Hyojin Kim

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Yoon H, et al. J Gynecol Oncol. 2026 ;37:e86. https://doi.org/10.3802/jgo.2026.37.e86

Park SM, et al. J Gynecol Oncol. 2026 ;37:e80. https://doi.org/10.3802/jgo.2026.37.e80

Baseline characteristics of the test sets (test set A, B, and C).

Figure S6. Prediction scores for EGFR mutation prediction between two magnifications in test set C (n = 2,261).

Performance of the ensemble model to predict EGFR mutation status across test set A (n = 1,461) and test set B (n = 599). ROC curves for test set A (A–D) and test set B (E–I) are shown. For each test set, the following subgroup analyses were performed: entire dataset (A and E), specimen type (surgical resection and biopsy; B and F), EGFR mutation subtypes (E19del, L858R point mutations, E20ins, T790M, and other mutations; C and G) and scanner type (D and H). For test set B, data source subgroups are additionally presented (I). The sample size (“n”) for each subgroup reflects the number of cases and controls. For EGFR subtypes, each ROC curve was generated using cases positive for the specific EGFR subtype (with or without co-mutations) as EGFR mutant and all EGFR wild-type cases as EGFR wild type. The data represent the AUC together with corresponding 95% CIs.

Baseline characteristics of the training set and tuning set.

Figure S3. Prediction scores of the ensemble model for EGFR mutation prediction across EGFR mutation subtypes in test set A.

Figure S5. Prediction scores for EGFR mutation prediction across scanner types in test set C (n = 2,261).