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A machine learning (ML) model using two preprocedural cardiac CT-derived metrics, automatically extractable with open-source tools, predicts 1-year mortality after TAVI.
It was showed that current prognostic models incompletely capture the biological heterogeneity underlying CRLM recurrence, and dynamic biomarkers and treatment-response indicators may support future multimodal prognostic frameworks and improve risk stratification following curative-intent treatment.
By combining structured clinical variables and multimodal imaging data, the AI-HOPE Lung Cancer study aims to support refined risk stratification and treatment personalization, ultimately facilitating the responsible integration of AI into routine thoracic oncology practice.
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