在人工智能驱动的上腺事件瘤诊所评估的患者中,轻度自主皮质醇分泌的预测模型
Mehmet Kostek1, Chandler McLeod1, Julia Kasmirski1
1Department of Surgery, University of Alabama at Birmingham, Birmingham, AL.
Journal of the American College of Surgeons
|February 5, 2026
概括
使用临床和放射学数据的预测模型可以帮助区分轻度自主皮质醇分泌 (MACS) 和非功能性上腺意外瘤 (NFAI). 这些模型为诊断上腺附带瘤而没有明显的库辛综合征提供了有前途的工具.
科学领域:
- 内分泌学 在内分泌学.
- 医疗成像医学成像
- 数据科学数据科学数据科学
背景情况:
- 轻度自主皮质醇分泌 (MACS) 影响20-50%的上腺瘤.
- 患有MACS的患者缺乏明显的库辛综合征症状.
- 区分MACS与非功能性上腺瘤 (NFAI) 是至关重要的,通常依赖于低剂量德克萨米他抑制试验.
研究的目的:
- 开发预测模型来区分MACS与NFAI.
- 利用临床和放射学参数进行准确的预测.
- 改进上腺意外瘤的诊断策略.
主要方法:
- 对397名被诊断为NFAI或MACS的患者进行了回顾性研究.
- 数据收集包括人口统计,病史和放射性特征.
- 使用LASSO回归和随机森林 (RF) 算法构建预测模型,训练/测试比例为 3:1.
主要成果:
- 对MACS最重要的预测因素是身体质量指数 (BMI),后部肥胖指数和抗高血压药物的数量.
- 拉索和射频模型的AUC分别为0.686和0.736.
- 射频模型显示了更高的精度 (72%),具有64.7%的灵敏度和75.8%的特异性.
结论:
- 综合临床和放射学数据的预测模型显示了将MACS与NFAI区分开来的潜力.
- 这些模型提供了一种非侵入性和高效的诊断方法.
- 进一步验证可能会提高它们在治疗上腺瘤的临床实用性.
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