Multimodal Fusion Artificial Intelligence Model to Predict Risk for MACE and Myocarditis in Cancer Patients Receiving Immune Checkpoint Inhibitor Therapy
A multimodal fusion AI model to predict myocarditis and adverse cardiovascular events in cancer patients starting ICI therapy had good prognostic performance. It may have clinical utility in identifying at-risk patients who may benefit from closer surveillance.
Authors
- Milagros Pereyra (ORCID: https://orcid.org/0000-0003-4695-8436)
- Chadi Ayoub (ORCID: https://orcid.org/0000-0001-5877-7478)
- Ahmed K. Mahmoud
- Imon Banerjee (ORCID: https://orcid.org/0000-0002-3327-8004)
- Chieh‐Ju Chao (ORCID: https://orcid.org/0000-0001-6155-0266)
- Isabel G. Scalia (ORCID: https://orcid.org/0000-0002-6459-9767)
- Nobuhisa Baba
- Bhavik N. Patel (ORCID: https://orcid.org/0000-0001-5157-9903)
- Jiwoong Jeong (ORCID: https://orcid.org/0000-0001-5630-9443)
- Juan Farina (ORCID: https://orcid.org/0000-0002-5824-8485)
- Mohammed Tiseer Abbas
- Steven J. Lester
- Reza Arsanjani
- Lalith Appari
Institutions
- Mayo Clinic (US)
- WinnMed (US)
- Mayo Clinic Hospital (US)
- Mayo Clinic in Florida (US)
Publication Details
- Journal
- JACC Advances
- Published
- 2024-12-13
- DOI
- https://doi.org/10.1016/j.jacadv.2024.101435
- Citations
- 11
- Primary Topic
- Cancer Immunotherapy and Biomarkers
- Type
- article
- Field-Weighted Citation Impact
- 3.11