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Breast Cancer Research
Published in: Breast Cancer Research 1/2022

Open Access 01-12-2022 | Breast Cancer | Review

Artificial intelligence in mammographic phenotyping of breast cancer risk: a narrative review

Authors: Aimilia Gastounioti, Shyam Desai, Vinayak S. Ahluwalia, Emily F. Conant, Despina Kontos

Published in: Breast Cancer Research | Issue 1/2022

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Abstract

Background

Improved breast cancer risk assessment models are needed to enable personalized screening strategies that achieve better harm-to-benefit ratio based on earlier detection and better breast cancer outcomes than existing screening guidelines. Computational mammographic phenotypes have demonstrated a promising role in breast cancer risk prediction. With the recent exponential growth of computational efficiency, the artificial intelligence (AI) revolution, driven by the introduction of deep learning, has expanded the utility of imaging in predictive models. Consequently, AI-based imaging-derived data has led to some of the most promising tools for precision breast cancer screening.

Main body

This review aims to synthesize the current state-of-the-art applications of AI in mammographic phenotyping of breast cancer risk. We discuss the fundamentals of AI and explore the computing advancements that have made AI-based image analysis essential in refining breast cancer risk assessment. Specifically, we discuss the use of data derived from digital mammography as well as digital breast tomosynthesis. Different aspects of breast cancer risk assessment are targeted including (a) robust and reproducible evaluations of breast density, a well-established breast cancer risk factor, (b) assessment of a woman’s inherent breast cancer risk, and (c) identification of women who are likely to be diagnosed with breast cancers after a negative or routine screen due to masking or the rapid and aggressive growth of a tumor. Lastly, we discuss AI challenges unique to the computational analysis of mammographic imaging as well as future directions for this promising research field.

