Miller KD, et al. Cancer treatment and survivorship statistics, 2022. CA Cancer J Clin. 2022;72(5):409–36.

Article  PubMed  Google Scholar 

Sławiński G et al. Immune Checkpoint inhibitors and Cardiac Toxicity in patients treated for Non-small Lung Cancer: a review. Int J Mol Sci, 2020. 21(19).

Mozdzanowska D, Woźniewski M. Radiotherapy and anthracyclines - cardiovascular toxicity. Contemp Oncol (Pozn). 2015;19(2):93–7.

PubMed  Google Scholar 

de Boer RA, et al. Cancer and heart disease: associations and relations. Eur J Heart Fail. 2019;21(12):1515–25.

Article  PubMed  Google Scholar 

Lyon AR, et al. Baseline cardiovascular risk assessment in cancer patients scheduled to receive cardiotoxic cancer therapies: a position statement and new risk assessment tools from the Cardio-Oncology Study Group of the Heart Failure Association of the European Society of Cardiology in collaboration with the International Cardio-Oncology Society. Eur J Heart Fail. 2020;22(11):1945–60.

Article  PubMed  Google Scholar 

Li YH, et al. Innovation and challenges of artificial intelligence technology in personalized healthcare. Sci Rep. 2024;14(1):18994.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Moslehi JJ. Cardiovascular toxic effects of targeted Cancer therapies. N Engl J Med. 2016;375(15):1457–67.

Article  CAS  PubMed  Google Scholar 

Khera R, et al. Transforming Cardiovascular Care with Artificial Intelligence: from Discovery to Practice: JACC State-of-the-art review. J Am Coll Cardiol. 2024;84(1):97–114.

Article  PubMed  Google Scholar 

Echefu G, et al. The Digital Revolution in Medicine: applications in Cardio-Oncology. Current Treatment Options in Cardiovascular Medicine; 2024.

Poalelungi DG et al. Advancing patient care: how Artificial Intelligence is transforming Healthcare. J Pers Med, 2023;13(8).

Qian L et al. AI-empowered perturbation proteomics for complex biological systems. Cell Genomics, 2024;4.

Chang WT, et al. An artificial intelligence approach for predicting cardiotoxicity in breast cancer patients receiving anthracycline. Arch Toxicol. 2022;96(10):2731–7.

Article  CAS  PubMed  Google Scholar 

Hanneman K, et al. Value Creation through Artificial Intelligence and Cardiovascular Imaging: A Scientific Statement from the American Heart Association. Circulation. 2024;149(6):e296–311.

Article  PubMed  Google Scholar 

O’Driscoll JM, et al. Left ventricular assessment with artificial intelligence increases the diagnostic accuracy of stress echocardiography. Eur Heart J Open. 2022;2(5):oeac059.

Article  PubMed  PubMed Central  Google Scholar 

Battisha A, et al. Clinical applications and advancements of Positron Emission Tomography/Computed Tomography in Cardio-Oncology: a Comprehensive Literature Review and emerging perspectives. Curr Oncol Rep; 2024.

Oikonomou EK, et al. Artificial Intelligence-enhanced risk stratification of Cancer therapeutics-related Cardiac Dysfunction using Electrocardiographic images. Circ Cardiovasc Qual Outcomes; 2024.

Stefanovic F, et al. Neural net modeling of checkpoint inhibitor related myocarditis and Steroid Response. Clin Pharmacol. 2022;14:69–90.

PubMed  PubMed Central  Google Scholar 

Heilbroner SP et al. Predicting cardiac adverse events in patients receiving immune checkpoint inhibitors: a machine learning approach. J Immunother Cancer, 2021;9(10).

Elsaid MI, Meara AS, Owen DH. Role for Artificial Intelligence in the detection of Immune-related adverse events. J Clin Oncol, 2024;Jco2401570.

Sadler D, et al. Cardio oncology: Digital innovations, precision medicine and health equity. Front Cardiovasc Med. 2022;9:951551.

Article  PubMed  PubMed Central  Google Scholar 

Brown SA, et al. Patient similarity and other artificial intelligence machine learning algorithms in clinical decision aid for shared decision-making in the Prevention of Cardiovascular toxicity (PACT): a feasibility trial design. Cardiooncology. 2023;9(1):7.

PubMed  PubMed Central  Google Scholar 

Lal JC, Cheng F. Artificial Intelligence for Risk Assessment of Cancer Therapy-Related Cardiotoxicity and Precision Cardio-Oncology, in Machine Learning and Deep Learning in Computational Toxicology, H. Hong, Editor. 2023, Springer International Publishing: Cham. pp. 563–578.

Deng YT et al. Atlas of the plasma proteome in health and disease in 53,026 adults. Cell, 2024.

Vandenberk B, et al. Successes and challenges of artificial intelligence in cardiology. Front Digit Health. 2023;5:1201392.

Article  PubMed  PubMed Central  Google Scholar 

Beam AL, Manrai AK, Ghassemi M. Challenges to the Reproducibility of Machine Learning Models in Health Care. JAMA. 2020;323(4):305–6.

