Artificial Intelligence for Image-Based Breast Cancer Risk Prediction Using Attention
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data
- Screen-Detected Cancers (SDC): women who were diagnosed with screen-detected breast cancer on study entry;
- Future Screen-Detected Cancers 1/2/3 (FSDC): women who were confirmed cancer-free on study entry but went on to develop screen-detected cancer. Numbers refer to which subsequent screen was used, so FSDC 1 refers to women who were diagnosed on the first subsequent screen and so on;
- Interval 1/2/3: women who were confirmed cancer-free on study entry but went on to develop an interval cancer after entry. The number refers to before which subsequent screen was the interval diagnosed, so Interval 1 refers to women who developed an interval cancer before the first subsequent screen.
2.2. Data Preparation
2.3. Model
2.4. Experimental Setup and Model Evaluation
3. Results
3.1. Logistic Regression Analysis
3.2. Model Performance for Different Sets
4. Discussion
Limitations
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
MAI-risk | Manchester Artificial Intelligence risk |
PROCAS | Predicting Risk of Cancer At Screening |
PRC-mixed | PROCAS Remaining Cancers - mixed |
BCR-priors | Breast Cancer Research - priors |
SDC | Screen-Detected Cancer |
FSDC | Future Screen-Detected Cancer |
MIL | Mulitple Instance Learning |
CNN | Convolutional Neural Network |
FFDM | Full-Field Digital Mammography |
BMI | Body Mass Index |
HRT | Hormone Replacement Therapy |
CC | CranioCaudal |
MLO | MedioLateral Oblique |
AUC | Area Under the receiver operating characteristic Curve |
FP | False Positive |
TP | True Positive |
OR | Odds Ratio |
GE | General Electric |
Appendix A
PRC-Mixed | BCR-Priors | ||||
---|---|---|---|---|---|
Cases (%) | Controls (%) | Cases (%) | Controls (%) | ||
Age | <50 | 210 (5.5) | 129 (10.2) | 16 (5.1) | 47 (5.0) |
50–54 | 355 (28.0) | 948 (25.0) | 64 (20.3) | 196 (20.7) | |
55–59 | 245 (19.4) | 1156 (30.4) | 58 (18.4) | 171 (18.0) | |
60–64 | 303 (23.9) | 504 (13.3) | 99 (31.3) | 297 (31.3) | |
65–69 | 180 (14.2) | 756 (19.9) | 62 (19.6) | 186 (19.6) | |
>70 | 54 (4.3) | 224 (5.9) | 17 (5.4) | 51 (5.4) | |
Menopausal Status | Premenopausal | 165 (13.0) | 500 (13.2) | 22 (7.0) | 67 (7.1) |
Perimenopausal | 209 (16.5) | 657 (17.3) | 46 (14.6) | 135 (14.2) | |
Postmenopausal | 835 (66.0) | 2584 (68.0) | 237 (75.0) | 713 (75.2) | |
Unknown | 57 (4.5) | 57 (1.5) | 11 (3.5) | 33 (3.5) | |
HRT Status | Current/Previous | 470 (37.1) | 1480 (39.0) | 148 (46.8) | 456 (48.1) |
Never | 779 (61.5) | 2316 (61.0) | 166 (52.5) | 485 (51.2) | |
Unknown | 17 (1.3) | 2 (0.1) | 2 (0.6) | 7 (0.7) | |
BMI | <25 kg/m | 396 (31.3) | 1193 (31.4) | 99 (31.3) | 292 (30.8) |
25–30 kg/m | 428 (33.8) | 1283 (33.8) | 111 (35.1) | 337 (35.5) | |
>30 kg/m | 341 (26.9) | 1026 (27.0) | 88 (27.8) | 272 (28.7) | |
