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Updated: Sep 17, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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一种受监督的机器学习方法,用于预测在壮症患者手术后干预的需要.

Yuki Shinya^1,2, Abdul Karim Ghaith¹, Sukwoo Hong^1,2

¹1Department of Neurologic Surgery, Mayo Clinic, Rochester, Minnesota.

Neurosurgical focus

|July 1, 2025

概括

机器学习模型可以预测手术后生长激素 (GH) 分泌的垂体腺瘤患者的长期结果. 该工具有助于识别需要进一步治疗的患者,改善个性化护理.

关键词:

亚克罗梅加利症是什么长期的结果是长期的结果.机器学习是机器学习.结果预测结果预测.下垂体腺瘤是什么

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科学领域:

内分泌学在内分泌学.
神经外科神经外科
数据科学数据科学数据科学

背景情况:

产生生长激素 (GH) 的垂体腺瘤 (PAs) 导致显著的发病率和死亡率.
脑内膜透膜外科手术 (ETS) 是主要的治疗方法,但复发很常见.
预测ETS后的长期结果对于患者管理至关重要.

研究的目的:

开发和验证一种机器学习 (ML) 模型,用于预测GH分泌PA后ETS患者的长期结果.
在初级ETS之后,确定无干预率 (IFR) 的关键预测因素.

主要方法:

100名患有GH分泌PA的患者接受ETS治疗的回顾性队列研究.
收集的临床,放射和生化数据.
开发并评估使用AUROC和SHAP值预测IFR的监督ML模型 (决策树,随机森林).

主要成果:

在初级ETS之后,3年和5年无干预率 (IFR) 分别为70%和67%.
一个决策树模型实现了81%的准确性,突出了总体切除 (GTR) 和患者年龄作为关键预测因素.
SHAP分析确定了瘤大小<9毫米,GTR,年龄>65岁,Knosp等级0作为与更长的IFR相关的因素.

结论:

开发的ML模型提供了对可能经历ETS后复发或持久壮病的患者的细微预测.
这个模型可以帮助个性化治疗规划和后续策略.
建议对ML模型进行外部验证,以提高临床效用和资源配置.