Predicting immune checkpoint inhibitor cardiotoxicity using machine learning: a systematic review of model performance and methodological quality

Clicks: 2
ID: 323437
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #256 of 282 articles by views in European heart journal supplements : journal of the European Society of Cardiology

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 282 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Abstract Background Immune checkpoint inhibitors (ICIs) significantly improve cancer outcomes but can cause rare, potentially fatal cardiotoxicity, including myocarditis and major adverse cardiovascular events (MACE). Machine learning (ML) and artificial intelligence (AI) models have emerged as promising tools for early detection and risk prediction; however, their performance, methodological quality, and clinical applicability remain unclear. Objective To systematically evaluate ML-based prediction models developed to identify cardiotoxicity, myocarditis, or cardiac adverse events in patients receiving ICIs, and assess their methodological robustness using PROBAST. Methods A systematic search of PubMed (18–20 November 2025) was performed using a multi-stage strategy (broad → refined → high-specificity). Seven studies met eligibility criteria. Data extraction covered population, predictors, outcomes, model types, validation, and performance metrics. Risk of bias was assessed using PROBAST. Supplementary files include full search strategies, extraction sheets, PRISMA flow diagram, and PROBAST tables. Results Seven ML models were identified, including XGBoost, multimodal deep learning, neural networks, ECG-based AI models, pharmacovigilance-driven ML, and two clinical nomograms. Sample sizes ranged from 23 to 4,282 patients. Reported AUC values varied: XGBoost, 0.92; Fusion AI, 0.88; FAERS ML, 0.83; ECG AI, 0.91; Time-series NN, predictive ranking only; Nomogram, 0.83–0.967. Troponin, ECG features, neutrophil–lymphocyte ratio, NT-proBNP, and clinical factors such as steroid dose and radiotherapy emerged as consistent predictors. PROBAST identified high risk of bias in three models (mainly due to small datasets, inadequate validation, and unclear handling of missing data). Two studies using independent validation cohorts achieved low risk of bias. Conclusion ML-based models show promising discriminative ability for predicting ICI-related cardiotoxicity and myocarditis; however, most studies suffer from methodological limitations that restrict real-world clinical adoption. Larger multicenter cohorts, standardized definitions, multimodal integration, and adherence to TRIPOD-AI reporting guidelines are needed to advance clinically deployable models.
Reference Key
openalex_W7172270563 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors V Roy
Journal European heart journal supplements : journal of the European Society of Cardiology
Year 2026
DOI
10.1093/eurheartjsupp/suag097.207
URL
https://doi.org/10.1093/eurheartjsupp/suag097.207
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.