Article  PubMed  PubMed Central  Google Scholar 

Goetz L, et al. Generalization-a key challenge for responsible AI in patient-facing clinical applications. NPJ Digit Med. 2024;7(1):126.

Article  PubMed  PubMed Central  Google Scholar 

Mittermaier M, Raza MM, Kvedar JC. Bias in AI-based models for medical applications: challenges and mitigation strategies. NPJ Digit Med. 2023;6(1):113.

Article  PubMed  PubMed Central  Google Scholar 

Wu Y, Lin C. Unveiling the black box: imperative for explainable AI in cardiovascular disease prevention. Lancet Reg Health West Pac. 2024;48:101145.

PubMed  PubMed Central  Google Scholar 

Salih A, et al. Explainable Artificial Intelligence and Cardiac Imaging: toward more interpretable models. Circ Cardiovasc Imaging. 2023;16(4):e014519.

Article  PubMed  Google Scholar 

Brown SA et al. Establishing an interdisciplinary research team for cardio-oncology artificial intelligence informatics precision and health equity. Am Heart J Plus, 2022;13.

Hamid A, et al. Editorial: leveraging digital and technological innovations in cardio-oncology: building collaborative networks, implementing education and improving the cardiac outcomes of patients. Front Cardiovasc Med. 2023;10:1184988.

Article  PubMed  PubMed Central  Google Scholar 

Warraich HJ, Tazbaz T, Califf RM. FDA Perspective on the Regulation of Artificial Intelligence in Health Care and Biomedicine. JAMA, 2024.

Cuomo A, et al. Heart failure and Cancer: mechanisms of Old and New Cardiotoxic drugs in Cancer patients. Card Fail Rev. 2019;5(2):112–8.

Article  PubMed  PubMed Central  Google Scholar 

Lyon AR et al. 2022 ESC Guidelines on cardio-oncology developed in collaboration with the European Hematology Association (EHA), the European Society for Therapeutic Radiology and Oncology (ESTRO) and the International Cardio-Oncology Society (IC-OS). Eur Heart J, 2022;43(41):4229–4361.

Leong DP, et al. Cardiovascular risk in prostate Cancer. Volume 6. JACC: CardioOncology; 2024. pp. 835–46. 6.

Google Scholar 

Alexandre J, et al. Cardiovascular Toxicity related to Cancer Treatment: A Pragmatic Approach to the American and European Cardio-Oncology guidelines. J Am Heart Assoc. 2020;9(18):e018403.

Article  CAS  PubMed  PubMed Central  Google Scholar 

D’Agostino RB, Sr, et al. General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation. 2008;117(6):743–53.

Goff DC, et al. 2013 ACC/AHA Guideline on the Assessment of Cardiovascular Risk. Circulation. 2014;129(25suppl2):S49–73.

PubMed  Google Scholar 

Assmann G, Cullen P, Schulte H. Simple scoring scheme for calculating the risk of acute coronary events based on the 10-year follow-up of the prospective cardiovascular Münster (PROCAM) study. Circulation. 2002;105(3):310–5.

Article  PubMed  Google Scholar 

Hippisley-Cox J, et al. Predicting cardiovascular risk in England and Wales: prospective derivation and validation of QRISK2. BMJ. 2008;336(7659):1475–82.

Article  PubMed  PubMed Central  Google Scholar 

Woodward M, Brindle P, Tunstall-Pedoe H. Adding social deprivation and family history to cardiovascular risk assessment: the ASSIGN score from the Scottish Heart Health Extended Cohort (SHHEC). Heart. 2007;93(2):172–6.

Article  PubMed  Google Scholar 

Levy WC, et al. The Seattle Heart failure model: prediction of survival in heart failure. Circulation. 2006;113(11):1424–33.

Article  PubMed  Google Scholar 

SCORE2 risk. Prediction algorithms: new models to estimate 10-year risk of cardiovascular disease in Europe. Eur Heart J. 2021;42(25):2439–54.

Article  Google Scholar 

Mulas O, et al. The new systematic coronary risk evaluation (SCORE2 and SCORE2-OP) estimates the risk of arterial occlusive events in chronic myeloid leukemia patients treated with nilotinib or ponatinib. Ann Hematol. 2024;103(2):427–36.

Article  CAS  PubMed  Google Scholar 

Guha A et al. ASCVD risk scores versus a novel cancer-specific machine learning-based calculator among patients with breast, colorectal, lung or prostate cancer. Eur Heart J, 2024. 45(Supplement_1).

Shah V. Current Traditional Methods in Different Stages of Cardio-Oncology, in Created in BioRender. 2024.

Zhou Y, et al. Machine learning-based Risk Assessment for Cancer Therapy-Related Cardiac Dysfunction in 4300 Longitudinal Oncology patients. J Am Heart Assoc. 2020;9(23):e019628.

Article  PubMed  PubMed Central  Google Scholar 

Perez IE, et al. Cancer Therapy-Related Cardiac Dysfunction: an overview for the Clinician. Clin Med Insights Cardiol. 2019;13:1179546819866445.

Article  PubMed  PubMed Central  Google Scholar