Tissue Classification of Breast Cancer by Hyperspectral Unmixing
Abstract
:Simple Summary
Abstract
1. Introduction
2. Materials and Methods
2.1. Study Design
2.2. Hyperspectral Imaging
2.3. Pipeline to Assign Ground-Truth Labels
2.3.1. Reference Image
2.3.2. Pathology Processing
2.3.3. Assigning Ground-Truth Labels with Hyperspectral Unmixing
Algorithm 1 Label assignment based on hyperspectral unmixing |
Input: Training samples with labels at locations P Output: Certain Labeled representative spectra Ł
|
2.4. Tissue Classification
Performance Testing
3. Results
3.1. Dataset Description
3.2. Assigning Ground-Truth Labels with Hyperspectral Unmixing
3.3. Tissue Classification
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Characteristic | No. of Patients (%) | Mean ± STD |
---|---|---|
Age, years | 57 ± 11 | |
<50 | 48 (25) | |
50–59 | 69 (37) | |
60–69 | 43 (23) | |
≥70 | 29 (15) | |
Menopausal stage | ||
Pre | 41 (22) | |
Peri | 16 (8) | |
Post | 109 (58) | |
Unknown | 23 (12) | |
Breast side | ||
Left | 92 (49) | |
Right | 97 (51) | |
Breast density, ACR score | ||
1 | 15 (8) | |
2 | 75 (40) | |
3 | 75 (40) | |
4 | 20 (11) | |
Unknown | 4 (2) | |
Neoadjuvant therapy | ||
Chemotherapy | 19 (10) | |
Hormone therapy | 13 (7) | |
Immunotherapy | 2 (1) | |
None | 155 (82) | |
Size lumpectomy, cm | 57 ± 56 |
Tissue Class | Training Set | Test Set |
---|---|---|
#Patients (#Locations) | Labeled | Labeled |
Healthy | 129 (221) | 35 (53) |
Malignant | 59 (81) | 12 (17) |
Total | 151 (302) | 38 (70) |
Sensitivity Mean [95% CI] | Specificity Mean [95% CI] | Accuracy Mean [95% CI] | MCC | AUC | |
---|---|---|---|---|---|
Patch center | 0.76 [0.50, 0.93] | 0.85 [0.72, 0.93] | 0.83 [0.72, 0.91] | 0.57 | 0.85 |
Patch average | 0.88 [0.64, 0.99] | 0.85 [0.72, 0.93] | 0.86 [0.75, 0.93] | 0.67 | 0.93 |
Patch unmixing | 0.94 [0.71, 1.00] | 0.85 [0.72, 0.93] | 0.87 [0.77, 0.94] | 0.71 | 0.92 |
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Jong, L.-J.S.; Post, A.L.; Veluponnar, D.; Geldof, F.; Sterenborg, H.J.C.M.; Ruers, T.J.M.; Dashtbozorg, B. Tissue Classification of Breast Cancer by Hyperspectral Unmixing. Cancers 2023, 15, 2679. https://doi.org/10.3390/cancers15102679
Jong L-JS, Post AL, Veluponnar D, Geldof F, Sterenborg HJCM, Ruers TJM, Dashtbozorg B. Tissue Classification of Breast Cancer by Hyperspectral Unmixing. Cancers. 2023; 15(10):2679. https://doi.org/10.3390/cancers15102679
Chicago/Turabian StyleJong, Lynn-Jade S., Anouk L. Post, Dinusha Veluponnar, Freija Geldof, Henricus J. C. M. Sterenborg, Theo J. M. Ruers, and Behdad Dashtbozorg. 2023. "Tissue Classification of Breast Cancer by Hyperspectral Unmixing" Cancers 15, no. 10: 2679. https://doi.org/10.3390/cancers15102679
APA StyleJong, L. -J. S., Post, A. L., Veluponnar, D., Geldof, F., Sterenborg, H. J. C. M., Ruers, T. J. M., & Dashtbozorg, B. (2023). Tissue Classification of Breast Cancer by Hyperspectral Unmixing. Cancers, 15(10), 2679. https://doi.org/10.3390/cancers15102679