Key result
Integrating AI, biomarkers, and advanced imaging enables earlier detection of subclinical cardiotoxicity than conventional LVEF.
Why the study?
Conventional surveillance for cancer therapy-related cardiovascular toxicity relies on LVEF assessment, which often detects declines only after substantial myocardial injury has already occurred. This limitation has created a need for more sensitive approaches using AI, biomarkers, and advanced imaging to identify subclinical cardiotoxicity earlier.
Does the integration of artificial intelligence, circulating biomarkers, and advanced cardiac imaging improve the early detection of subclinical cancer therapy-related cardiotoxicity in cancer patients and survivors?
Does the integration of artificial intelligence, circulating biomarkers, and advanced cardiac imaging improve the early detection of subclinical cancer therapy-related cardiotoxicity in cancer patients and survivors?
This review highlights how integrating artificial intelligence, circulating biomarkers, and advanced cardiac imaging can shift cardio-oncology care from reactive management of LVEF declines to proactive, personalized early detection of subclinical cardiotoxicity.
Cardiovascular surveillance in cancer survivors needs updating; leaves open optimal long-term strategies for this growing population.
The remarkable progress achieved in cancer treatment over the past several decades has transformed the prognosis of many malignancies. Improved survival has created a rapidly growing population of cancer survivors whose long-term health extends far beyond oncologic outcomes alone. Among the challenges faced by these individuals, cardiovascular toxicity has emerged as one of the most important causes of treatment-related morbidity and mortality. Conventional surveillance strategies have traditionally relied on the assessment of left ventricular ejection fraction (LVEF), yet declines in LVEF often become apparent only after substantial myocardial injury has already occurred. This limitation has stimulated interest in more sensitive approaches capable of identifying cardiovascular injury at a stage when preventive interventions may still preserve cardiac function. Precision cardio-oncology has emerged as a response to this need. By integrating artificial intelligence (AI), circulating biomarkers, advanced cardiac imaging, and individualized risk assessment, clinicians are beginning to move beyond reactive management toward proactive cardiovascular care. Recent developments in machine learning, deep learning, AI-enabled electrocardiography, myocardial strain imaging, cardiac magnetic resonance imaging, and biomarker-guided surveillance have expanded the ability to detect subclinical cardiotoxicity long before overt cardiac dysfunction becomes evident. These technologies are increasingly being combined within multimodal frameworks that seek to personalize monitoring strategies according to each patient's unique cardiovascular risk profile. This review explores the evolving role of AI, biomarkers, and advanced imaging in the early detection of cancer therapy–related cardiovascular toxicity and discusses how their integration may shape the future of precision cardio-oncology practice.
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Vrattika Singh (2026) conducted a review in Cancer therapy-related cardiovascular toxicity. Artificial intelligence, circulating biomarkers, and advanced cardiac imaging vs. Conventional LVEF surveillance was evaluated on Early detection of cancer therapy-related cardiotoxicity. Integrating AI, biomarkers, and advanced imaging enables earlier detection of subclinical cancer therapy-related cardiotoxicity compared with conventional LVEF surveillance.
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