Breast cancer is the most common malignant tumor among women worldwide, with its incidence and mortality ranking first among all cancers. Early diagnosis and treatment significantly improve prognosis and reduce disease-related mortality. Chest computed tomography (CT), a routine examination for physical assessments and hospitalized patients, can screen for the presence of breast nodules and provide an initial assessment of malignancy risk. In recent years, artificial intelligence (AI) has advanced rapidly in the medical field. Studies have demonstrated that the sensitivity and accuracy of chest CT in diagnosing breast cancer are enhanced through the application of AI methods. This article explores the research progress in breast cancer diagnosis utilizing artificial intelligence based on chest CT examinations.
Hussain A, Gordon-Dixon A, Almusawy H, et al., 2010, The Incidence and Outcome of Incidental Breast Lesions Detected by Computed Tomography. Ann R Coll Surg Engl, 92(2): 124–126. https://doi.org/10.1308/003588410X12518836439083
Yuan Y, Yang F, Wang Y, et al., 2021, Factors Associated with Liver Cancer Prognosis After Hepatectomy: A Retrospective Cohort Study. Medicine (Baltimore), 100(42): e27378. https://doi.org/10.1097/MD.0000000000027378
Koh J, Yoon Y, Kim S, et al., 2022, Deep Learning for the Detection of Breast Cancers on Chest Computed Tomography. Clin Breast Cancer, 22(1): 26–31. https://doi.org/10.1016/j.clbc.2021.04.015
Bin Saeedan M, Mobara M, Arafah MA, et al., 2015, Breast Lesions on Chest Computed Tomography: Pictorial Review with Mammography and Ultrasound Correlation. Curr Probl Diagn Radiol, 44(2): 144–154. https://doi.org/10.1067/j.cpradiol.2014.09.002
Sharpe RE Jr, Levin DC, Parker L, et al., 2013, The Effect of the Controversial US Preventive Services Task Force Recommendations on the Use of Screening Mammography. J Am Coll Radiol, 10(1): 21–24. https://doi.org/10.1016/j.jacr.2012.07.008
Broder J, Warshauer DM, 2006, Increasing Utilization of Computed Tomography in the Adult Emergency Department, 2000–2005. Emerg Radiol, 13(1): 25–30. https://doi.org/10.1007/s10140-006-0493-9
Eberth JM, Qiu R, Adams SA, et al., 2014, Lung Cancer Screening Using Low-Dose CT: The Current National Landscape. Lung Cancer, 85(3): 379–384. https://doi.org/10.1016/j.lungcan.2014.07.002
Oeffinger KC, Fontham ET, Etzioni R, et al., 2015, Breast Cancer Screening for Women at Average Risk: 2015 Guideline Update From the American Cancer Society. JAMA, 314(15): 1599–1614. https://doi.org/10.1001/jama.2015.12783. Erratum in JAMA, 315(13): 1406. https://doi.org/10.1001/jama.2016.3404
US Preventive Services Task Force, 2009, Screening for Breast Cancer: U.S. Preventive Services Task Force Recommendation Statement. Ann Intern Med, 151(10): 716–26, W-236. https://doi.org/10.7326/0003-4819-151-10-200911170-00008. Erratum in Ann Intern Med, 152(3): 199–200. Erratum in Ann Intern Med, 152(10): 688.
