Volume 17 -                   Qom Univ Med Sci J 2023, 17 - : 334-345 | Back to browse issues page


XML Persian Abstract Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Achak A, Hedyehzadeh M. Assessing the Efficiency of Deep Learning Methods in Estimating the Malignancy of Bi-Rads 4 Breast Lesions Using Contrast-enhanced Spectral Mammography Images. Qom Univ Med Sci J 2023; 17 : 2756.1
URL: http://journal.muq.ac.ir/article-1-3600-en.html
1- Department of Biomedical Engineering, Dezful Branch, Islamic Azad University, Dezful, Iran.
2- Young Researchers and Elite Club, Dezful Branch, Islamic Azad University, Dezful, Iran. , Mrhedyehzadeh@iaud.ac.ir
Abstract:   (679 Views)
Background and Objectives: According to the Breast Imaging-Reporting and Data System (BI-RADS), category 4 breast lesions have a 2-95% probability of malignancy. Such estimation can cause challenges in planning for the treatment of women with breast cancer. Contrast-enhanced spectral mammography (CESM) is one of the best imaging modalities in breast cancer detection. In this study, we aim to assess the efficiency of deep learning methods in determining the malignancy degree of BI-RADS 4 breast lesions using CESM images.
Methods: In this study, 1408 CESM images of BI-RADS 4 breast lesions were used. The image pre-processing step was first done to remove noises and improve image quality. Then, segmentation was done for the region of interest extraction. Feature extraction was done using three different conventional classifiers. Finally, the classification of images was done using deep learning methods.
Results: Among the applied methods, the Densenet-201 network used for feature extraction and K-nearest neighbor (KNN) used for Classification showed the best results with accuracy, sensitivity, specificity, and area under the curve of 98.57%, 99.20%, 97.50% and 0.987 respectively.
Conclusion: The proposed method (Densenet-201 and KNN) using CESM images is effective in estimating the malignancy of BI-RADS 4 breast lesions and thus in timely treatment of breast cancer.
Article number: 2756.1
Full-Text [PDF 3892 kb]   (347 Downloads) |   |   Full-Text (HTML)  (425 Views)  
Type of Study: Original Article | Subject: زنان
Received: 2022/11/4 | Accepted: 2023/01/29 | Published: 2023/08/1

References
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]

Add your comments about this article : Your username or Email:
CAPTCHA

Send email to the article author


Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

© 2025 CC BY-NC 4.0 | Qom University of Medical Sciences Journal

Designed & Developed by : Yektaweb