The Application of Artificial Intelligence in Detecting Breast Lesions with Medical Imaging: A Literature Review

REVIEW
Salem Saeed Alghamdi

 
International Journal of Biomedicine. 2023;13(1):9-13.
DOI: 10.21103/Article13(1)_RA1
Originally published March 3, 2023

Abstract: 

Breast cancer is considered the most commonly diagnosed cancer among women worldwide. Several studies have shown that mammography screening could significantly decrease breast cancer mortality. Despite other screening modalities, such as MRI and ultrasound (US), mammography plays a vital role in detecting cancer and following up on it, due to its qualities and properties. The aim of this literature review is to look at recent studies that use AI with different medical imaging mammograms, MRI, and US, in detecting breast lesions.
A literature search was carried out using Google Scholar, Semantic Scholar, medRxiv, and PubMed databases for a period of the last four years. The search terms were "breast lesion," "breast imaging," and "breast cancer" combined with "machine learning," "deep learning," and "artificial intelligence." Among these studies, only the medical imaging related to breast lesions with AI was selected. A total of 25 articles were extracted from the following databases: 4 Google Scholar, 3 Semantic Scholar, 4 medRxiv, and 14 PubMed. Only papers related to breast lesions with medical imaging modalities were extracted, and all duplications were removed. In this study, the papers were reviewed by medical imaging professionals.
This literature review summarizes the most recent articles on utilizing AI in detecting breast lesions for different imaging modalities: mammogram, ultrasound, and MRI. Reviewed studies showed that AI performance in detecting lesions was significant, associated with high accuracy, sensitivity, and specificity for these modalities.

Keywords: 
artificial intelligence • convolution neural network • machine learning • neural network artificial
References: 

