1. Mann L, Ranjan Nayak S. Recent advances on mammogram imaging for breast cancer analysis: A technological review. In: Das AK, Nayak J, Naik B, Dutta S, Pelusi D, editors. Computational Intelligence in Pattern Recognition. Berlin: Springer; 2022. [DOI:10.1007/978-981-16-2543-5_46] [
DOI:10.1007/978-981-16-2543-5_46]
2. Altameem A, Mahanty C, Poonia RC, Saudagar AKJ, Kumar R. Breast cancer detection in mammography images using deep convolutional neural networks and fuzzy ensemble modeling techniques. Diagnostics. 2022; 12(8):1812. [DOI:10.3390/diagnostics12081812] [PMID] [PMCID] [
DOI:10.3390/diagnostics12081812]
3. National Cancer Institute. Cancer statistics [Internet]. 2021 [Updated 2021 December 15]. Available from: [Link]
4. Hawkes N. Cancer survival data emphasise importance of early diagnosis. BMJ. 2019; 364:l408. [DOI:10.1136/bmj.1408] [
DOI:10.1136/bmj.l408]
5. Tagliafico AS, Bignotti B, Rossi F, Signori A, Sormani MP, Valdora F, et al. Diagnostic performance of contrast-enhanced spectral mammography: Systematic review and meta-analysis. Breast. 2016; 28:13-9. [DOI:10.1016/j.breast.2016.04.008] [PMID] [
DOI:10.1016/j.breast.2016.04.008]
6. Daniaux M, De Zordo T, Santner W, Amort B, Koppelstätter F, Jaschke W, et al Dual-energy contrast-enhanced spectral mammography (CESM). Arch Gynecol Obstet. 2015; 292(4):739-47. [DOI:10.1007/s00404-015-3693-2] [PMID] [
DOI:10.1007/s00404-015-3693-2]
7. Heck L, Dierolf M, Jud C, Eggl E, Sellerer T, Mechlem K, et al. Contrast-enhanced spectral mammography with a compact synchrotron source. Plos One. 2019; 14(10):e0222816. [DOI:10.1371/journal.pone.0222816] [PMID] [PMCID] [
DOI:10.1371/journal.pone.0222816]
8. Cheung YC, Lin YC, Wan YL, Yeow KM, Huang PC, Lo YF, et al. Diagnostic performance of dual-energy contrast-enhanced subtracted mammography in dense breasts compared to mammography alone: Interobserver blind-reading analysis. Eur Radiol. 2014; 24(10):2394-403. [DOI:10.1007/s00330-014-3271-1] [PMID] [
DOI:10.1007/s00330-014-3271-1]
9. Fallenberg EM, Schmitzberger FF, Amer H, Ingold-Heppner B, Balleyguier C, Diekmann F, et al. Contrast-enhanced spectral mammography vs. mammography and MRI - clinical performance in a multi-reader evaluation. Eur Radiol. 2017; 27(7):2752-64. [DOI:10.1007/s00330-016-4650-6] [PMID] [
DOI:10.1007/s00330-016-4650-6]
10. Mercado CL. BI-RADS update. Radiol Clin North Am. 2014; 52(3):481-7. [DOI:10.1016/j.rcl.2014.02.008] [PMID] [
DOI:10.1016/j.rcl.2014.02.008]
11. Zhang R, Wei W, Li R, Li J, Zhou Z, Ma M, et al. An MRI-based radiomics model for predicting the benignity and malignancy of BI-RADS 4 breast lesions. Front Oncol. 2022; 11:733260. [DOI:10.3389/fonc.2021.733260] [PMID] [PMCID] [
DOI:10.3389/fonc.2021.733260]
12. Danala G, Patel B, Aghaei F, Heidari M, Li J, Wu T, et al. Classification of breast masses using a computer-aided diagnosis scheme of contrast enhanced digital mammograms. Ann Biomed Eng. 2018; 46(9):1419-31. [DOI:10.1007/s10439-018-2044-4] [PMID] [PMCID] [
DOI:10.1007/s10439-018-2044-4]
13. Lopez-Almazan H, Javier Pérez-Benito F, Larroza A, Perez-Cortes JC, Pollan M, Perez-Gomez B, et al. A deep learning framework to classify breast density with noisy labels regularization. Comput Methods Programs Biomed. 2022; 221:106885. [DOI:10.1016/j.cmpb.2022.106885] [PMID] [
DOI:10.1016/j.cmpb.2022.106885]
14. Elezaby M, Li G, Bhargavan-Chatfield M, Burnside ES, DeMartini WB. ACR BI-RADS assessment category 4 subdivisions in diagnostic mammography: Utilization and outcomes in the national mammography database. Radiology. 2018; 287(2):416-22. [DOI:10.1148/radiol.2017170770] [PMID] [PMCID] [
DOI:10.1148/radiol.2017170770]
15. Kuhl CK. Abbreviated magnetic resonance imaging (MRI) for breast cancer screening: Rationale, concept, and transfer to clinical practice. Annu Rev Med. 2019; 70:501-19. [DOI:10.1146/annurev-med-121417-100403] [PMID] [
DOI:10.1146/annurev-med-121417-100403]
