Artificial Intelligence-Enhanced Quantitative Ultrasound for Breast Cancer: Pilot Study on Quantitative Parameters and Biopsy Outcomes
Abstract
:1. Introduction
2. Materials and Methods
2.1. Phase 1. Development of Algorithm with New Quantitative Ultrasound Parameters for Diagnosis of Breast Cancer
2.1.1. Quantitative Ultrasound Parameters
2.1.2. Development of Quantitative Imaging Algorithm
2.1.3. Implementation Details
2.1.4. Quantitative Ultrasound Measurement System
2.1.5. QUS Formula Using AC, SoS, ESD, and ESC
2.2. Phase 2: Using Completed Algorithm to Observe Patients with Real Breast Masses
2.2.1. Patient Enrollment
2.2.2. Imaging and Data Acquisition
2.2.3. Image Evaluation by Radiologist Readers
2.2.4. Image Confirmation by Biopsy
2.3. Outcomes
2.3.1. Primary Outcome
2.3.2. Secondary Outcomes
2.3.3. Statistical Analysis
3. Results
3.1. QUS Formula Using AC, SoS, ESD, and ESC
3.2. Using Completed Algorithm to Observe Patients with Real Breast Masses
3.2.1. Participant Characteristics
3.2.2. QUS Parameters According to the Final Diagnosis and Pathologic Outcome
3.3. Quantitative Ultrasound Parameters Help Identify Unclear Tumor Types in Traditional B-Mode Images
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Algorithm Optimization |
---|
Input: |
Initialize: |
1: For it in iterations do |
2: Split |
3: Composed train batch: L |
4: Calculate loss: |
5: Model Optimization = |
6: end for |
QUS Parameter | Odds Ratio | Standard Error | z | p-Value | 95% CI |
---|---|---|---|---|---|
AC | 41,734.12 | 144,309.9 | 3.08 | 0.002 | 47.55–3.66 × 107 |
SoS | 1.02 | 0.016 | 1.33 | 0.183 | 0.99–1.05 |
ESD | 0.91 | 0.032 | −2.70 | 0.007 | 0.85–0.97 |
ESC | 1.08 | 0.48 | 0.18 | 0.86 | 0.46–2.57 |
Benign, 32 (58) | Malignant, 23 (42) | Total, 55 (100) | |
---|---|---|---|
Age (mean ± SD) | 41.9 (1.8) | 53.8 (2.3) | |
Tumor size (mm), median (IQR) | 14 (10–17) | 20.5 (14–28) | |
BI-RADS category, n (%) | |||
1 | 1 (3) | 0 (0) | 1 (3) |
2 | 5 (14) | 0 (0) | 5 (14) |
3 | 5 (14) | 1 (3) | 6 (17) |
4 | 2 (6) | 1 (3) | 3 (8) |
5 | 0 (0) | 1 (3) | 1 (3) |
6 | 0 (0) | 20 (64) | 20 (64) |
Pathology outcome, n (%) | |||
IDC | 0 (0) | 18 (33) | 18 (33) |
DCIS | 0 (0) | 3 (5) | 3 (5) |
N/A | 32 (58) | 2 (4) | 34 (6) |
QUS Parameters | Benign, 32 (58) | Malignant, 23 (42) | p-Value |
---|---|---|---|
AC, median (IQR) | 0.506 (0.402–0.623) | 0.666 (0.609–0.731) | <0.001 |
SoS, median (IQR) | 1542 (1525–1558.5) | 1565 (1550–1579) | 0.002 |
ESD, median (IQR) | 91.53 (86.46–97.71) | 83.76 (74–90.6) | 0.001 |
ESC, median (IQR) | 2.396 (1.926–3.008) | 2.6 (1.987–3.733) | 0.16 |
IDC, 18 (86) | DCIS, 3 (14) | ||
AC, median (IQR) | 0.664 (0.616–0.73) | 0.682 (0.577–0.877) | … * |
SoS, median (IQR) | 1565 (1552–1577) | 1577 (1525–1579) | … |
ESD, median (IQR) | 83.2 (74–88.8) | 97 (90.33–101.9) | … |
ESC, median (IQR) | 2.684 (2.22–3.804) | 3.148 (2.525–3.733) | … |
BI-RADS Category | QUS Parameter | Benign, 7 | Malignant, 2 | ||||
---|---|---|---|---|---|---|---|
Lesion 1 | Lesion 2 | Lesion 3 | Lesion 4 | Lesion 5 | Lesion 6 | ||
3 | AC | 0.39 | 0.507 | 0.560 | 0.461 | 0.505 | 0.8061 |
SoS | 1508 | 1542 | 1492 | 1530 | 1557 | 1590 | |
ESD | 86.67 | 103 | 77.53 | 64.94 | 96.12 | 89.88 | |
ESC | 2.4 | 1.293 | 2.392 | 2.847 | 2.118 | 1.976 | |
Lesion 7 | Lesion 8 | Lesion 9 | |||||
4 | AC | 0.52 | 0.427 | 0.432 | |||
SoS | 1538 | 1507 | 1525 | ||||
ESD | 94 | 94.12 | 55.6 | ||||
ESC | 4.153 | 3.247 | 1.58 |
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Kwon, H.; Oh, S.; Kim, M.-G.; Kim, Y.; Jung, G.; Lee, H.-J.; Kim, S.-Y.; Bae, H.-M. Artificial Intelligence-Enhanced Quantitative Ultrasound for Breast Cancer: Pilot Study on Quantitative Parameters and Biopsy Outcomes. Diagnostics 2024, 14, 419. https://doi.org/10.3390/diagnostics14040419
Kwon H, Oh S, Kim M-G, Kim Y, Jung G, Lee H-J, Kim S-Y, Bae H-M. Artificial Intelligence-Enhanced Quantitative Ultrasound for Breast Cancer: Pilot Study on Quantitative Parameters and Biopsy Outcomes. Diagnostics. 2024; 14(4):419. https://doi.org/10.3390/diagnostics14040419
Chicago/Turabian StyleKwon, Hyuksool, Seokhwan Oh, Myeong-Gee Kim, Youngmin Kim, Guil Jung, Hyeon-Jik Lee, Sang-Yun Kim, and Hyeon-Min Bae. 2024. "Artificial Intelligence-Enhanced Quantitative Ultrasound for Breast Cancer: Pilot Study on Quantitative Parameters and Biopsy Outcomes" Diagnostics 14, no. 4: 419. https://doi.org/10.3390/diagnostics14040419
APA StyleKwon, H., Oh, S., Kim, M. -G., Kim, Y., Jung, G., Lee, H. -J., Kim, S. -Y., & Bae, H. -M. (2024). Artificial Intelligence-Enhanced Quantitative Ultrasound for Breast Cancer: Pilot Study on Quantitative Parameters and Biopsy Outcomes. Diagnostics, 14(4), 419. https://doi.org/10.3390/diagnostics14040419