Conclusions

We provide a useful reference for AI researchers investigating image-based breast cancer risk assessment while indicating key priorities and challenges that, if properly addressed, could accelerate the implementation of AI-assisted risk stratification to future refine and individualize breast cancer screening strategies.
Literature
1.
Pace LE, Keating NL. A systematic assessment of benefits and risks to guide breast cancer screening decisions. JAMA. 2014;311(13):1327–35.PubMed
2.
3.
McDonald ES, Clark AS, Tchou J, Zhang P, Freedman GM. Clinical diagnosis and management of breast cancer. J Nucl Med. 2016;57(Supplement 1):9S-16S.PubMed
4.
Pashayan N, Antoniou AC, Ivanus U, Esserman LJ, Easton DF, French D, Sroczynski G, Hall P, Cuzick J, Evans DG. Personalized early detection and prevention of breast cancer: ENVISION consensus statement. Nat Rev Clin Oncol. 2020;17(11):687–705.PubMedPubMedCentral
5.
Destounis SV, Santacroce A, Arieno A. Update on breast density, risk estimation, and supplemental screening. Am J Roentgenol. 2020;214(2):296–305.
6.
Conant EF, Sprague BL, Kontos D. Beyond BI-RADS density: a call for quantification in the breast imaging clinic. Radiology. 2018;286(2):401–4.PubMed
7.
Gastounioti A, Conant EF, Kontos D. Beyond breast density: a review on the advancing role of parenchymal texture analysis in breast cancer risk assessment. Breast Cancer Res. 2016;18(1):91.PubMedPubMedCentral
8.
Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, Van Der Laak JA, Van Ginneken B, Sánchez CI. A survey on deep learning in medical image analysis. Med Image Anal. 2017;42:60–88.PubMed
9.
Bertsimas D, Wiberg H. Machine learning in oncology: methods, applications, and challenges. JCO Clin Cancer Inform. 2020;4:885–94.PubMed
10.
Sechopoulos I, Teuwen J, Mann R. Artificial intelligence for breast cancer detection in mammography and digital breast tomosynthesis: state of the art. Semin Cancer Biol. 2021;72:214–25.PubMed
11.
Geras KJ, Mann RM, Moy L. Artificial intelligence for mammography and digital breast tomosynthesis: current concepts and future perspectives. Radiology. 2019;293(2):246–59.PubMed
12.
13.
14.
Schmidhuber J. Deep learning in neural networks: an overview. Neural Netw. 2015;61:85–117.PubMed
15.
Sun C, Shrivastava A, Singh S, Gupta A. Revisiting unreasonable effectiveness of data in deep learning era. In: Proceedings of the IEEE international conference on computer vision: 2017; 2017. p. 843–52.
16.
Cho J, Lee K, Shin E, Choy G, Do S. How much data is needed to train a medical image deep learning system to achieve necessary high accuracy? 2015. arXiv preprint arXiv:​1511.​06348.
17.
Samala RK, Chan H-P, Hadjiiski L, Helvie MA, Richter CD, Cha KH. Breast cancer diagnosis in digital breast tomosynthesis: effects of training sample size on multi-stage transfer learning using deep neural nets. IEEE Trans Med Imaging. 2018;38(3):686–96.
18.
LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436–44.
19.
Bengio Y. Learning deep architectures for AI. Found Trends Mach Learn. 2009;2(1):1–127.
20.
D’Orsi CJ. ACR BI-RADS atlas: breast imaging reporting and data system. Reston: American College of Radiology; 2013.
21.
Sprague BL, Conant EF, Onega T, Garcia MP, Beaber EF, Herschorn SD, Lehman CD, Tosteson AN, Lacson R, Schnall MD. Variation in mammographic breast density assessments among radiologists in clinical practice: a multicenter observational study. Ann Intern Med. 2016;165(7):457–64.PubMedPubMedCentral
22.
ACR. Breast imaging reporting and data system® (BI-RADS®) Atlas. In: Edited by Radiology ACo, 4 edn. Reston, VA; 2003.
23.
Youk JH, Kim SJ, Son EJ, Gweon HM, Kim J-A. Comparison of visual assessment of breast density in BI-RADS 4th and 5th editions with automated volumetric measurement. Am J Roentgenol. 2017;209(3):703–8.
24.
Kerlikowske K, Scott CG, Mahmoudzadeh AP, Ma L, Winham S, Jensen MR, Wu FF, Malkov S, Pankratz VS, Cummings SR. Automated and clinical breast imaging reporting and data system density measures predict risk for screen-detected and interval cancers: a case–control study. Ann Intern Med. 2018;168(11):757–65.PubMedPubMedCentral
25.
Dontchos BN, Yala A, Barzilay R, Xiang J, Lehman CD. External validation of a deep learning model for predicting mammographic breast density in routine clinical practice. Acad Radiol. 2021;28(4):475–80.PubMed
