Artificial intelligence opportunities in cardio-oncology: Overview with spotlight on electrocardiography

Lara-Martinez, D Sierra; Noseworthy, Peter A.; Akbilgiç, Oğuz; Herrmann, Joerg; Ruddy, Kathryn J.; Hamid, Abdulaziz; Maddula, Ragasnehith; Singh, Ashima; Davis, Robert L.; Güntürkün, Fatma; Jefferies, John L.; Brown, Sherry-Ann

doi:10.1016/j.ahjo.2022.100129

Cited by 17 publications

(10 citation statements)

References 133 publications

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“…3,9 Given its ubiquitous nature and low cost, ECG is widely endorsed as a class I recommendation for the baseline cardiovascular risk stratification before initiating potentially cardiotoxic chemotherapy, including anthracyclines and/or trastuzumab. 3,15 Transthoracic echocardiography (TTE), 31 including strain imaging by GLS, 17,18 and circulating biomarker characterization with natriuretic peptide and troponin measurements, 3,[31][32][33][34] are also routinely recommended according to the risk profile of the proposed regimen and each individual patient. More recently, multimodal imaging with magnetic resonance imaging, including T1 mapping, extracellular volume (ECV) quantification, 11,14 or positron emission tomography with radiotracers tracking myocardial function, metabolism, and tissue repair have also shown value in pre-clinical or small clinical studies.…”

Section: Discussionmentioning

confidence: 99%

“…5–7 Recent guideline statements have summarized a growing body of literature on the appropriate risk stratification, prevention, and management of comorbid or incident cardiovascular disease, 3, 8, 9 boosted by the development in non-invasive biomarkers, 10 multimodal imaging, 11 and, more recently, artificial intelligence (AI). 12–15…”

Section: Introductionmentioning

confidence: 99%

“…[5][6][7] Recent guideline statements have summarized a growing body of literature on the appropriate risk stratification, prevention, and management of comorbid or incident cardiovascular disease, 3,8,9 boosted by the development in non-invasive biomarkers, 10 multimodal imaging, 11 and, more recently, artificial intelligence (AI). [12][13][14][15] Despite this, the accurate identification of high-risk individuals before the initiation of therapy remains an elusive goal. A recent systematic review concluded that there is currently insufficient evidence for personalized risk prediction tools in this population.…”

Section: Introductionmentioning

confidence: 99%

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Artificial intelligence-enhanced risk stratification of cancer therapeutics-related cardiac dysfunction using electrocardiographic images

Oikonomou,

Sangha,

Dhingra

et al. 2024

Preprint

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Background: Risk stratification strategies for cancer therapeutics-related cardiac dysfunction (CTRCD) rely on serial monitoring by specialized imaging, limiting their scalability and affordability. Objectives: To examine an artificial intelligence (AI)-enhanced electrocardiographic (AI-ECG) surrogate for imaging-based risk biomarkers, and its association with CTRCD. Methods: Across five hospitals of a U.S.-based health system (2013-2023), we identified patients with breast cancer or non-Hodgkin lymphoma (NHL) who received anthracyclines (AC) and/or trastuzumab (TZM), as well as a (negative) control cohort of patients receiving immune checkpoint inhibitors (ICI). We deployed a validated AI model of left ventricular systolic dysfunction (LVSD) to ECG images (>=0.1, positive screen) and explored the association between the model's predictions and: i) global longitudinal strain (GLS) measurements within 15 days; ii) future CTRCD (new cardiomyopathy, heart failure, or left ventricular ejection fraction (LVEF) decrease to <50%), and LVEF decrease <40%. In a negative control analysis of patients receiving ICI, we correlated baseline AI-ECG LVSD predictions with downstream myocarditis. Results: Higher AI-ECG LVSD predictions were associated with progressively worse GLS (-18% [IQR: -20 to -17%] for AI-ECG predictions <0.1, to -12% [-15 to -9%] for >=0.5 (p<0.001), n=7,271). In 1,308 patients receiving AC/TZM (age 59 [IQR 49-67] years, 999 [76.4%] women, 80 [IQR 42-115] follow-up months) a positive baseline AI-ECG LVSD screen was associated with ~2-fold and ~4.8-fold increase in the incidence of CTRCD (HRadjusted 2.22 [95%CI: 1.63-3.02]), and LVEF <40% (HRadjusted 4.76 [95%CI: 2.62-8.66], p<0.001), respectively. Among 2,056 patients receiving ICI (age 65 [IQR 57-73] years, 913 (44.4%) women, follow-up 63 [IQR 28-99] months) AI-ECG predictions were not associated with ICI myocarditis risk (HRadjusted 1.36 [0.47-3.93]). Conclusion: AI applied to baseline ECG images can stratify the risk of CTRCD associated with anthracycline or trastuzumab exposure.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Artificial intelligence-enhanced risk stratification of cancer therapeutics-related cardiac dysfunction using electrocardiographic images

Oikonomou,

Sangha,

Dhingra

et al. 2024

Preprint

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“…For the purpose of better understanding similarities and relations between cardiooncology and COVID-19, some common biological pathways between them will be discussed below [18].…”

Section: Global Health System In Covid-19 Pandemicmentioning

confidence: 99%

Cardio-Oncology and the COVID-19 Pandemic

Mortezaei¹,

Hosseini²

2023

Novel Pathogenesis and Treatments for Cardiovascular Disease

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As one of the novel interesting fields of cardiology, cardio-oncology focuses on monitoring, detecting, and treating cardiovascular diseases caused due to chemotherapy or radiotherapy side effects. It has been observed that cardiovascular patients have a higher risk of viral infections and poorer treatment outcomes. COVID-19 is a disease caused by the new coronavirus, SARS-CoV-2, which emerged in Wuhan, China, in 2019 and then distributed worldwide. Recent evidence showed that the risk of COVID-19 and its mortality rate is higher in patients suffering from cardiovascular side effects of cancer therapies. Additional diagnosis complexity in cardio-oncology is another problem due to overlapping with COVID-19. Therefore, the cardio-oncology community had to re-evaluate the best clinical care in the COVID-19 pandemic. The present study aims to review previous studies focusing on the interaction between COVID-19 and cardio-oncology, which will pave the way for studying human diseases overlapping with COVID-19.

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“…Cardiovascular disease is a leading cause of mortality among cancer survivors globally, exceeding 370,000 deaths annually, followed by the risk of new tumour development ( 1 ). The field of cardio-oncology has emerged due to heightened awareness of cardiac dysfunction resulting from cancer therapies like chemotherapy, radiotherapy, and immunotherapy.…”

mentioning

confidence: 99%

Editorial: Interpretable predictive analytics for precision cardio-oncology preventive care

Zhou,

Liu,

Roever

et al. 2024

Front. Cardiovasc. Med.

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Artificial intelligence opportunities in cardio-oncology: Overview with spotlight on electrocardiography

Cited by 17 publications

References 133 publications

Artificial intelligence-enhanced risk stratification of cancer therapeutics-related cardiac dysfunction using electrocardiographic images

Artificial intelligence-enhanced risk stratification of cancer therapeutics-related cardiac dysfunction using electrocardiographic images

Cardio-Oncology and the COVID-19 Pandemic

Editorial: Interpretable predictive analytics for precision cardio-oncology preventive care

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