Our host discusses with Dr. Anna Laury, from the Department of Pathology, University of Helsinki, Finland, her team's study on the utilization of AI-guided spatial transcriptomic analysis to allow for the biological interpretation of morphologic features detected by AI algorithms when applied to WSI of standard H&E sections.
The authors previously trained an AI model to identify HGSC (high-grade serous carcinoma of the ovary) tumor regions that are highly associated with outcome status but are indistinguishable by conventional morphologic methods. In the here discussed study, Dr. Laury’s team applied spatially resolved transcriptomics to further profile the AI-identified tumor regions in 16 patients (8 per outcome group) and identify molecular features related to disease outcome in patients who underwent primary debulking surgery and platinum-based chemotherapy.
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By Modern Pathology
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Our host discusses with Dr. Anna Laury, from the Department of Pathology, University of Helsinki, Finland, her team's study on the utilization of AI-guided spatial transcriptomic analysis to allow for the biological interpretation of morphologic features detected by AI algorithms when applied to WSI of standard H&E sections.
The authors previously trained an AI model to identify HGSC (high-grade serous carcinoma of the ovary) tumor regions that are highly associated with outcome status but are indistinguishable by conventional morphologic methods. In the here discussed study, Dr. Laury’s team applied spatially resolved transcriptomics to further profile the AI-identified tumor regions in 16 patients (8 per outcome group) and identify molecular features related to disease outcome in patients who underwent primary debulking surgery and platinum-based chemotherapy.
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