Unknown | 101 (8.0) | 296 (7.8) | 18 (5.7) | 47 (5.0) | |
Ethnic Origin | White | 1148 (90.7) | 3500 (92.2) | 282 (89.2) | 866 (91.4) |
Other/Unknown | 118 (9.3) | 298 (7.8) | 34 (10.8) | 82 (8.6) | |
Year of Entry | 2009 | 3 (0.2) | 23 (0.6) | 0 (0.0) | 0 (0.0) |
2010 | 261 (20.6) | 1267 (33.4) | 63 (19.9) | 178 (18.8) | |
2011 | 449 (35.5) | 1353 (35.6) | 163 (51.6) | 511 (53.9) | |
2012 | 375 (29.6) | 810 (21.3) | 85 (26.9) | 258 (27.2) | |
2013 | 85 (6.7) | 168 (4.4) | 3 (0.9) | 1 (0.1) | |
2014 | 78 (6.2) | 135 (3.6) | 2 (0.6) | 0 (0.0) | |
2015 | 15 (1.2) | 42 (1.1) | 0 (0.0) | 0 (0.0) | |
Family History | FDR Only | 163 (12.9) | 403 (10.6) | 40 (12.7) | 103 (10.9) |
SDR Only | 216 (17.1) | 606 (16.0) | 51 (16.1) | 153 (16.1) | |
Both | 82 (6.5) | 163 (4.3) | 23 (7.3) | 46 (4.9) | |
Neither | 805 (63.6) | 2626 (69.1) | 202 (63.9) | 646 (68.1) | |
Alcohol Use | Yes | 961 (75.9) | 2677 (70.5) | 229 (72.5) | 673 (71.0) |
No | 281 (22.2) | 1060 (27.9) | 85 (26.9) | 262 (27.6) | |
Unknown | 24 (1.9) | 61 (1.6) | 2 (0.6) | 13 (1.4) | |
Parity | Yes | 1085 (85.7) | 3261 (85.9) | 269 (85.1) | 852 (89.9) |
No | 179 (14.1) | 537 (14.1) | 44 (13.9) | 95 (10.0) | |
Unknown | 2 (0.2) | 0 (0.0) | 3 (0.9) | 1 (0.1) |
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Number of Pixels Corresponding to Breast Tissue | Overlap Step Size |
---|---|
>5,017,600 | 200 |
2,508,800–5,017,600 | 168 |
752,640–2,508,800 | 112 |
<752,640 | 74 |
Target Covariate | OR (Normalised) | OR (Representative) |
---|---|---|
VAS | 1.60 (1.37, 1.88) | 1.19 (1.05, 1.36) |
IBIS | 1.34 (1.08, 1.65) | 1.03 (1.02, 1.04) |
MAI-risk | 3.04 (2.58, 3.59) | 4.70 (3.74, 5.93) |
Group | Number of Women | AUC |
---|---|---|
SDC | 1600 | 0.676 (0.645, 0.707) |
Interval 1 | 160 | 0.571 (0.464, 0.679) |
FSDC 1 | 396 | 0.573 (0.506, 0.641) |
Interval 2 | 268 | 0.657 (0.582, 0.730) |
FSDC 2 | 700 | 0.644 (0.594, 0.693) |
Interval 3 | 380 | 0.666 (0.602, 0.727) |
FSDC 3 | 1312 | 0.650 (0.614, 0.685) |
Distant | 248 | 0.649 (0.565, 0.731) |
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Romanov, S.; Howell, S.; Harkness, E.; Bydder, M.; Evans, D.G.; Squires, S.; Fergie, M.; Astley, S. Artificial Intelligence for Image-Based Breast Cancer Risk Prediction Using Attention. Tomography 2023, 9, 2103-2115. https://doi.org/10.3390/tomography9060165
Romanov S, Howell S, Harkness E, Bydder M, Evans DG, Squires S, Fergie M, Astley S. Artificial Intelligence for Image-Based Breast Cancer Risk Prediction Using Attention. Tomography. 2023; 9(6):2103-2115. https://doi.org/10.3390/tomography9060165
Chicago/Turabian StyleRomanov, Stepan, Sacha Howell, Elaine Harkness, Megan Bydder, D. Gareth Evans, Steven Squires, Martin Fergie, and Sue Astley. 2023. "Artificial Intelligence for Image-Based Breast Cancer Risk Prediction Using Attention" Tomography 9, no. 6: 2103-2115. https://doi.org/10.3390/tomography9060165