Fig. 5: Interpretable histopathological features stratify HGSOC patients by OS. | Nature Cancer

Fig. 5: Interpretable histopathological features stratify HGSOC patients by OS.

From: Multimodal data integration using machine learning improves risk stratification of high-grade serous ovarian cancer

Fig. 5: Interpretable histopathological features stratify HGSOC patients by OS.The alternative text for this image may have been generated using AI.

a, Tissue map from H&E slides with nuclear detections yielding tissue-type and cell-type features. b, Log HRs of the two chosen histological features (with 95% CI as estimated by Cox regression; fit on n = 243 patients). c, Training and test concordance indices are shown: the height of each bar shows the c-Index and the lower and upper points of the respective error bars depict the 95% CI by 100-fold leave-one-out bootstrapping. d,e, Kaplan–Meier survival analysis and log-rank test statistics for training (d) and test sets (e). f,g, H&E of extreme examples of the model’s inferred mean tumoral nuclear area (scale bar, 50 µm for each image).

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