26.
Matthews TP, Singh S, Mombourquette B, Su J, Shah MP, Pedemonte S, Long A, Maffit D, Gurney J, Morales Hoil R. A multi-site study of a breast density deep learning model for full-field digital mammography images and synthetic mammography images. Radiol Artif Intell. 2020;3:e200015.PubMedPubMedCentral
27.
Saffari N, Rashwan HA, Abdel-Nasser M, Kumar Singh V, Arenas M, Mangina E, Herrera B, Puig D. Fully automated breast density segmentation and classification using deep learning. Diagnostics. 2020;10(11):988.PubMedCentral
28.
Deng J, Ma Y, Li D, Zhao J, Liu Y, Zhang H. Classification of breast density categories based on SE-attention neural networks. Comput Methods Programs Biomed. 2020;193:105489.PubMed
29.
Pérez-Benito FJ, Signol F, Perez-Cortes J-C, Fuster-Baggetto A, Pollan M, Pérez-Gómez B, Salas-Trejo D, Casals M, Martínez I, LLobet R. A deep learning system to obtain the optimal parameters for a threshold-based breast and dense tissue segmentation. Comput Methods Programs Biomed. 2020;195:105668.PubMed
30.
Chang K, Beers AL, Brink L, Patel JB, Singh P, Arun NT, Hoebel KV, Gaw N, Shah M, Pisano ED. Multi-institutional assessment and crowdsourcing evaluation of deep learning for automated classification of breast density. J Am Coll Radiol. 2020;17(12):1653–62.PubMed
31.
Ciritsis A, Rossi C, Vittoria De Martini I, Eberhard M, Marcon M, Becker AS, Berger N, Boss A. Determination of mammographic breast density using a deep convolutional neural network. Br J Radiol. 2019;92(1093):20180691.PubMed
32.
Lehman CD, Yala A, Schuster T, Dontchos B, Bahl M, Swanson K, Barzilay R. Mammographic breast density assessment using deep learning: clinical implementation. Radiology. 2019;290(1):52–8.PubMed
33.
Mohamed AA, Berg WA, Peng H, Luo Y, Jankowitz RC, Wu S. A deep learning method for classifying mammographic breast density categories. Med Phys. 2018;45(1):314–21.PubMed
34.
Mohamed AA, Luo Y, Peng H, Jankowitz RC, Wu S. Understanding clinical mammographic breast density assessment: a deep learning perspective. J Digit Imaging. 2018;31(4):387–92.PubMed
35.
Roth HR, Chang K, Singh P, Neumark N, Li W, Gupta V, Gupta S, Qu L, Ihsani A, Bizzo BC, et al. Federated learning for breast density classification: a real-world implementation. In: Albarqouni S, et al., editors. Domain adaptation and representation transfer, and distributed and collaborative learning. Cham: Springer; 2020. p. 181–91.
36.
Kallenberg M, Petersen K, Nielsen M, Ng A, Diao P, Igel C, Vachon C, Holland K, Karssemeijer N, Lillholm M. Unsupervised deep learning applied to breast density segmentation and mammographic risk scoring. IEEE Trans Med Imaging. 2016;35(5):1322–31.PubMed
37.
Li S, Wei J, Chan H-P, Helvie MA, Roubidoux MA, Lu Y, Zhou C, Hadjiiski LM, Samala RK. Computer-aided assessment of breast density: comparison of supervised deep learning and feature-based statistical learning. Phys Med Biol. 2018;63(2):025005.PubMedPubMedCentral
38.
Maghsoudi OH, Gastounioti A, Scott C, Pantalone L, Wu F-F, Cohen EA, Winham S, Conant EF, Vachon C, Kontos D. Deep-LIBRA: an artificial-intelligence method for robust quantification of breast density with independent validation in breast cancer risk assessment. Med Image Anal. 2021;73:102138.
39.
Lee J, Nishikawa RM. Automated mammographic breast density estimation using a fully convolutional network. Med Phys. 2018;45(3):1178–90.PubMed
40.
Gastounioti A, Pantalone L, Scott CG, Cohen EA, Wu FF, Winham SJ, Jensen MR, Maidment AD, Vachon CM, Conant EF. Fully automated volumetric breast density estimation from digital breast tomosynthesis. Radiology. 2021;301(3):561–8.PubMed
41.
Li H, Giger ML, Huynh BQ, Antropova NO. Deep learning in breast cancer risk assessment: evaluation of convolutional neural networks on a clinical dataset of full-field digital mammograms. J Med Imaging. 2017;4(4):041304.
42.
Gastounioti A, Oustimov A, Hsieh M-K, Pantalone L, Conant EF, Kontos D. Using convolutional neural networks for enhanced capture of breast parenchymal complexity patterns associated with breast cancer risk. Acad Radiol. 2018;25:977–84.PubMedPubMedCentral
43.
Dembrower K, Liu Y, Azizpour H, Eklund M, Smith K, Lindholm P, Strand F. Comparison of a deep learning risk score and standard mammographic density score for breast cancer risk prediction. Radiology. 2020;294(2):265–72.PubMed
