儿童内分泌瘤中的人工智能和机器学习:机会,陷和可靠临床翻译的路线图
Michaela Kuhlen1,2, Fabio Hellmann3, Elisabeth Pfaehler4
1Department of Pediatrics, Pediatric Hematology/Oncology, Otto-von-Guericke-University, Leipziger Str. 44, D-39120 Magdeburg, Germany.
Biomedicines
|January 28, 2026
概括
人工智能 (AI) 和机器学习 (ML) 在儿科内分泌瘤中显示出早期的希望,特别是在早期检测和生存预测方面. 进一步验证和标准化方法对于安全和公平的临床采用至关重要.
科学领域:
- 在瘤学瘤学.
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 人工智能 (AI) 和机器学习 (ML) 越来越多地被用于癌症研究和临床实践.
- 儿科内分泌瘤罕见且多样化,对AI/ML应用提出了独特的挑战.
- 在儿科瘤学中现有的AI/ML证据是初步的,成像显示了最强大的结果.
研究的目的:
- 在儿科内分泌瘤中合成AI/ML应用,专注于诊断,风险分层和治疗反应.
- 为临床医生提供AI/ML方法的简单介绍和儿科内分泌瘤评估的检查清单.
- 为将AI/ML工具转化为儿科内分泌瘤实践提出路线图.
主要方法:
- 在特定的儿科内分泌瘤类型 (PGL,ACT,DTC/MTC,GEP-NEN) 中对AI/ML应用进行系统审查和综合.
- 开发一个临床医生基础和一个儿科内分泌瘤护检查清单.
- 一个利用现有欧洲基础设施的翻译路线图的概要.
主要成果:
- 初步证据表明,AI/ML可以模拟儿科分化甲状腺癌 (DTC) 的非缓解/复发,并预测儿科上腺皮层瘤 (ACT) 的存活率.
- 对超结瘤 (PGL) 和胃肠神经内分泌瘤 (GEP-NEN) 的AI/ML应用主要由成年人主导,为儿科使用提供了方法论基础.
- 关键的挑战包括小型数据集,领域转移,有限的外部验证和可解释性问题.
结论:
- 人工智能/ML有可能促进儿科内分泌瘤治疗,但目前的证据是初步的,需要严格的验证.
- 解决数据限制,确保可靠的验证和监测公平性对于安全有效的AI/ML采用至关重要.
- 需要一种协作,分阶段的方法来将AI/ML创新转化为常规的儿科内分泌瘤实践.
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