Machine learning enhanced baseline risk prediction models for cancer therapy related cardiac dysfunction: a systematic review and critical appraisal of methodological quality

Clicks: 6
ID: 323529
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

Ranked #27 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 Cancer therapy-related cardiac dysfunction (CTRCD) is a prevalent complication with adverse clinical implications. Major guidelines have emphasised the need for reliable baseline risk prediction to guide prevention strategies. While machine learning (ML) has shown promise in cardiovascular medicine, its readiness for application in cardio-oncology remains unexplored. Aim To systematically review and critically appraise the performance, methodological quality, and clinical applicability of ML-based models for predicting baseline CTRCD risk. Methods A search on Embase and Medline was conducted up to April 2025. Eligible studies used at least one ML algorithm to estimate CTRCD risk prior to cancer treatment. Data were extracted using an adapted version of the CHARMS checklist. Risk of bias (ROB) and reporting quality were assessed using PROBAST and TRIPOD+AI respectively. Results Seven studies were included. Five developed novel models using multiple predictors, and two repurposed or validated existing AI-enhanced electrocardiography tools. While their levels of discrimination were at least moderate (AUC 0.65–0.88), calibration was not reported in any of the studies and only one study included a small external validation cohort. ECG-only models showed modest performance, whereas models integrating clinical and imaging predictors performed better but were methodologically constrained. Four studies scored high ROB, one low, and two unclear. Common limitations included small datasets with low number of events, limited transparency in reporting, and lack of reproducibility measures. Conclusions Current ML-based models for predicting CTRCD are underdeveloped and not ready for clinical deployment. The present findings underscore the need for more rigorous and standardised study designs, with greater emphasis on methodological transparency with clearly-defined adjudicated outcomes and external validation to improve reproducibility.Central Illustration Studies and models characteristics
Reference Key
openalex_W7172258425 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors M Andres, V Maharajan, A Pons Rivarola, F Thuny, S Ramalingam, M Howe, M A Mamas, A Banarjee, C Manisty, A Gosh, A Bharadwaj, A R Lyon, M Mohamed
Journal European heart journal supplements : journal of the European Society of Cardiology
Year 2026
DOI
10.1093/eurheartjsupp/suag097.208
URL
https://doi.org/10.1093/eurheartjsupp/suag097.208
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.