Why the study?
Anthracyclines carry a risk of cancer therapy-related cardiac dysfunction in pediatric cancer treatment, prompting the use of unsupervised machine learning to categorize distinct phenotypes.
Can unsupervised machine learning identify distinct phenotypes associated with cancer therapy-related cardiac dysfunction in pediatric patients treated with anthracyclines?
Can unsupervised machine learning identify distinct phenotypes associated with cancer therapy-related cardiac dysfunction in pediatric patients treated with anthracyclines?
Unsupervised machine learning can identify distinct clinical and echocardiographic phenogroups in pediatric cancer patients that predict the risk of anthracycline-induced cardiac dysfunction.
Supports phenotyping for CTRCD risk in pediatric anthracycline recipients; leaves open clinical utility pending prospective validation.
BACKGROUND: Anthracyclines are essential in pediatric cancer treatment, but patients are at risk cancer therapy-related cardiac dysfunction (CTRCD). Standardized definitions by the International Cardio-Oncology Society (IC-OS) aim to enhance precision in risk assessment. OBJECTIVES: Categorize distinct phenotypes among pediatric patients undergoing anthracycline chemotherapy using unsupervised machine learning. METHODS: Pediatric cancer patients undergoing anthracycline chemotherapy at our institution were retrospectively included. Clinical and echocardiographic data at baseline, along with follow-up data, were collected from patient records. Unsupervised machine learning was performed, involving dimensionality reduction using principal component analysis and K-means clustering to identify different phenotypic clusters. Identified phenogroups were analyzed for associations with CTRCD, defined following contemporary IC-OS definitions, and hypertensive response. RESULTS: A total of 187 patients (63.1% male, median age 15.5 years [10.4-18.7]) were included and received anthracycline chemotherapy with a median treatment duration of 0.66 years [0.35-1.92]. Median follow-up duration was 2.78 years [1.31-4.21]. Four phenogroups were identified with following distribution: Cluster 0 (32.6%, n = 61), Cluster 1 (13.9%, n = 26), Cluster 2 (24.6%, n = 46), and Cluster 3 (28.9%, n = 54). Cluster 0 showed the highest risk of moderate CTRCD (HR: 3.10 [95% CI: 1.18-8.16], P = 0.022) compared to other clusters. Cluster 3 demonstrated a protective effect against hypertensive response (HR: 0.30 [95% CI: 0.13- 0.67], P = 0.003) after excluding baseline hypertensive patients. Longitudinal assessments revealed differences in global longitudinal strain and systolic blood pressure among phenogroups. CONCLUSIONS: Unsupervised machine learning identified distinct phenogroups among pediatric cancer patients undergoing anthracycline chemotherapy, offering potential for personalized risk assessment.
No takes yet. Share an insight, caveat, or question.
Jacquemyn et al. (2024) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: