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Fig. 2 | Journal of Translational Medicine

Fig. 2

From: A novel model for predicting postoperative liver metastasis in R0 resected pancreatic neuroendocrine tumors: integrating computational pathology and deep learning-radiomics

Fig. 2

Schematic illustration of the study design. Features were selected from clinical history, pathologic slides, and CT images. Logistic analysis of clinical information identified independent prognostic factors for liver metastasis. A computational pathology model was used to calculate the Ki67 index in hotspot areas and the MH index in heterogeneous distributions to determine the Pathomics score. The radiomics and deep learning models were used to analyze CT images to derive the DLR model. Finally, based on the total scores from the nomogram, patients were categorized into high- and low-risk groups for predicting postoperative liver metastasis

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