Deep Learning Approaches with Digital Mammography for Evaluating Breast Cancer Risk, a Narrative Review
Abstract
:1. Introduction
1.1. Screening Guidelines
1.2. Cancer Risk Models
1.3. Imaging Features for Risk Evaluation
1.4. AI and Risk Assessment
2. Methods
3. Study Selection
3.1. Small Scale Studies
3.2. Towards Clinical Validation
3.3. Novel Applications of DL Models beyond Screening
4. Discussion
4.1. Screening Implications
4.2. Summary and Future Direction
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Exams | Patients | Metric | Method | |
---|---|---|---|---|
Arefan, 2020 [20] | 226 | 226 (113) | 0.73 AUC |
8 Layer Inception CNN |
Gastounioti, 2018 [24] | 424 | 424 (106) | 0.90 AUC |
2 Layer CNN with CTA |
Li, 2017 [25] | 456 | 456 (75) | 0.84 AUC | 8 Layer CNN |
Ha, 2019 [30] | 1474 | 737 (210) | 0.72 Accuracy |
21 Layer Resnet CNN |
Kallenberg, 2016 [21] | 2069 | 2069 (394) | 0.57 AUC | 4 Layer CNN |
Zhu, 2021 [28] | 6369 | 6369 (278) | 0.72 C-index | 4 Layer CNN |
Dembrowser, 2020 [16] | 14,034 | 2283 (278) | 0.65 AUC | Inceptionv2 CNN+RF |
Michel, 2023 [23] | 23,467 (121) | 0.654 AUC |
21 Layer Resnet CNN | |
McKinney, 2020 [31] | 28,953 (1100) | 0.889 AUC | DL model | |
Yala, 2019 [18] | 88,994 | 39,571 | 0.70 AUC | 19 Layer Resnet CNN with transformers +RF |
Wanders, 2018 [26] | 51,400 (898) | 51,400 (898) | 0.61 C-index | 3 Layer CNN |
Lehman, 2022 [27] | 119,139 | 57,635 | 0.68 AUC | 19 Layer Resnet CNN with transformers +RF |
Yala, 2022 [29] | 19 Layer Resnet CNN with transformers +RF | |||
MGH | 25,855 (588) | 7005 (233) | 0.75 C-index | |
Novant | 14,157 (235) | 5887 (123) | 0.75 C-index | |
Emory | 44,008 (1003) | 16,495 (495) | 0.77 C-index | |
Maccabi Assuta | 6187 (186) | 6189 (186) | 0.77 C-index | |
Karolinska | 19,328 (1413) | 7353 (799) | 0.81 C-index | |
CGMH | 13,356 (244) | 13,356 (244) | 0.79 C-index | |
Barretos | 5900 (146) | 5900 (146) | 0.84 C-index |
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Siddique, M.; Liu, M.; Duong, P.; Jambawalikar, S.; Ha, R. Deep Learning Approaches with Digital Mammography for Evaluating Breast Cancer Risk, a Narrative Review. Tomography 2023, 9, 1110-1119. https://doi.org/10.3390/tomography9030091
Siddique M, Liu M, Duong P, Jambawalikar S, Ha R. Deep Learning Approaches with Digital Mammography for Evaluating Breast Cancer Risk, a Narrative Review. Tomography. 2023; 9(3):1110-1119. https://doi.org/10.3390/tomography9030091
Chicago/Turabian StyleSiddique, Maham, Michael Liu, Phuong Duong, Sachin Jambawalikar, and Richard Ha. 2023. "Deep Learning Approaches with Digital Mammography for Evaluating Breast Cancer Risk, a Narrative Review" Tomography 9, no. 3: 1110-1119. https://doi.org/10.3390/tomography9030091