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- W4387461570 abstract "Mesothelioma is classified into three histological subtypes, epithelioid, sarcomatoid, and biphasic, according to the relative proportions of epithelioid and sarcomatoid tumor cells present. Current guidelines recommend that the sarcomatoid component of each mesothelioma is quantified, as a higher percentage of sarcomatoid pattern in biphasic mesothelioma shows poorer prognosis. In this work, we develop a dual-task graph neural network (GNN) architecture with ranking loss to learn a model capable of scoring regions of tissue down to cellular resolution. This allows quantitative profiling of a tumor sample according to the aggregate sarcomatoid association score. Tissue is represented by a cell graph with both cell-level morphological and regional features. We use an external multicentric test set from Mesobank, on which we demonstrate the predictive performance of our model. We additionally validate our model predictions through an analysis of the typical morphological features of cells according to their predicted score." @default.
- W4387461570 created "2023-10-10" @default.
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- W4387461570 date "2023-10-01" @default.
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- W4387461570 title "MesoGraph: Automatic profiling of mesothelioma subtypes from histological images" @default.
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- W4387461570 doi "https://doi.org/10.1016/j.xcrm.2023.101226" @default.
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