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Fig. 3 | Breast Cancer Research

Fig. 3

From: Multimodal recurrence risk prediction model for HR+/HER2- early breast cancer following adjuvant chemo-endocrine therapy: integrating pathology image and clinicalpathological features

Fig. 3

Comparison of the impact of different feature encoders on deep learning model performance and Kaplan-Meier analysis of WSI-based risk score. (A-B). Performance metrics (AUC, accuracy, recall, and F1 score) of five feature encoders (CTransPath, UNI, CONCH, Virchow, and REMEDIS) in the ACMIL-Based deep learning model for the WCH and TCGA cohorts. (C). Heatmap distribution of WSI-based risk score and patient recurrence status for the UNI-ACMIL model. (D-H). Kaplan-Meier curves for RFS in the test set. HR and 95% CI were calculated using the Cox proportional hazards model. P values were calculated using the log-rank test. WSI, whole slide image; RFS, recurrence-free survival

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Breast Cancer Research

ISSN: 1465-542X

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