1.      Islami F, Guerra CE, Minihan A, Yabroff KR, Fedewa SA, Sloan K, Wiedt TL, Thomson B, Siegel RL, Nargis N, Winn RA, Lacasse L, Makaroff L, Daniels EC, Patel AV, Cance WG, Jemal A. American Cancer Society's report on the status of cancer disparities in the United States, 2021. CA Cancer J Clin. 2022 Mar;72(2):112-143. doi: 10.3322/caac.21703.
2.      Fusco R, Piccirillo A, Sansone M, Granata V, Rubulotta MR, Petrosino T, Barretta ML, Vallone P, Di Giacomo R, Esposito E, Di Bonito M, Petrillo A. Radiomics and Artificial Intelligence Analysis with Textural Metrics Extracted by Contrast-Enhanced Mammography in the Breast Lesions Classification. Diagnostics (Basel). 2021 Apr 30;11(5):815. doi: 10.3390/diagnostics11050815. 
3.      Kim HE, Kim HH, Han BK, Kim KH, Han K, Nam H, Lee EH, Kim EK. Changes in cancer detection and false-positive recall in mammography using artificial intelligence: a retrospective, multireader study. Lancet Digit Health. 2020 Mar;2(3):e138-e148. doi: 10.1016/S2589-7500(20)30003-0. 
4.      Patel BK, Lobbes MBI, Lewin J. Contrast Enhanced Spectral Mammography: A Review. Semin Ultrasound CT MR. 2018 Feb;39(1):70-79. doi: 10.1053/j.sult.2017.08.005.
5.      Majid AS, de Paredes ES, Doherty RD, Sharma NR, Salvador X. Missed breast carcinoma: pitfalls and pearls. Radiographics. 2003 Jul-Aug;23(4):881-95. doi: 10.1148/rg.234025083.
6.      Rawashdeh MA, Lee WB, Bourne RM, Ryan EA, Pietrzyk MW, Reed WM, Heard RC, Black DA, Brennan PC. Markers of good performance in mammography depend on number of annual readings. Radiology. 2013 Oct;269(1):61-7. doi: 10.1148/radiol.13122581.
7.      Lehman CD, Wellman RD, Buist DS, Kerlikowske K, Tosteson AN, Miglioretti DL; Breast Cancer Surveillance Consortium. Diagnostic Accuracy of Digital Screening Mammography With and Without Computer-Aided Detection. JAMA Intern Med. 2015 Nov;175(11):1828-37. doi: 10.1001/jamainternmed.2015.5231. 
8.      Giger ML, Chan HP, Boone J. Anniversary paper: History and status of CAD and quantitative image analysis: the role of Medical Physics and AAPM. Med Phys. 2008 Dec;35(12):5799-820. doi: 10.1118/1.3013555.
9.      Warren Burhenne LJ, Wood SA, D'Orsi CJ, Feig SA, Kopans DB, O'Shaughnessy KF, Sickles EA, Tabar L, Vyborny CJ, Castellino RA. Potential contribution of computer-aided detection to the sensitivity of screening mammography. Radiology. 2000 May;215(2):554-62. doi: 10.1148/radiology.215.2.r00ma15554. Erratum in: Radiology 2000 Jul;216(1):306. 
10.    Freer TW, Ulissey MJ. Screening mammography with computer-aided detection: prospective study of 12,860 patients in a community breast center. Radiology. 2001 Sep;220(3):781-6. doi: 10.1148/radiol.2203001282. 
11.    Fenton JJ, Taplin SH, Carney PA, Abraham L, Sickles EA, D'Orsi C, Berns EA, Cutter G, Hendrick RE, Barlow WE, Elmore JG. Influence of computer-aided detection on performance of screening mammography. N Engl J Med. 2007 Apr 5;356(14):1399-409. doi: 10.1056/NEJMoa066099.
12.    James JJ, Gilbert FJ, Wallis MG, Gillan MG, Astley SM, Boggis CR, Agbaje OF, Brentnall AR, Duffy SW. Mammographic features of breast cancers at single reading with computer-aided detection and at double reading in a large multicenter prospective trial of computer-aided detection: CADET II. Radiology. 2010 Aug;256(2):379-86. doi: 10.1148/radiol.10091899. 
13.    Ehteshami Bejnordi B, Veta M, Johannes van Diest P, van Ginneken B, Karssemeijer N, Litjens G, van der Laak JAWM; the CAMELYON16 Consortium, Hermsen M, Manson QF, Balkenhol M, Geessink O, Stathonikos N, van Dijk MC, et al. Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer. JAMA. 2017 Dec 12;318(22):2199-2210. doi: 10.1001/jama.2017.14585. 
14.    Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, et al. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA. 2016 Dec 13;316(22):2402-2410. doi: 10.1001/jama.2016.17216. 
15.    Kooi T, Litjens G, van Ginneken B, Gubern-Mérida A, Sánchez CI, Mann R, et al. Large scale deep learning for computer aided detection of mammographic lesions. Med Image Anal. 2017 Jan;35:303-312. doi: 10.1016/j.media.2016.07.007. 
16.    Rodríguez-Ruiz A, Krupinski E, Mordang JJ, Schilling K, Heywang-Köbrunner SH, Sechopoulos I, Mann RM. Detection of Breast Cancer with Mammography: Effect of an Artificial Intelligence Support System. Radiology. 2019 Feb;290(2):305-314. doi: 10.1148/radiol.2018181371. 
17.    Shen L, Margolies LR, Rothstein JH, Fluder E, McBride R, Sieh W. Deep Learning to Improve Breast Cancer Detection on Screening Mammography. Sci Rep. 2019 Aug 29;9(1):12495. doi: 10.1038/s41598-019-48995-4.
18.    Pacilè S, Lopez J, Chone P, Bertinotti T, Grouin JM, Fillard P. Improving Breast Cancer Detection Accuracy of Mammography with the Concurrent Use of an Artificial Intelligence Tool. Radiol Artif Intell. 2020 Nov 4;2(6):e190208. doi: 10.1148/ryai.2020190208.
19.    Rodriguez-Ruiz A, Lång K, Gubern-Merida A, Broeders M, Gennaro G, Clauser P, Helbich TH, Chevalier M, Tan T, Mertelmeier T, Wallis MG, Andersson I, Zackrisson S, Mann RM, Sechopoulos I. Stand-Alone Artificial Intelligence for Breast Cancer Detection in Mammography: Comparison With 101 Radiologists. J Natl Cancer Inst. 2019 Sep 1;111(9):916-922. doi: 10.1093/jnci/djy222.
