Fig. 2: The ability of the proposed system to identify hormone receptor-positive patients. | Communications Medicine

Fig. 2: The ability of the proposed system to identify hormone receptor-positive patients.

From: Clinical utility of receptor status prediction in breast cancer and misdiagnosis identification using deep learning on hematoxylin and eosin-stained slides

Fig. 2

Slides with prediction scores above the threshold are classified as hormone receptor-positive. The system’s performance is evaluated as specificity (orange), sensitivity (cyan), and PPV (green) per slide. The high specificity and PPV demonstrate the system’s ability to identify hormone receptor-positive patients without IHC staining. The performance and impacted patients (dark blue) are presented with respect to the threshold, showing that a threshold of 0.6 is a good choice for the operating threshold. The sensitivity can be interpreted as the percentage of actual hormone receptor-positive slides that are correctly identified as hormone receptor-positive. The 95% confidence intervals are highlighted for each plot. The number of independent samples for each threshold corresponds to the relative portion of impacted patients from the total number of patients in each dataset: CAT-Cross-Validation (n = 4186), CAT-Test (n = 1369), Carmel-Test (n = 577), ABCTB-Test (n = 625), TCGA-Test (n = 167), Haemek (n = 482), CHUCB (n = 172), and Ipatimup (n = 100). IHC Immunohistochemistry, PPV positive predictive value. CAT Carmel, ABCTB, TCGA.

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