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Machine learning-driven probability of cancer therapy–related cardiac dysfunction in patients with non-Hodgkin lymphoma

28 August 2026 (12:15 - 13:00)
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Congress Presentation Part of: Predicting cancer therapy–related cardiac dysfunction: from artificial intelligence-enabled assessment to risk refinement Cardio-Oncology ESC2026 Premium Access ESC Congress 2026 European Society of Cardiology

About the speaker

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Doctor Daniela Bursacovschi

Institute of Cardiology, Chisinau (Moldova (Republic of))

6 More presentations in this session

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Baseline early diastolic mitral annular velocities and cancer therapy related cardiac dysfunction in HER2 therapy
Mr A. Chowdhury (Clayton, AU)
Abstract Slides
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Incremental value of TAPSE added to HFA-ICOS for ESC-Defined Cancer Therapy–Related Cardiac Dysfunction
Doctor N. Espinola-Zavaleta (Mexico City, MX)
Abstract Slides Video
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Incidence and severity of cancer-therapy-related cardiac dysfunction during contemporary breast cancer therapy in a real-world cohort
Doctor V. Vinje (Nordbyhagen, NO)
Abstract Slides Video
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Agreement between manual and AI-enhanced echocardiographic assessment of cancer therapy-related cardiac dysfunction in patients with early breast cancer
Mr E. Orstad (Nordbyhagen, NO)
Abstract Slides Video
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Cardiopulmonary fitness and cancer therapy-related cardiac dysfunction defined by CMR: insights from the EMBRACE-MRI study
Doctor T. Goncalves (Toronto, CA)
Abstract Slides Video

Access the full session

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Predicting cancer therapy–related cardiac dysfunction: from artificial intelligence-enabled assessment to risk refinement

The Event

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ESC Congress 2026

28 August 2026 12:15 CET
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