20.    Frazer HM, Qin AK, Pan H, Brotchie P. Evaluation of deep learning-based artificial intelligence techniques for breast cancer detection on mammograms: Results from a retrospective study using a BreastScreen Victoria dataset. J Med Imaging Radiat Oncol. 2021 Aug;65(5):529-537. doi: 10.1111/1754-9485.13278.
21.    Arasu VA, Habel LA, Achacoso NS, Buist DSM, Cord JB, Esserman LJ, et al. Comparison of Mammography Artificial Intelligence Algorithms for 5-year Breast Cancer Risk Prediction. medRxiv 2022.01.05.22268746; doi: 10.1101/2022.01.05.22268746
22.    Kerschke L, Weigel S, Rodriguez-Ruiz A, Karssemeijer N, Heindel W. Using deep learning to assist readers during the arbitration process: a lesion-based retrospective evaluation of breast cancer screening performance. Eur Radiol. 2022 Feb;32(2):842-852. doi: 10.1007/s00330-021-08217-w. 
23.    van Winkel SL, Rodríguez-Ruiz A, Appelman L, Gubern-Mérida A, Karssemeijer N, Teuwen J, Wanders AJT, Sechopoulos I, Mann RM. Impact of artificial intelligence support on accuracy and reading time in breast tomosynthesis image interpretation: a multi-reader multi-case study. Eur Radiol. 2021 Nov;31(11):8682-8691. doi: 10.1007/s00330-021-07992-w.
24.    Mansour S, Kamal R, Hashem L, AlKalaawy B. Can artificial intelligence replace ultrasound as a complementary tool to mammogram for the diagnosis of the breast cancer? Br J Radiol. 2021 Dec;94(1128):20210820. doi: 10.1259/bjr.20210820.
25.    Rodríguez-Ruiz A, Krupinski E, Mordang JJ, Schilling K, Heywang-Köbrunner SH, Sechopoulos I, Mann RM. Detection of Breast Cancer with Mammography: Effect of an Artificial Intelligence Support System. Radiology. 2019 Feb;290(2):305-314. doi: 10.1148/radiol.2018181371. 
26.    Interlenghi M, Salvatore C, Magni V, Caldara G, Schiavon E, Cozzi A, Schiaffino S, Carbonaro LA, Castiglioni I, Sardanelli F. A Machine Learning Ensemble Based on Radiomics to Predict BI-RADS Category and Reduce the Biopsy Rate of Ultrasound-Detected Suspicious Breast Masses. Diagnostics (Basel). 2022 Jan 13;12(1):187. doi: 10.3390/diagnostics12010187.
27.    Shen Y, Shamout FE, Oliver JR, Witowski J, Kannan K, Park J, et al. Artificial intelligence system reduces false-positive findings in the interpretation of breast ultrasound exams. Nat Commun. 2021 Sep 24;12(1):5645. doi: 10.1038/s41467-021-26023-2.
28.    Xia Q, Cheng Y, Hu J, Huang J, Yu Y, Xie H, Wang J. Differential diagnosis of breast cancer assisted by S-Detect artificial intelligence system. Math Biosci Eng. 2021 Apr 27;18(4):3680-3689. doi: 10.3934/mbe.2021184.
29.    Gao Y, Liu B, Zhu Y, Chen L, Tan M, Xiao X, Yu G, Guo Y. Detection and recognition of ultrasound breast nodules based on semi-supervised deep learning: a powerful alternative strategy. Quant Imaging Med Surg. 2021 Jun;11(6):2265-2278. doi: 10.21037/qims-20-12b.
30.    Lyu SY, Zhang Y, Zhang MW, Zhang BS, Gao LB, Bai LT, Wang J. Diagnostic value of artificial intelligence automatic detection systems for breast BI-RADS 4 nodules. World J Clin Cases. 2022 Jan 14;10(2):518-527. doi: 10.12998/wjcc.v10.i2.518.
31.    PhD XL, Xu M, Tang G, PhD YW, Wang N, PhD DN, PhD XL, Li AH. The lesion detection efficacy of deep learning on automatic breast ultrasound and factors affecting its efficacy: a pilot study. Br J Radiol. 2022 Feb 1;95(1130):20210438. doi: 10.1259/bjr.20210438. 
32.    Mango VL, Sun M, Wynn RT, Ha R. Should We Ignore, Follow, or Biopsy? Impact of Artificial Intelligence Decision Support on Breast Ultrasound Lesion Assessment. AJR Am J Roentgenol. 2020 Jun;214(6):1445-1452. doi: 10.2214/AJR.19.21872. 
33.    Zhou LQ, Wu XL, Huang SY, Wu GG, Ye HR, Wei Q, Bao LY, Deng YB, Li XR, Cui XW, Dietrich CF. Lymph Node Metastasis Prediction from Primary Breast Cancer US Images Using Deep Learning. Radiology. 2020 Jan;294(1):19-28. doi: 10.1148/radiol.2019190372. 
34.    Zhou J, Zhang Y, Chang KT, Lee KE, Wang O, Li J, Lin Y, Pan Z, Chang P, Chow D, Wang M, Su MY. Diagnosis of Benign and Malignant Breast Lesions on DCE-MRI by Using Radiomics and Deep Learning With Consideration of Peritumor Tissue. J Magn Reson Imaging. 2020 Mar;51(3):798-809. doi: 10.1002/jmri.26981.
35.    Dalmiş MU, Gubern-Mérida A, Vreemann S, Bult P, Karssemeijer N, Mann R, Teuwen J. Artificial Intelligence-Based Classification of Breast Lesions Imaged With a Multiparametric Breast MRI Protocol With Ultrafast DCE-MRI, T2, and DWI. Invest Radiol. 2019 Jun;54(6):325-332. doi: 10.1097/RLI.0000000000000544.
36.    Adachi M, Fujioka T, Mori M, Kubota K, Kikuchi Y, Xiaotong W, Oyama J, Kimura K, Oda G, Nakagawa T, Uetake H, Tateishi U. Detection and Diagnosis of Breast Cancer Using Artificial Intelligence Based assessment of Maximum Intensity Projection Dynamic Contrast-Enhanced Magnetic Resonance Images. Diagnostics (Basel). 2020 May 20;10(5):330. doi: 10.3390/diagnostics10050330. 
37.    Herent P, Schmauch B, Jehanno P, Dehaene O, Saillard C, Balleyguier C, Arfi-Rouche J, Jégou S. Detection and characterization of MRI breast lesions using deep learning. Diagn Interv Imaging. 2019 Apr;100(4):219-225. doi: 10.1016/j.diii.2019.02.008.
38.    Jiang Y, Edwards AV, Newstead GM. Artificial Intelligence Applied to Breast MRI for Improved Diagnosis. Radiology. 2021 Jan;298(1):38-46. doi: 10.1148/radiol.2020200292. 

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Received November 19, 2022.
Accepted December 12, 2022.
©2023 International Medical Research and Development Corporation.