44.
Yala A, Lehman C, Schuster T, Portnoi T, Barzilay R. A deep learning mammography-based model for improved breast cancer risk prediction. Radiology. 2019;292(1):60–6.PubMed
45.
Arefan D, Mohamed AA, Berg WA, Zuley ML, Sumkin JH, Wu S. Deep learning modeling using normal mammograms for predicting breast cancer risk. Med Phys. 2020;47(1):110–8.PubMed
46.
47.
Ha R, Chang P, Karcich J, Mutasa S, Van Sant EP, Liu MZ, Jambawalikar S. Convolutional neural network based breast cancer risk stratification using a mammographic dataset. Acad Radiol. 2019;26(4):544–9.PubMed
48.
Lotter W, Diab AR, Haslam B, Kim JG, Grisot G, Wu E, Wu K, Onieva JO, Boyer Y, Boxerman JL. Robust breast cancer detection in mammography and digital breast tomosynthesis using an annotation-efficient deep learning approach. Nat Med. 2021;27(2):244–9.PubMed
49.
Eriksson M, Czene K, Strand F, Zackrisson S, Lindholm P, Lång K, Förnvik D, Sartor H, Mavaddat N, Easton D. Identification of women at high risk of breast cancer who need supplemental screening. Radiology. 2020;297(2):327–33.PubMed
50.
McKinney SM, Sieniek M, Godbole V, Godwin J, Antropova N, Ashrafian H, Back T, Chesus M, Corrado GC, Darzi A. International evaluation of an AI system for breast cancer screening. Nature. 2020;577(7788):89–94.PubMed
51.
Hinton B, Ma L, Mahmoudzadeh AP, Malkov S, Fan B, Greenwood H, Joe B, Lee V, Kerlikowske K, Shepherd J. Deep learning networks find unique mammographic differences in previous negative mammograms between interval and screen-detected cancers: a case-case study. Cancer Imaging. 2019;19(1):41.PubMedPubMedCentral
52.
Liu Y, Azizpour H, Strand F, Smith K. Decoupling inherent risk and early cancer signs in image-based breast cancer risk models. In: International conference on medical image computing and computer-assisted intervention: 2020. Springer; 2020. p. 230–40.
53.
Geras KJ, Wolfson S, Kim S, Moy L, Cho K. High-resolution breast cancer screening with multi-view deep convolutional neural networks. 2017. arXiv:​1703.​07047.
54.
Kretz T, Müller K-R, Schaeffter T, Elster C. Mammography image quality assurance using deep learning. IEEE Trans Biomed Eng. 2020;67(12):3317–26.PubMed
55.
Gastounioti A, Kontos D. Is it time to get rid of black boxes and cultivate trust in AI? Radiol Artif Intell. 2020;2(3):e200088.PubMedPubMedCentral
56.
57.
Buda M, Saha A, Walsh R, Ghate S, Li N, Święcicki A, Lo JY, Mazurowski MA. A data set and deep learning algorithm for the detection of masses and architectural distortions in digital breast tomosynthesis images. JAMA Netw Open. 2021;4(8):e2119100–e2119100.PubMedPubMedCentral
58.
Dembrower K, Lindholm P, Strand F. A multi-million mammography image dataset and population-based screening cohort for the training and evaluation of deep neural networks: the cohort of screen-aged women (CSAW). J Digit imaging. 2019;33:408–13.PubMedCentral
59.
Dench E, Bond-Smith D, Darcey E, Lee G, Aung YK, Chan A, Cuzick J, Ding ZY, Evans CF, Harvey J. Measurement challenge: protocol for international case–control comparison of mammographic measures that predict breast cancer risk. BMJ open. 2019;9(12):e031041.PubMedPubMedCentral
60.
Reyes M, Meier R, Pereira S, Silva CA, Dahlweid FM, von Tengg-Kobligk H, Summers RM, Wiest R. On the interpretability of artificial intelligence in radiology: challenges and opportunities. Radiol Artif Intell. 2020;2(3):e190043.PubMedPubMedCentral
61.
62.
Kaushal A, Altman R, Langlotz C. Geographic distribution of US cohorts used to train deep learning algorithms. JAMA. 2020;324(12):1212–3.PubMedPubMedCentral
63.
Hickman SE, Baxter GC, Gilbert FJ. Adoption of artificial intelligence in breast imaging: evaluation, ethical constraints and limitations. Br J Cancer. 2021;125:15–22.PubMedPubMedCentral
Metadata
Title
Artificial intelligence in mammographic phenotyping of breast cancer risk: a narrative review
Authors
Aimilia Gastounioti
Shyam Desai
Vinayak S. Ahluwalia
Emily F. Conant
Despina Kontos
Publication date
01-12-2022
Publisher
BioMed Central
Published in
Breast Cancer Research / Issue 1/2022
Electronic ISSN: 1465-542X
DOI
https://doi.org/10.1186/s13058-022-01509-z

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