我们可以使用机器学习算法在3个月内预测在玛刀放射手术后,来自非小细胞肺癌的大脑转移的整体存活率吗?
Hyeong Cheol Moon1, Byung Jun Min2, Young Seok Park1,3
1Department of Neurosurgery, Gamma Knife Icon Center, Chungbuk National University Hospital, Cheongju, Republic of Korea.
Medicine
|February 2, 2024
概括
机器学习准确地预测了玛刀放射手术 (GRKS) 后非小细胞肺癌患者的3个月生存期. 老年和巨大的瘤体积表明死亡风险较高,可能避免使用GRKS过度治疗.
科学领域:
- 在瘤学瘤学.
- 医学物理 医学物理
- 人工智能的人工智能
背景情况:
- 玛刀放射性手术 (GRKS) 是大脑转移的常见治疗方法.
- 预测GRKS后3个月内非小细胞肺癌 (NSCLC) 患者的整体存活率 (OS) 仍然具有挑战性.
- 显著比例的NSCLC患者在GRKS后8周内发生死亡,这表明潜在的过度治疗.
研究的目的:
- 开发机器学习 (ML) 模型,用于预测经过GRKS的NSCLC患者的3个月的生存期.
- 确定影响NSCLC患者在GRKS后生存的关键预后特征.
主要方法:
- 分析了接受GRKS的120名NSCLC患者.
- 数据被分为训练 (n=80) 和测试 (n=40) 组,共14个特征.
- 使用三个ML算法 (决策树,随机森林,增强树) 来预测3个月的OS.
主要成果:
- 决策树算法实现了最高的预测准确率,为77.5%.
- 确定的主要预测因素包括年龄,化疗状态和预治疗.
- 瘤体积 (>10厘米) 和年龄 (>71岁) 是3个月死亡率的关键因素.
结论:
- ML算法可以有效地预测GRKS后NSCLC患者的3个月的OS.
- 识别高风险患者 (年龄较大,瘤体积较大) 可以帮助个性化治疗决策.
- GRKS可能不适合预后不佳,年龄较大,瘤体积较大的NSCLC患者.
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