使用临床,荷尔蒙和身体组成数据对上腺瘤进行基于机器学习的分类
Seung Shin Park1,2, Jongsung Noh3, Jinhee Kim4
1Department of Internal Medicine, Seoul National University College of Medicine, Seoul 03080, Korea.
European journal of endocrinology
|July 29, 2025
概括
一个新的机器学习模型准确地诊断上腺瘤,包括轻度自主皮质醇分泌 (MACS) 和上腺库辛综合征 (ACS). 这种人工智能方法整合了临床,荷尔蒙和身体成分数据,以提高诊断准确度.
科学领域:
- 内分泌学 在内分泌学.
- 在瘤学瘤学.
- 人工智能的人工智能
背景情况:
- 准确诊断各种上腺瘤是一项挑战.
- 亚型包括轻度自主皮质醇分泌 (MACS),上腺库辛综合征 (ACS),原发性阿尔多斯特隆症 (PA),色细胞瘤 (PCC) 和非功能上腺腺瘤 (NFAs).
- 目前的诊断方法可能是复杂的和侵入性的.
研究的目的:
- 为上腺瘤开发基于机器学习 (ML) 的单步诊断方法.
- 整合临床数据,血清上腺激素概况 (SAP) 和身体成分数据,以改善差异化.
- 提高诊断准确度,减少对侵入性手术的需要.
主要方法:
- 开发了ML模型,使用了641名上腺瘤患者的数据.
- 整合了32个临床数据点,49个SAP标志物和15个身体成分数据点.
- 随机将患者分为训练 (4:1比) 和测试队列.
主要成果:
- 最好的ML模型实现了0.78的平衡精度和0.89的AUC,用于区分所有五种瘤类型.
- 在区分特定瘤与非功能性上腺腺瘤 (NFAs) 时,观察到高精度,AUC高达0.99.
- 血清上腺激素 (SAP) 概况是最关键的特征;身体组成数据的贡献最小.
结论:
- 该ML模型证明了各种上腺瘤亚型的高诊断准确性.
- 结合临床,身体组成和SAP数据,有助于区分上腺瘤.
- 这种人工智能驱动的方法可能会改善临床决策并减少侵入性测试.
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