个性化玛刀放射手术对垂体腺瘤的处方剂量:推进神经网络模型的开发和内部验证
Jheremy S Reyes1, Vidhan K Dhol1, Alexandros Bouras1
1Center for Image-Guided Neurosurgery, Department of Neurological Surgery, University of Pittsburgh Medical Center (UPMC), Pittsburgh, PA, USA.
Pituitary
|February 9, 2026
概括
这项研究开发了一种神经网络,用于预测海马刀放射性手术 (GKRS) 对垂体腺瘤的剂量,显示年龄和瘤特征是关键预测因素. 该模型提供了一种数据驱动的方法来规范GKRS治疗计划.
科学领域:
- 神经外科 神经外科
- 辐射瘤学 辐射瘤学
- 医疗成像医学成像
背景情况:
- 玛刀放射性手术 (GKRS) 是对垂体腺瘤的标准治疗方法.
- 目前的GKRS剂量选择严重依赖于临床医生的经验.
- 数据驱动的模型可以使用临床和成像数据来标准化剂量选择.
研究的目的:
- 开发和验证一个前神经网络 (FNN) 来预测用于垂体腺瘤的GKRS处方剂量.
- 使用常规可用的临床和成像功能来预测剂量.
- 建立基于数据的剂量推工具的基础.
主要方法:
- 用GKRS治疗的102个垂体腺瘤的回顾性分析.
- 输入特征包括患者的人口统计,瘤特征和治疗史.
- 使用MAE,RMSE和R2.2训练和评估了一种两层隐藏的FNN.
- 变换的重要性评估预测因素贡献.
主要成果:
- 在试验组中,FNN实现了可接受的预测准确性 (MAE: 1.61 Gy,RMSE: 2.31 Gy,R2: 0.51).
- 治疗时的年龄是最重要的预测因素,其次是功能状态,瘤体积和洞穴鼻入侵.
- 人口和活检相关因素对剂量预测的影响最小.
结论:
- 一个概念验证FNN可以生成针对垂体腺瘤的个性化GKRS剂量估计.
- 该模型展示了GKRS剂量建议的数据驱动工具的可行性.
- 建议在较大的队列中进行外部验证,以确认发现.
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