A Deep Learning Model for Cervical Cancer Screening on Liquid-Based Cytology Specimens in Whole Slide Images
Abstract
:Simple Summary
Abstract
1. Introduction
2. Materials and Methods
2.1. Clinical Cases and Cytopathological Records
2.2. Dataset
2.3. Annotation
2.4. Deep Learning Models
2.5. Interobserver Concordance Study
2.6. Software and Statistical Analysis
2.7. Code Availability
3. Results
3.1. High AUC Performance of WSI Evaluation of Neoplastic Cervical Liquid-Based Cytology (LBC) Images
3.2. True Positive Prediction
3.3. True Negative Prediction
3.4. False Positive Prediction
3.5. Interobserver Variability
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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Total | Neoplastic | NILM | |
---|---|---|---|
training | 1503 | 302 | 1201 |
validation | 150 | 50 | 100 |
test: full agreement | 300 | 20 | 280 |
test: equal balance | 750 | 375 | 375 |
test: equal balance-rev. | 643 | 279 | 364 |
test: clinical balance | 750 | 38 | 712 |
test: clinical balance-rev. | 525 | 35 | 490 |
Full Agreement | Clinical Balance | Clinical Balance-rev. | Equal Balance | Equal Balance-rev. | |
---|---|---|---|---|---|
ROC AUC | 0.960 [0.921–0.988] | 0.774 [0.679–0.841] | 0.890 [0.808–0.963] | 0.827 [0.795–0.852] | 0.915 [0.892–0.937] |
log loss | 2.244 [2.021–2.458] | 2.272 [2.141–2.412] | 1.347 [1.238–1.465] | 1.126 [0.994–1.264] | 0.913 [0.794–1.055] |
accuracy | 0.907 [0.873–0.937] | 0.629 [0.591–0.660] | 0.903 [0.876–0.924] | 0.759 [0.725–0.785] | 0.885 [0.859–0.908] |
sensitivity | 0.850 [0.667–1.000] | 0.816 [0.686–0.923] | 0.886 [0.774–0.978] | 0.624 [0.573–0.668] | 0.839 [0.794–0.880] |
specificity | 0.911 [0.877–0.942] | 0.619 [0.579–0.652] | 0.904 [0.877–0.926] | 0.893 [0.862–0.924] | 0.920 [0.890–0.945] |
Predicted Label | ||||
---|---|---|---|---|
NILM | Neoplastic | |||
Full agreement | True label | NILM | 255 | 25 |
Neoplastic | 3 | 17 | ||
Clinical balance | True label | NILM | 441 | 271 |
Neoplastic | 7 | 31 | ||
Clinical balance-rev. | True label | NILM | 443 | 47 |
Neoplastic | 4 | 31 | ||
Equal balance | True label | NILM | 335 | 40 |
Neoplastic | 141 | 234 | ||
Equal balance-rev. | True label | NILM | 335 | 29 |
Neoplastic | 45 | 234 |
Age | Exp. (Years) | Case 1 | Case 2 | Case 3 | Case 4 | Case 5 | Case 6 | Case 7 | Case 8 | Case 9 | Case 10 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
Dx | NILM | NILM | NILM | NILM | NILM | NILM | NILM | NILM | HSIL | LSIL | ||
30s | ≥10 | CS1 | NILM | NILM | NILM | NILM | NILM | NILM | NILM | NILM | HSIL | ASC-H |
50s | CS2 | NILM | NILM | NILM | ASC-H | NILM | NILM | HSIL | ASC-H | HSIL | HSIL | |
50s | CS3 | NILM | NILM | NILM | NILM | NILM | NILM | NILM | ASC-US | HSIL | LSIL | |
40s | CS4 | NILM | NILM | NILM | ASC-US | NILM | NILM | NILM | ASC-US | HSIL | SCC | |
30s | CS5 | NILM | NILM | NILM | NILM | NILM | NILM | NILM | NILM | HSIL | ASC-US | |
30s | CS6 | NILM | ASC-US | NILM | NILM | NILM | NILM | NILM | NILM | HSIL | HSIL | |
60s | CS7 | NILM | NILM | NILM | NILM | NILM | NILM | NILM | NILM | HSIL | ASC-H | |
40s | CS8 | NILM | NILM | NILM | NILM | NILM | NILM | NILM | NILM | HSIL | ASC-US | |
20s | <10 | CS9 | NILM | NILM | NILM | NILM | NILM | NILM | NILM | NILM | HSIL | LSIL |
20s | CS10 | NILM | NILM | NILM | NILM | NILM | NILM | NILM | NILM | LSIL | LSIL | |
30s | CS11 | NILM | NILM | NILM | NILM | ASC-H | NILM | NILM | HSIL | LSIL | HSIL | |
20s | CS12 | NILM | ASC-US | ASC-H | NILM | NILM | NILM | NILM | LSIL | SCC | HSIL | |
40s | CS13 | NILM | NILM | HSIL | NILM | NILM | NILM | NILM | ASC-US | HSIL | ASC-H | |
30s | CS14 | NILM | NILM | LSIL | NILM | NILM | NILM | NILM | NILM | HSIL | LSIL | |
20s | CS15 | NILM | NILM | NILM | NILM | NILM | NILM | LSIL | NILM | HSIL | ASC-US | |
20s | CS16 | NILM | NILM | NILM | ASC-US | LSIL | NILM | NILM | ASC-US | HSIL | SCC |
Classification | Dx Report | 16 Cytoscreeners | 8 Cytoscreeners (≥10 Years of Exp.) | |
---|---|---|---|---|
NILM | 0.042 (slight) | 0.755 (substantial) | ||
Subclass | Neoplastic | 0.098 (slight) | 0.500 (moderate) | |
All cases | 0.364 (fair) | 0.716 (substantial) | ||
NILM | 0.073 (slight) | 0.815 (almost perfect) | ||
Binary | Neoplastic | 1.000 (complete) | 1.000 (complete) | |
All cases | 0.568 (moderate) | 0.861 (almost perfect) |
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Kanavati, F.; Hirose, N.; Ishii, T.; Fukuda, A.; Ichihara, S.; Tsuneki, M. A Deep Learning Model for Cervical Cancer Screening on Liquid-Based Cytology Specimens in Whole Slide Images. Cancers 2022, 14, 1159. https://doi.org/10.3390/cancers14051159
Kanavati F, Hirose N, Ishii T, Fukuda A, Ichihara S, Tsuneki M. A Deep Learning Model for Cervical Cancer Screening on Liquid-Based Cytology Specimens in Whole Slide Images. Cancers. 2022; 14(5):1159. https://doi.org/10.3390/cancers14051159
Chicago/Turabian StyleKanavati, Fahdi, Naoki Hirose, Takahiro Ishii, Ayaka Fukuda, Shin Ichihara, and Masayuki Tsuneki. 2022. "A Deep Learning Model for Cervical Cancer Screening on Liquid-Based Cytology Specimens in Whole Slide Images" Cancers 14, no. 5: 1159. https://doi.org/10.3390/cancers14051159