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儿科瘤学中的心脏毒性:系统性审查和元分析
Nóra Beke1, Xenia Jockers1,2, Márk Hernádfői1,3
1Centre for Translational Medicine, Semmelweis University, Budapest, Hungary.
Pediatric research
|December 17, 2025
概括
在儿童癌症幸存者中早期发现心脏毒性是具有挑战性的. 人工智能 (AI) 模型有希望,但结合各种诊断工具的多式联络方法目前最有效用于监测儿科患者.
科学领域:
- 儿科心脏瘤学
- 心血管研究研究心血管研究
- 癌症的生存率 癌症的生存率
背景情况:
- 在儿童癌症幸存者中,抗环素化疗具有显著的心脏毒性风险.
- 早期发现亚临床心脏功能障碍对于及时干预至关重要,但目前的诊断方法缺乏明确的准确性.
- 儿童癌症幸存者的长期心血管健康问题越来越令人担忧.
研究的目的:
- 系统地审查和元分析各种诊断方式的预后准确性,以预测儿科患者的化疗诱导心脏毒性.
- 评估回声心脏学,血清生物标志物,microRNAs (miRNAs) 和人工智能 (AI) 模型在早期心脏毒性检测中的有效性.
- 确定差距,并指导未来的策略可靠的心脏毒性监测在儿科瘤学.
主要方法:
- 对73项研究的系统综述和元分析 (10项元分析) 评估了儿童化疗诱导心脏毒性的预后准确性.
- 对心声回声学 (包括全球纵向菌株 - GLS),血清生物标志物 (热波宁,NT-proBNP),miRNA和AI模型的评估.
- 使用QUAPAS工具进行质量评估;使用I2统计评估异质性.
主要成果:
- 人工智能 (AI) 模型展示了最高的聚合预测性能 (AUC ~ 0.80),尽管存在显著的异质性.
- 全球纵向菌株 (GLS) 显示中度准确度 (AUC ~ 0.72).
- 心脏生物标志物 (热波宁,NT-proBNP) 的准确度有变化,受时间和值的影响;初步的miRNA数据是有希望的,但缺乏标准化.
结论:
- 没有单一的诊断方式可靠地检测儿童癌症患者的早期心脏毒性.
- 人工智能 (AI) 模型和微RNA (miRNA) 显示出潜力,但需要进一步验证.
- 建议采用综合成像,生物标志物和临床数据的多式诊断策略,以有效监测,并紧急标准化定义和协议.
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