Swensen SJ, Jett JR, Sloan JA, et al., 2002, Screening for Lung Cancer with Low-Dose Spiral Computed Tomography. Am J Respir Crit Care Med, 165(4): 508–513. https://doi.org/10.1164/ajrccm.165.4.2107006
Monzawa S, Washio T, Yasuoka R, et al., 2013, Incidental Detection of Clinically Unexpected Breast Lesions by Computed Tomography. Acta Radiol, 54(4): 374–379. https://doi.org/10.1177/0284185113475607
Poyraz N, Emlik GD, Keskin S, et al., 2015, Incidental Breast Lesions Detected on Computed Thorax Tomography. J Breast Health, 11(4): 163–167. https://doi.org/10.5152/tjbh.2015.2656
Lin YP, Hsu HH, Ko KH, et al., 2016, Differentiation of Malignant and Benign Incidental Breast Lesions Detected by Chest Multidetector-Row Computed Tomography: Added Value of Quantitative Enhancement Analysis. PLoS One, 11(4): e0154569. https://doi.org/10.1371/journal.pone.0154569
Parvaiz MA, Isgar B, 2013, Incidental Breast Lesions Detected on Diagnostic CT Scans: A 4-Year Prospective Study. Breast J, 19(4): 457–459. https://doi.org/10.1111/tbj.12142
Salvatore M, Margolies L, Kale M, et al., 2014, Breast Density: Comparison of Chest CT with Mammography. Radiology, 270(1): 67–73. https://doi.org/10.1148/radiol.13130733
Chetlen A, Mack J, Chan T, 2016, Breast Cancer Screening Controversies: Who, When, Why, and How? Clin Imaging, 40(2): 279–282. https://doi.org/10.1016/j.clinimag.2015.05.017
Webb ML, Cady B, Michaelson JS, et al., 2014, A Failure Analysis of Invasive Breast Cancer: Most Deaths from Disease Occur in Women Not Regularly Screened. Cancer, 120(18): 2839–2846. https://doi.org/10.1002/cncr.28199
Agliata MF, Calabrò N, Tricca S, et al., 2023, Mammary Nodules as Incidental Findings on Chest Computed Tomography: A Retrospective Analysis on Their Frequency and Predictive Value. Radiol Med, 128(8): 912–921. https://doi.org/10.1007/s11547-023-01670-1
Gu D, Su K, Zhao H, 2020, A Case-Based Ensemble Learning System for Explainable Breast Cancer Recurrence Prediction. Artif Intell Med, 107: 101858. https://doi.org/10.1016/j.artmed.2020.101858
Lambin P, Rios-Velazquez E, Leijenaar R, et al., 2012, Radiomics: Extracting More Information from Medical Images Using Advanced Feature Analysis. Eur J Cancer, 48(4): 441–446. https://doi.org/10.1016/j.ejca.2011.11.036
Valdora F, Houssami N, Rossi F, et al., 2018, Rapid Review: Radiomics and Breast Cancer. Breast Cancer Res Treat, 169(2): 217–229. https://doi.org/10.1007/s10549-018-4675-4
Gillies RJ, Kinahan PE, Hricak H, 2016, Radiomics: Images Are More than Pictures, They Are Data. Radiology, 278(2): 563–577. https://doi.org/10.1148/radiol.2015151169
Avanzo M, Stancanello J, El Naqa I, 2017, Beyond Imaging: The Promise of Radiomics. Phys Med, 38: 122–139. https://doi.org/10.1016/j.ejmp.2017.05.071
Feng Q, Hu Q, Liu Y, et al., 2020, Diagnosis of Triple Negative Breast Cancer Based on Radiomics Signatures Extracted from Preoperative Contrast-Enhanced Chest Computed Tomography. BMC Cancer, 20(1): 579. https://doi.org/10.1186/s12885-020-07053-3
Liu Q, Liu W, Yang J, et al., 2020, Pre-Academic Prediction of Axillary Lymph Node Metastasis in Breast Cancer by CT Imaging Group. China Medical Equipment, 35(9): 88–92.
Sechopoulos I, Teuwen J, Mann R, 2021, Artificial Intelligence for Breast Cancer Detection in Mammography and Digital Breast Tomosynthesis: State of the Art. Semin Cancer Biol, 72: 214–225. https://doi.org/10.1016/j.semcancer.2020.06.002
Mahmood T, Arsalan M, Owais M, et al., 2020, Artificial Intelligence-Based Mitosis Detection in Breast Cancer Histopathology Images Using Faster R-CNN and Deep CNNs. J Clin Med, 9(3): 749. https://doi.org/10.3390/jcm9030749
Mazurowski MA, Buda M, Saha A, et al., 2019, Deep Learning in Radiology: An Overview of the Concepts and a Survey of the State of the Art with Focus on MRI. J Magn Reson Imaging, 49(4): 939–954. https://doi.org/10.1002/jmri.26534
Chartrand G, Cheng PM, Vorontsov E, et al., 2017, Deep Learning: A Primer for Radiologists. Radiographics, 37(7): 2113–2131. https://doi.org/10.1148/rg.2017170077
Yang X, Wu L, Ye W, et al., 2020, Deep Learning Signature Based on Staging CT for Preoperative Prediction of Sentinel Lymph Node Metastasis in Breast Cancer. Acad Radiol, 27(9): 1226–1233. https://doi.org/10.1016/j.acra.2019.11.007