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Peer-reviewedCC BY 4.0Cancers

Precision Cardio-Oncology: A Multimodal Approach to Cancer Therapy-Related Cardiovascular Toxicity Prediction and Assessment

  • Carl Simela1,2
  • Ishikaa Mukherjee3
  • Abhinav Kandala4
  • Aruni Ghose1,2,5,6
  • Turja Chakraborty7,8
  • Navin Mathiyalagan9
  • Amitava Banerjee10
  • Arjun K. Ghosh1,2,11,12
12 affiliations
  1. 1Cardio-Oncology Service, University College London Hospitals NHS Foundation Trust, London NW1 2PG, UK
  2. 2Hatter Cardiovascular Institute, University College London, London WC1E 6HX, UK
  3. 3Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London E1 2AD, UK
  4. 4Faculty of Medical Sciences, University College London, London WC1E 6BT, UK
  5. 5Velindre Cancer Centre, Velindre University NHS Trust, Cardiff CF14 2TL, UK
  6. 6Global Business School for Health, Faculty of Population Health Sciences, University College London, London E20 2AE, UK
  7. 7Department of Cardiology, London North West University Healthcare NHS Trust, London HA1 3UJ, UK
  8. 8National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London SW3 6LY, UK
  9. 9Oncology and Cardio-Oncology Services, Nottingham University Hospitals NHS Trust, Nottingham NG7 2UH, UK
  10. 10Institute of Health Informatics, Faculty of Population Health Sciences, University College London, London NW1 2DA, UK
  11. 11Cardio-Oncology Service, Barts Heart Centre, Barts Health NHS Trust, London EC1A 7BE, UK
  12. 12William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Faculty of Medicine and Dentistry, Queen Mary University of London, London EC1M 6BQ, UK
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Abstract

Cancer therapy-related cardiovascular toxicity (CTR-CVT) is associated with morbidity, mortality, and interruptions to cancer treatment. Current cardio-oncology clinical assessment tools generally rely on a limited range of clinical data; however, in the era of precision medicine, it may be possible to leverage multimodal data, including imaging and multi-omic inputs, to personalise CTR-CVT prediction and assessment. In this review, we examine the genotypic and phenotypic predictors of CTR-CVT and the evidence supporting their incorporation into multimodal risk models. Most research focuses on cancer therapy-related cardiac dysfunction (CTRCD). We identify a strong evidence base for multimodal imaging-guided prediction, particularly cardiovascular magnetic resonance (CMR) and transthoracic echocardiography (TTE)-derived global longitudinal strain measurements. Emerging evidence also supports the potential value of novel imaging parameters, including computed tomography (CT) and TTE-derived radiomic features. From the genomic and proteomic perspective, truncating titin variants and microRNA-126 may be informative predictors of cancer therapy-related cardiac dysfunction (CTRCD). At the same time, the literature also highlights potential epigenomic markers of CTR-CVT. Finally, we consider how these diverse data can be integrated into multimodal risk models and how digital innovation and advanced machine learning methods may further improve model predictive performance. However, a large implementation gap remains, with challenges such as data privacy and a lack of external validation studies preventing routine clinical implementation.

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Cite

BibTeXRIS
BibTeX
@article{simela2026precision,
  title = {{Precision Cardio-Oncology: A Multimodal Approach to Cancer Therapy-Related Cardiovascular Toxicity Prediction and Assessment}},
  author = {Carl Simela and Ishikaa Mukherjee and Abhinav Kandala and Aruni Ghose and Turja Chakraborty and Navin Mathiyalagan and Amitava Banerjee and Arjun K. Ghosh},
  journal = {Cancers},
  year = {2026},
  month = oct,
  doi = {10.3390/cancers18203271},
  url = {https://www.mdpi.com/2072-6694/18/20/3271},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Precision Cardio-Oncology: A Multimodal Approach to Cancer Therapy-Related Cardiovascular Toxicity Prediction and Assessment
AU  - Carl Simela
AU  - Ishikaa Mukherjee
AU  - Abhinav Kandala
AU  - Aruni Ghose
AU  - Turja Chakraborty
AU  - Navin Mathiyalagan
AU  - Amitava Banerjee
AU  - Arjun K. Ghosh
PY  - 2026
DA  - 2026/10/01
JO  - Cancers
DO  - 10.3390/cancers18203271
UR  - https://www.mdpi.com/2072-6694/18/20/3271
AB  - Cancer therapy-related cardiovascular toxicity (CTR-CVT) is associated with morbidity, mortality, and interruptions to cancer treatment. Current cardio-oncology clinical assessment tools generally rely on a limited range of clinical data; however, in the era of precision medicine, it may be possible to leverage multimodal data, including imaging and multi-omic inputs, to personalise CTR-CVT prediction and assessment. In this review, we examine the genotypic and phenotypic predictors of CTR-CVT and the evidence supporting their incorporation into multimodal risk models. Most research focuses on cancer therapy-related cardiac dysfunction (CTRCD). We identify a strong evidence base for multimodal imaging-guided prediction, particularly cardiovascular magnetic resonance (CMR) and transthoracic echocardiography (TTE)-derived global longitudinal strain measurements. Emerging evidence also supports the potential value of novel imaging parameters, including computed tomography (CT) and TTE-derived radiomic features. From the genomic and proteomic perspective, truncating titin variants and microRNA-126 may be informative predictors of cancer therapy-related cardiac dysfunction (CTRCD). At the same time, the literature also highlights potential epigenomic markers of CTR-CVT. Finally, we consider how these diverse data can be integrated into multimodal risk models and how digital innovation and advanced machine learning methods may further improve model predictive performance. However, a large implementation gap remains, with challenges such as data privacy and a lack of external validation studies preventing routine clinical implementation.
SN  - 2072-6694
ER  -