在儿童癌症中延迟化疗诱导的恶心和吐的基于机器学习的预测模型:一项前性队列研究
Jun Deng1, Hongyu Lou2, Longzhen Liu1
1State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China.
Pediatric blood & cancer
|January 10, 2026
概括
一个新的人工神经网络 (ANN) 模型准确地预测了儿童延迟化疗诱导的恶心和吐 (CINV) 风险. 该工具有助于临床医生为儿科瘤患者提供有针对性的CINV管理.
科学领域:
- 在瘤学瘤学.
- 机器学习 机器学习
- 儿科医学 儿科医学
背景情况:
- 延迟化疗诱导的恶心和吐 (CINV) 在儿科瘤学中未得到充分承认.
- 预测CINV风险对于有效的患者管理至关重要.
研究的目的:
- 开发和验证用于预测儿科患者延迟CINV风险的机器学习模型.
- 创建一个用于临床应用的视觉工具.
主要方法:
- 这是一项前性队列研究,涉及399名儿科瘤患者.
- 开发和验证使用29个特征的7个机器学习模型.
- 使用曲线下面面积 (AUC) 和SHAP进行性能评估,以提供可解释性.
主要成果:
- 人工神经网络 (ANN) 模型实现了最高的AUC,为0.815.
- 关键预测因素包括急性CINV,化疗持续时间,年龄和CINV预期.
- 为可视化CINV风险而开发了一个基于Web的计算器.
结论:
- 该ANN模型有效地识别了患有延迟CINV的高风险儿童.
- 基于网络的计算器支持针对儿科瘤患者的针对性CINV管理策略.
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