New Frontiers in Breast Cancer Imaging: The Rise of AI
Abstract
:1. Introduction
2. Imaging Modalities and Their Advances
2.1. Mammography
2.1.1. Technique
2.1.2. Cancer Detection
2.1.3. Prognostic Factors
2.1.4. Risk Stratification
2.2. Ultrasound
2.2.1. Cancer Detection and Diagnosis
2.2.2. Prognostic Factors
2.2.3. Surgical Planning
2.3. MRI
2.3.1. Technique
2.3.2. Cancer Detection
2.3.3. Cancer Diagnosis–Lesion Characterization
2.3.4. Prognostic Factors
2.3.5. Risk Stratification
2.4. Other Relevant AI
Surgical Planning
3. Discussion
4. Conclusions
5. Future Directions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Topic | References |
---|---|
Mammography | |
Technique | [17,18] |
Cancer detection | [17,19,20,21,22,23,24,25,26,27,28,29,30,31] |
Prognosis | [6,12,13,32,33,34] |
Risk stratification | [10,35,36,37,38,39,40,41] |
Ultrasound | |
Cancer detection and diagnosis | [3,5,8,17,42,43,44,45,46] |
Prognosis | [47,48] |
Surgical planning | [15] |
MRI | |
Technique | [49,50] |
Cancer detection | [51,52,53] |
Cancer diagnosis—lesion characterization | [51,54] |
Prognosis | [55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72] |
Risk stratification | [11,73,74] |
Surgical planning | [15,75,76,77,78,79] |
AI Algorithm | Purpose | Techniques |
---|---|---|
Mammography | ||
Quality | Improve image acquisition by providing real-time feedback regarding position and quality control metrics and aggregate data to help establish trends between staff members | |
Detection | Detect areas that need to be addressed by a radiologist | Computer-aided detection (CAD) AI, deep convolutional neural networks (CNN) |
Prognostic factors | Automated estimate of fibroglandular tissue, which is correlated with breast cancer risk | |
Risk stratification | Predicts the upgrade rate of in situ cancer to invasive malignancy and predicts 5 year risk of developing breast cancer | CNN and radiomics |
Ultrasound | ||
Diagnosis | Provides decision support that ultimately improves accurate BI-RADS classification | CNN and an additional algorithm for classification |
Prognostic factors | Ultrasound features, such as triple-negative breast cancer, used to predict the risk of recurrence | Radiomics analysis |
Surgical planning | Assess vascular supply of the breast to determine the plausibility of reconstructive techniques | |
Magnetic Resonance Imaging (MRI) | ||
Technique | Accelerated image acquisition by improving signal processing and reducing image noise | Artificially filling k-space |
Diagnosis | Assess radiomic features extracted from contrast-enhanced T1-weighted and T2-weighted images | Machine-based learning |
Prognostic factors | Quantitative assessment of background parenchymal enhancement, which is a possible risk factor for breast cancer | CNN |
Risk stratification | Predicts the upgrade rate of in situ cancer to invasive malignancy |
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Share and Cite
Shamir, S.B.; Sasson, A.L.; Margolies, L.R.; Mendelson, D.S. New Frontiers in Breast Cancer Imaging: The Rise of AI. Bioengineering 2024, 11, 451. https://doi.org/10.3390/bioengineering11050451
Shamir SB, Sasson AL, Margolies LR, Mendelson DS. New Frontiers in Breast Cancer Imaging: The Rise of AI. Bioengineering. 2024; 11(5):451. https://doi.org/10.3390/bioengineering11050451
Chicago/Turabian StyleShamir, Stephanie B., Arielle L. Sasson, Laurie R. Margolies, and David S. Mendelson. 2024. "New Frontiers in Breast Cancer Imaging: The Rise of AI" Bioengineering 11, no. 5: 451. https://doi.org/10.3390/bioengineering11050451