecpc.org/health-and-research/bd4bo-pioneer/ (2022, August 22).
[40]
Pioneer project increasing the efficacy of immunotherapy, Available from: https://www.uib.no/en/ccbio/158861/pioneer-project-increasing-efficacy-immunotherapy
[41]
Ak MF. A comparative analysis of breast cancer detection and diagnosis using data visualization and machine learning applications. Health Care 2020; 8(2): 111.
[http://dx.doi.org/10.3390/healthcare8020111]
[42]
Artificial Intelligence. 2020. Available from: https://www.cancer.gov/research/areas/diagnosis/artificial-intelligence
[43]
Konz N, Buda M, Gu H, et al. A competition, benchmark, code, and data for using artificial intelligence to detect lesions in digital breast tomosynthesis. JAMA Netw Open 2023; 6(2): e230524.
[http://dx.doi.org/10.1001/jamanetworkopen.2023.0524] [PMID: 36821110]
[44]
Micheel CM, Sweeney SM, LeNoue-Newton ML, et al. American association for cancer research project genomics evidence neoplasia information exchange: From inception to first data release and beyond—lessons learned and member institutions’ perspectives. JCO Clin Cancer Inform 2018; 2(2): 1-14. b
[http://dx.doi.org/10.1200/CCI.17.00083] [PMID: 30652542]
[45]
Pugh TJ, Bell JL, Bruce JP, et al. AACR Project GENIE: 100,000 cases and beyond. Cancer Discov 2022; 12(9): 2057.
[http://dx.doi.org/10.1158/2159-8290.CD-21-1547]
[46]
XC. MA. The cancer genome atlas: Clinical applications for breast cancer. Document : Gale Academic OneFile 2013. Available from; https://go.gale.com/ps/i.do?id=GALE%7CA355152534&sid=googleScholar&v=2.1&it=r&linkaccess=abs&issn=08909091&p=AONE&sw=w&userGroupName=anon%7Eb013b984(2013, December 1)
[47]
van Amerongen R. Behind the scenes of the human breast cell atlas project. J Mammary Gland Biol Neoplasia 2021; 26(1): 67-70.
[http://dx.doi.org/10.1007/s10911-021-09482-7] [PMID: 33914224]
[48]
HBCA | Main Page. HBCA | Main Page. Available from: https://navinlabcode.github.io/HumanBreastCellAtlas.github.io/
[49]
Mohamed TIA, Ezugwu AE, Fonou-Dombeu JV, et al. A bio-inspired convolution neural network architecture for automatic breast cancer detection and classification using RNA-Seq gene expression data. Sci Rep 2023; 13(1): 14644.
[http://dx.doi.org/10.1038/s41598-023-41731-z]
[50]
Kirsch N. Groupon Made Eric Lefkofsky A Billionaire—His Cancer-Fighting Startup Is Worth Far More. Forbes 2019. Available from: https://www.forbes.com/sites/noahkirsch/2019/07/31/eric-lefkofsky-cancer-tempus/?sh=b4f53d631b7b (2019, July 31).
[51]
Carron E. Tempus and Precision Health Informatics Announce Precision Medicine Collaboration. 2022. Available from: https://www.tempus.com/news/pr/tempus-and-precision-health-informatics-announce-precision-medicine-collaboration/
[52]
Beaubier N, Tell R, Huether R, et al. Clinical validation of the Tempus xO assay. Oncotarget 2018; 9(40): 25826-32.
[http://dx.doi.org/10.18632/oncotarget.25381]
[53]
SOPHiA GENETICS | The Healthcare Technology Report. Available from: https://thehealthcaretechnologyreport.com/top-companies/sophia-genetics/
[54]
SOPHiA GENETICS SA 2021 Annual Report, pg no-26 2021. Available from: https://thehealthcaretechnologyreport.com/top-companies/sophia-genetics/
[55]
Foundation Medicine | A World-leading Molecular Insights Company. Foundation Medicine | A World-leading Molecular Insights Company. Available from: https://www.foundationmedicine.com/
[56]
FoundationOne CDx P170019; FDA. Available from: https://www.accessdata.fda.gov/cdrh_docs/pdf17/P170019B.pdf
[57]
Deep 6 AI: 2017 in review : Deep6.ai. Deep6.ai. 2022. Available from: https://deep6.ai/resources/deep-6-ai-2017-in-review/ (2022, January 25).
[58]
Yusoff M, Haryanto T, Suhartanto H, et al. Accuracy analysis of deep learning methods in breast cancer classification: A structured review. Diagnostics 2023; 13(4): 683.
[http://dx.doi.org/10.3390/diagnostics13040683]
[59]
Desai AN. Artificial intelligence: Promise, pitfalls, and perspective. JAMA 2020; 323(24): 2448-9.
[http://dx.doi.org/10.1001/jama.2020.8737] [PMID: 32492093]
[60]
Becker A. Artificial intelligence in medicine: What is it doing for us today? Health Policy Technol 2019; 8(2): 198-205.
[http://dx.doi.org/10.1016/j.hlpt.2019.03.004]

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy