Assessment of Digital Pathology Imaging Biomarkers Associated with Breast Cancer Histologic Grade
Abstract
:1. Introduction
2. Methods
2.1. Patients and Dataset
2.2. Specimen Preparation
2.3. WSI Pre-Processing and Tumor Bed Identification
2.4. Instance Segmentation Network
2.5. Spatial Feature Extraction
2.6. Machine Learning
2.7. Software and Hardware
3. Results
3.1. Clinicopathological Characteristics
3.2. Mask R-CNN Segmentation
3.3. Computationally Derived Spatial Features
3.4. Predictive Modeling Using Machine Learning
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Patient Clinicopathological Characteristics | Study Cohort (n = 138) | ||
---|---|---|---|
G1, 2 (n = 58) n (%) | G3 (n = 80) n (%) | p-Value | |
Age | |||
Mean Age ± SD (y) | 51.6 ± 10.8 | 50.3 ± 9.2 | 0.423 |
≤50 years | 23 (39.7) | 39 (48.8) | 0.289 |
>50 years | 35 (60.3) | 41 (51.3) | |
Menopausal Status | |||
Pre | 30 (51.7) | 38 (47.5) | 0.624 |
Post | 28 (48.3) | 42 (52.5) | |
Tumor Laterality | |||
Left | 25 (43.1) | 38 (47.5) | 0.609 |
Right | 33 (56.9) | 42 (52.5) | |
Receptor Status | |||
Median ER ± SD (%) | 90 ± 44.1 | 0 ± 43.9 | <0.000 |
ER-positive | 43 (74.1) | 34 (42.5) | <0.000 |
ER-negative | 15 (25.9) | 46 (57.5) | |
Median PR ± SD (%) | 4 ± 42.8 | 0 ± 36.3 | 0.013 |
PR-positive | 35 (60.3) | 30 (37.5) | 0.008 |
PR-negative | 23 (39.7) | 50 (62.5) | |
HER2-positive | 26 (44.8) | 39 (48.8) | 0.649 |
HER2-negative | 32 (55.2) | 41 (51.3) | |
Tumor Size | 0.445 | ||
Mean Size ± SD (mm) | 48.3 ± 27.8 | 44.5 ± 25.1 | |
Clinical T Stage | |||
1 | 5 (8.6) | 4 (5.0) | 0.314 |
2 | 32 (55.2) | 54 (67.5) | |
3 | 21 (36.2) | 22 (27.5) | |
4 | 0 (0.0) | 0 (0.0) | |
Clinical N Stage | |||
0 | 12 (20.7) | 28 (35.0) | 0.183 |
1 | 40 (69.0) | 46 (57.5) | |
2 | 6 (10.3) | 6 (7.5) | |
3 | 0 (0.0) | 0 (0.0) | |
Node Status | |||
Node-positive | 46 (79.3) | 52 (65.0) | 0.067 |
Node-negative | 12 (20.7) | 28 (35.0) | |
Inflammatory Breast Cancer | |||
Yes | 5 (8.6) | 8 (10.0) | 0.784 |
No | 53 (91.4) | 72 (90.0) |
Model | Feature Type | Feature Index | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | ƒ | ||
Naïve Bayes | Spatial | V max dist disorder | MST branches min max ratio | 33 | ||||||||
Clinical | ||||||||||||
K-NN | Spatial | # of nuclei | V max dist disorder | ρ neighbors in dist 1 disorder | ρ neighbors in dist 4 stddev | ρ min | 16 | |||||
Clinical | Age (years) | ER (%) | PR (%) | HER2 status | 63 | |||||||
LR | Spatial | V max dist stddev | V max dist disorder | MST branches min max ratio | ρ neighbors in dist 1 disorder | ρ dist for neighbors 2 min max ratio | 43 | |||||
Clinical | ER (%) | PR (%) | 81 | |||||||||
RF | Spatial | # of nuclei | V max dist stddev | V max dist disorder | ρ neighbors in dist 1 disorder | ρ neighbors in dist 4 stddev | ρ dist for neighbors 2 min max ratio | ρ dist for neighbors 2 disorder | ρ min | ρ med | 15 | |
Clinical | PR (%) | HER2 status | 62 | |||||||||
SVM | Spatial | # of nuclei | V max dist disorder | MST branches min max ratio | ρ neighbors in dist 1 disorder | ρ neighbors in dist 4 stddev | ρ dist for neighbors 2 disorder | ρ min | ρ med | 25 | ||
Clinical | Age (years) | ER (%) | PR (%) | HER2 status | 84 | |||||||
XGBoost | Spatial | # of nuclei | V max dist stddev | V max dist disorder | MST branches min max ratio | ρ neighbors in dist 1 disorder | ρ neighbors in dist 4 stddev | ρ dist for neighbors 2 min max ratio | ρ dist for neighbors 2 disorder | ρ min | ρ med | 7 |
Clinical | ER (%) | PR (%) | 22 |
Feature Set | Model | Training Set | Testing Set | |||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Mean AUC ± SD | Mean ACC ± SD (%) | AUC | Acc (%) | Sn (%) | Sp (%) | Prev (%) | FNR (%) | FPR (%) | PPV (%) | NPV (%) | FDR (%) | FOR (%) | LR+ | LR- | DOR | f1 | ||
Clinical | K-NN | 0.66 ± 0.16 | 62 ± 14 | 0.62 | 64.29 | 75.00 | 50.00 | 57.14 | 25.00 | 50.00 | 66.67 | 60.00 | 33.33 | 40.00 | 1.50 | 0.50 | 3.00 | 0.71 |
LR | 0.66 ± 0.22 | 64 ± 19 | 0.77 | 73.81 | 75.00 | 72.22 | 57.14 | 25.00 | 27.78 | 78.26 | 68.42 | 21.74 | 31.58 | 2.70 | 0.35 | 7.80 | 0.77 | |
RF | 0.68 ± 0.15 | 59 ± 14 | 0.56 | 66.67 | 83.33 | 44.44 | 57.14 | 16.67 | 55.56 | 66.67 | 66.67 | 33.33 | 33.33 | 1.50 | 0.38 | 4.00 | 0.74 | |
SVM | 0.64 ± 0.25 | 52 ± 16 | 0.50 | 66.67 | 75.00 | 55.56 | 57.14 | 25.00 | 44.44 | 69.23 | 62.50 | 30.77 | 37.50 | 1.69 | 0.45 | 3.75 | 0.72 | |
XGBoost | 0.63 ± 0.23 | 59 ± 16 | 0.77 | 73.81 | 75.00 | 72.22 | 57.14 | 25.00 | 27.78 | 78.26 | 68.42 | 21.74 | 31.58 | 2.70 | 0.35 | 7.80 | 0.77 | |
Spatial | Naïve Bayes | 0.65 ± 0.07 | 59 ± 6 | 0.68 | 64.29 | 87.50 | 33.33 | 57.14 | 12.50 | 66.67 | 63.64 | 66.67 | 36.36 | 33.33 | 1.31 | 0.38 | 3.50 | 0.74 |
K-NN | 0.87 ± 0.03 | 76 ± 3 | 0.64 | 66.67 | 66.67 | 66.67 | 57.14 | 33.33 | 33.33 | 72.73 | 60.00 | 27.27 | 40.00 | 2.00 | 0.50 | 4.00 | 0.70 | |
LR | 0.67 ± 0.04 | 62 ± 5 | 0.73 | 66.67 | 62.50 | 72.22 | 57.14 | 37.50 | 27.78 | 75.00 | 59.09 | 25.00 | 40.91 | 2.25 | 0.52 | 4.33 | 0.68 | |
RF | 0.88 ± 0.04 | 79 ± 5 | 0.75 | 64.29 | 79.17 | 44.44 | 57.14 | 20.83 | 55.56 | 65.52 | 61.54 | 34.48 | 38.46 | 1.43 | 0.47 | 3.04 | 0.72 | |
SVM | 0.79 ± 0.06 | 77 ± 5 | 0.69 | 69.05 | 75.00 | 61.11 | 57.14 | 25.00 | 38.89 | 72.00 | 64.71 | 28.00 | 35.29 | 1.93 | 0.41 | 4.71 | 0.73 | |
XGBoost | 0.88 ± 0.03 | 79 ± 3 | 0.78 | 71.43 | 87.50 | 50.00 | 57.14 | 12.50 | 50.00 | 70.00 | 75.00 | 30.00 | 25.00 | 1.75 | 0.25 | 7.00 | 0.78 | |
Ensemble | LR + RF | 0.96 ± 0.12 | 88 ± 14 | 0.84 | 78.57 | 83.33 | 72.22 | 57.14 | 16.67 | 27.78 | 80.00 | 76.47 | 20.00 | 23.53 | 3.00 | 0.23 | 13.00 | 0.82 |
LR + XGBoost | 0.70 ± 0.23 | 56 ± 14 | 0.84 | 73.81 | 75.00 | 72.22 | 57.14 | 25.00 | 27.78 | 78.26 | 68.42 | 21.74 | 31.58 | 2.70 | 0.35 | 7.80 | 0.77 | |
XGBoost + RF | 0.96 ± 0.13 | 92 ± 13 | 0.83 | 73.81 | 87.50 | 55.56 | 57.14 | 12.50 | 44.44 | 72.41 | 76.92 | 27.59 | 23.08 | 1.97 | 0.23 | 8.75 | 0.79 |
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Lagree, A.; Shiner, A.; Alera, M.A.; Fleshner, L.; Law, E.; Law, B.; Lu, F.-I.; Dodington, D.; Gandhi, S.; Slodkowska, E.A.; et al. Assessment of Digital Pathology Imaging Biomarkers Associated with Breast Cancer Histologic Grade. Curr. Oncol. 2021, 28, 4298-4316. https://doi.org/10.3390/curroncol28060366
Lagree A, Shiner A, Alera MA, Fleshner L, Law E, Law B, Lu F-I, Dodington D, Gandhi S, Slodkowska EA, et al. Assessment of Digital Pathology Imaging Biomarkers Associated with Breast Cancer Histologic Grade. Current Oncology. 2021; 28(6):4298-4316. https://doi.org/10.3390/curroncol28060366
Chicago/Turabian StyleLagree, Andrew, Audrey Shiner, Marie Angeli Alera, Lauren Fleshner, Ethan Law, Brianna Law, Fang-I Lu, David Dodington, Sonal Gandhi, Elzbieta A. Slodkowska, and et al. 2021. "Assessment of Digital Pathology Imaging Biomarkers Associated with Breast Cancer Histologic Grade" Current Oncology 28, no. 6: 4298-4316. https://doi.org/10.3390/curroncol28060366
APA StyleLagree, A., Shiner, A., Alera, M. A., Fleshner, L., Law, E., Law, B., Lu, F. -I., Dodington, D., Gandhi, S., Slodkowska, E. A., Shenfield, A., Jerzak, K. J., Sadeghi-Naini, A., & Tran, W. T. (2021). Assessment of Digital Pathology Imaging Biomarkers Associated with Breast Cancer Histologic Grade. Current Oncology, 28(6), 4298-4316. https://doi.org/10.3390/curroncol28060366