16. Hamidinekoo A, Denton E, Rampun A, Honnor K, Zwiggelaar R. Deep learning in mammography and breast histology, an overview and future trends. Med Image Anal. 2018; 47:45-67. [DOI:10.1016/j.media.2018.03.006] [PMID] [
DOI:10.1016/j.media.2018.03.006]
17. Zheng X, Yao Z, Huang Y, Yu Y, Wang Y, Liu Y, et al. Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer. Nat Commun. 2020; 11(1):1236. [DOI:10.1038/s41467-020-15027-z] [PMID] [PMCID] [
DOI:10.1038/s41467-020-15027-z]
18. Hinton GE, Srivastava N, Krizhevsky A, Sutskever I, Salakhutdinov RR. Improving neural networks by preventing co-adaptation of feature detectors. arXiv. 2012. [Unpublished]. [DOI:10.48550/arXiv.1207.0580]
19. Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv. 2014. [Unpublished]. [DOI:10.48550/arXiv.1409.1556]
20. Ehteshami Bejnordi B, Mullooly M, Pfeiffer RM, Fan S, Vacek PM, Weaver DL, et al. Using deep convolutional neural networks to identify and classify tumor-associated stroma in diagnostic breast biopsies. Mod Pathol. 2018; 31(10):1502-12. [DOI:10.1038/s41379-018-0073-z] [PMID] [PMCID] [
DOI:10.1038/s41379-018-0073-z]
21. Liu H, Chen Y, Zhang Y, Wang L, Luo R, Wu H, et al. A deep learning model integrating mammography and clinical factors facilitates the malignancy prediction of BI-RADS 4 microcalcifications in breast cancer screening. Eur Radiol. 2021; 31(8):5902-12. [DOI:10.1007/s00330-020-07659-y] [PMID] [
DOI:10.1007/s00330-020-07659-y]
22. Long R, Cao K, Cao M, Li XT, Gao F, Zhang FD, et al. Improving the diagnostic accuracy of breast bi-rads 4 microcalcification-only lesions using contrast-enhanced mammography. Clin Breast Cancer. 2021; 21(3):256-62. [DOI:10.1016/j.clbc.2020.10.011] [PMID] [
DOI:10.1016/j.clbc.2020.10.011]
23. Song J, Zheng Y, Zakir Ullah M, Wang J, Jiang Y, Xu C, et al. Multiview multimodal network for breast cancer diagnosis in contrast-enhanced spectral mammography images. Int J Comput Assist Radiol Surg. 2021; 16(6):979-88. [DOI:10.1007/s11548-021-02391-4] [PMID] [
DOI:10.1007/s11548-021-02391-4]
24. Patel BK, Ranjbar S, Wu T, Pockaj BA, Li J, Zhang N, et al. Computer-aided diagnosis of contrast-enhanced spectral mammography: A feasibility study. Eur J Radiol. 2018; 98:207-13. [DOI:10.1016/j.ejrad.2017.11.024] [PMID] [
DOI:10.1016/j.ejrad.2017.11.024]
25. Fanizzi A, Losurdo L, Basile TMA, Bellotti R, Bottigli U, Delogu P, et al. Fully automated support system for diagnosis of breast cancer in contrast-enhanced spectral mammography images. J Clin Med. 2019; 8(6):891. [DOI:10.3390/jcm8060891] [PMID] [PMCID] [
DOI:10.3390/jcm8060891]
26. Dominique C, Callonnec F, Berghian A, Defta D, Vera P, Modzelewski R, et al. Deep learning analysis of contrast-enhanced spectral mammography to determine histoprognostic factors of malignant breast tumours. Eur Radiol. 2022; 32(7):4834-44. [DOI:10.1007/s00330-022-08538-4] [PMID] [PMCID] [
DOI:10.1007/s00330-022-08538-4]
27. Gao F, Wu T, Li J, Zheng B, Ruan L, Shang D, et al. SD-CNN: A shallow-deep CNN for improved breast cancer diagnosis. Comput Med Imaging Graph. 2018; 70:53-62. [DOI:10.1016/j.compmedimag.2018.09.004] [PMID] [
DOI:10.1016/j.compmedimag.2018.09.004]
28. Khaled R, Helal M, Alfarghaly O, Mokhtar O, Elkorany A, El Kassas H, et al. Categorized contrast enhanced mammography dataset for diagnostic and artificial intelligence research. Sci Data. 2022; 9(1):122. [DOI:10.1038/s41597-022-01238-0] [PMID] [PMCID] [
DOI:10.1038/s41597-022-01238-0]
29. Khaled R, Helal M, Alfarghaly O, Mokhtar O, Elkorany A, El Kassas H, et al. Categorized digital database for low energy and subtracted contrast enhanced spectral mammography images (CDD-CESM). Cancer Imaging Arch. 2021. [DOI:10.7937/29kw-ae92]
30. Perek S, Kiryati N, Zimmerman-Moreno G, Sklair-Levy M, Konen E, Mayer A. Classification of contrast-enhanced spectral mammography (CESM) images. Int J Comput Assist Radiol Surg. 2019; 14(2):249-57. [DOI:10.1007/s11548-018-1876-6] [PMID] [
DOI:10.1007/s11548-018-1876-6]