Machine Learning Identifies Clinical and Genetic Factors Associated With Anthracycline Cardiotoxicity in Pediatric Cancer Survivors

Chaix, Marie; Parmar, Neha; Kinnear, Caroline; Lafrenière-Roula, Myriam; Akinrinade, Oyediran; Yao, Roderick; Miron, Anastasia; Lam, Emily; Meng, Guoliang; Christie, Anne; Manickaraj, Ashok Kumar; Marjerrison, Stacey; Dillenburg, Rejane; Bassal, Mylène; Lougheed, Jane; Zelcer, Shayna; Rosenberg, Hans; Hodgson, David C.; Sender, Leonard S.; Kantor, Paul F.; Manlhiot, Cedric; Ellis, James; Mertens, Luc; Nathan, Paul C.; Mital, Seema

doi:10.1016/j.jaccao.2020.11.004

Cited by 20 publications

(16 citation statements)

References 38 publications

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Section: Introductionmentioning

confidence: 99%

“…Through a comprehensive search of PubMed databases, we found that although there have been many studies (6)(7)(8)(9)(10)(11)(12)(13) on the various risk factors for cardiotoxicity caused by anthracyclines, most of the clinical studies (6-11) conducted both within and outside of China have been confined to univariate analysis and multivariate analysis. To date, no effective risk prediction model of cardiotoxicity risk factors has been established in addition to the two studies mention above (12,13). Therefore, it is necessary to develop a prognostic model in which patients with cancer received doxorubicin liposomes as the core of chemotherapy, to guide clinical stratification according to the risk factors and further achieve safe and rational drug use.…”

Section: Introductionmentioning

confidence: 99%

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Development of a multivariable clinical prediction model for liposomal doxorubicin-induced cardiotoxicity in adult breast cancer patients: a retrospective multicenter study

Sun¹,

Ping²,

Miao³

et al. 2022

Ann Transl Med

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Background: The clinical use of anthracyclines is limited by the risk of cardiotoxicity. So, we aim to develop a clinical prediction model for liposomal doxorubicin-induced cardiotoxicity in adult breast cancer patients. Methods:We designed a multicenter retrospective cohort study. A total of 257 hospitalized breast cancer patients treated with doxorubicin liposomes were finally enrolled in the study, including 58 patients from Beijing Friendship Hospital and 199 from Beijing Cancer Hospital. In all, 32 cases developed cardiotoxicity, including 4 at the Beijing Friendship Hospital and 28 at the Beijing Cancer Hospital. The study involved breast cancer patients with no pre-existing heart disease, whose clinical data were collected from their medical records. All patients underwent electrocardiogram (ECG) and/or left ventricular ejection fraction (LVEF) measurements prior to treatment with doxorubicin liposomes. Patients were clinically assessed after each cycle of treatment, and ECG and/or LVEF measurements were performed at least once after treatment.Liposomal doxorubicin-induced cardiotoxicity was defined when one of the following three conditions was met: (I) a reduction in LVEF of at least 5% from the baseline and the absolute value was less than 55%, accompanied by congestive heart failure (CHF) symptoms or signs; (II) a reduction in LVEF of at least 10% to an absolute value of less than 55%, without CHF symptoms or signs; (III) the definite diagnosis of CHF.Variables associated with cardiotoxicity were identified by univariate and multivariate logistic regression, and the consistency and differentiation of the final model were evaluated.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Development of a multivariable clinical prediction model for liposomal doxorubicin-induced cardiotoxicity in adult breast cancer patients: a retrospective multicenter study

Sun¹,

Ping²,

Miao³

et al. 2022

Ann Transl Med

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“…At the pre-clinical stage, AI techniques have been used for high-throughput screening of cancer agents using a variety of disease models. These range from human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) exposed to antineoplastic agents, screening of drug libraries to detect agents that interact with channel proteins resulting in QT prolongation, all the way to exome sequencing to identify variants in cardiac injury pathway genes that protect against anthracycline-induced cardiotoxicity and dual transcriptomic and molecular machine learning to predict different types of cardiotoxic response (146)(147)(148)(149)(150). Such approaches can de-risk early-stage drug discovery but also contribute to post-marketing surveillance to maximize patient safety.…”

Section: Overview Of Current Ai Applications In Cardio-oncologymentioning

confidence: 99%

Multimodality Advanced Cardiovascular and Molecular Imaging for Early Detection and Monitoring of Cancer Therapy-Associated Cardiotoxicity and the Role of Artificial Intelligence and Big Data

Kwan

Oikonomou

Henry

et al. 2022

Front. Cardiovasc. Med.

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Cancer mortality has improved due to earlier detection via screening, as well as due to novel cancer therapies such as tyrosine kinase inhibitors and immune checkpoint inhibitions. However, similarly to older cancer therapies such as anthracyclines, these therapies have also been documented to cause cardiotoxic events including cardiomyopathy, myocardial infarction, myocarditis, arrhythmia, hypertension, and thrombosis. Imaging modalities such as echocardiography and magnetic resonance imaging (MRI) are critical in monitoring and evaluating for cardiotoxicity from these treatments, as well as in providing information for the assessment of function and wall motion abnormalities. MRI also allows for additional tissue characterization using T1, T2, extracellular volume (ECV), and delayed gadolinium enhancement (DGE) assessment. Furthermore, emerging technologies may be able to assist with these efforts. Nuclear imaging using targeted radiotracers, some of which are already clinically used, may have more specificity and help provide information on the mechanisms of cardiotoxicity, including in anthracycline mediated cardiomyopathy and checkpoint inhibitor myocarditis. Hyperpolarized MRI may be used to evaluate the effects of oncologic therapy on cardiac metabolism. Lastly, artificial intelligence and big data of imaging modalities may help predict and detect early signs of cardiotoxicity and response to cardioprotective medications as well as provide insights on the added value of molecular imaging and correlations with cardiovascular outcomes. In this review, the current imaging modalities used to assess for cardiotoxicity from cancer treatments are discussed, in addition to ongoing research on targeted molecular radiotracers, hyperpolarized MRI, as well as the role of artificial intelligence (AI) and big data in imaging that would help improve the detection and prognostication of cancer-treatment cardiotoxicity.

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“…Another research group recently used machine learning algorithms to develop a clinical and genetic risk prediction model for anthracycline cardiotoxicity in survivors of childhood cancer [ 129 ]. The study authors sequenced the exomes of 289 childhood cancer survivors who had been exposed to anthracyclines for at least three years.…”

Section: Ai In Precision and Translational Cardio-oncologymentioning

confidence: 99%

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

Lara-Martinez

Noseworthy

Akbilgiç

et al. 2022

American Heart Journal Plus: Cardiology Research and Practice

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Machine Learning Identifies Clinical and Genetic Factors Associated With Anthracycline Cardiotoxicity in Pediatric Cancer Survivors

Cited by 20 publications

References 38 publications

Development of a multivariable clinical prediction model for liposomal doxorubicin-induced cardiotoxicity in adult breast cancer patients: a retrospective multicenter study

Development of a multivariable clinical prediction model for liposomal doxorubicin-induced cardiotoxicity in adult breast cancer patients: a retrospective multicenter study

Multimodality Advanced Cardiovascular and Molecular Imaging for Early Detection and Monitoring of Cancer Therapy-Associated Cardiotoxicity and the Role of Artificial Intelligence and Big Data

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

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