Additional file 1 of Multiclass risk models for ovarian malignancy: an illustration of prediction uncertainty due to the choice of algorithm
Creators: name: Ledger, Ashleigh
name: Ceusters, Jolien
name: Valentin, Lil
name: Testa, Antonia
name: Van Holsbeke, Caroline
name: Franchi, Dorella
name: Bourne, Tom
name: Froyman, Wouter
name: Timmerman, Dirk
name: Van Calster, Ben
Publisher: name: figshare
Year: 2023
Subjects: Medicine
Biological Sciences not elsewhere classified
Information Systems not elsewhere classified
Mathematical Sciences not elsewhere classified
Cancer

description: Supplementary Material 1. Predictor selection. Supplementary Material 2. Sample size argumentation. Supplementary Material 3. Hyperparameter tuning. Supplementary Material 4. Multiple imputation for CA125. Supplementary Material 5. Flowcharts for modeling and validation procedure. Table S1. Descriptive statistics by reference standard (final diagnosis). Table S2. List of centers in the development and validation data. Table S3. Pairwise area under the receiver operating characteristic curve (AUROC) values (with 95% CI) for models with CA125 on external validation data. Table S4. Pairwise area under the receiver operating characteristic curve (AUROC) values for models without CA125. Table S5. Percentage of patients on validation data falling on opposite sides of the 10% risk of malignancy threshold when comparing two models. Figure S1. Polytomous discrimination index for models with CA125 on external validation data. Figure S2. AUROC for benign tumors vs any malignancy for models with CA125. Figure S3. Polytomous Discrimination Index (PDI) for models without CA125. Figure S4. AUROC for benign tumors vs any malignancy for models without CA125. Figure S5. Box plots of estimated probabilities for standard MLR with CA125. Figure S6. Box plots of estimated probabilities for ridge MLR with CA125. Figure S7. Box plots of estimated probabilities for random forest with CA125. Figure S8. Box plots of estimated probabilities for extreme gradient boosting (XGBoost) with CA125. Figure S9. Box plots of estimated probabilities for neural network with CA125. Figure S10. Box plots of estimat ed probabilities for support vector machine with CA125. Figure S11. Flexible calibration curves for models without CA125. Figure S12. Box plots of estimated probabilities for standard MLR without CA125. Figure S13. Box plots of estimated probabilities for ridge MLR without CA125. Figure S14. Box plots of estimated probabilities for random forest without CA125. Figure S15. Box plots of estimated probabilities for extreme gradient boosting (XGBoost) without CA125. Figure S16. Box plots of estimated probabilities for neural network without CA125. Figure S17. Box plots of estimated probabilities for support vector machine without CA125. Figure S18. Decision curves for models with CA125 on external validation data. Figure S19. Decision curves for models without CA125 on external validation data. Figure S20. Differences between the highest and lowest estimated probability for each outcome across the six models with CA125 (panel A) and the six models without CA125 (panel B) for patients in the external validation dataset. Each dot denotes the difference between the highest and the lowest estimated probability for one patient. This means that each patient is shown five times in each panel, once for each outcome category. For example, at the far left, the difference between the highest and lowest estimated probability for a benign tumor is shown for all 3199 patients in the dataset. The box represents the interquartile range which contains the middle 50% of the differences. The line inside the box indicates the median. Whiskers correspond to the 5th and 95th percentile. Figure S21. Scatter plots of the estimated risk of a benign tumor for each pair of models without CA125. Figure S22. Scatter plots of the estimated risk of a borderline tumor for each pair of models without CA125. Figure S23. Scatter plots of the estimated risk of a stage I primary invasive tumor for each pair of models without CA125. Figure S24. Scatter plots of the estimated risk of a stage II-IV primary invasive tumor for each pair of models without CA125. Figure S25. Scatter plots of the estimated risk of a secondary metastatic tumor for each pair of models without CA125.