建立和验证一个方便和有效的查工具,以共同的参数为基础,在肺癌患者中发现活跃肺结核
Fan Zhang1,2,3, Fei Qi1,4, Mengyan Sun5
1General Department, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China.
Therapeutic advances in medical oncology
|July 29, 2025
概括
使用随机森林 (RF) 的新查模型可以帮助识别具有共存肺结核和肺癌 (PTB-LC) 的患者. 这种基于性别和血液标记等因素的工具有助于在肺癌患者中早期检测PTB-LC.
科学领域:
- 在瘤学瘤学.
- 传染性疾病 传染性疾病
- 机器学习在医学中的应用
背景情况:
- 同时存在的肺结核和肺癌 (PTB-LC) 是罕见的,并且经常被误诊.
- 对PTB-LC进行准确的查对于有效的治疗和资源分配至关重要.
研究的目的:
- 开发一种有效的工具,用于在肺癌患者中识别结核病 (TB) 感染的个体.
- 用常见的临床参数为实际的查模型.
主要方法:
- 追溯和前性患者队列被用于模型开发和验证.
- 机器学习方法包括后勤回归,随机森林,SVM,决策树和神经网络.
- 多变量分析确定了性别,多叶膜溢出,化,单细胞计数 (MONO) 和腺脱氨酶 (ADA) 作为关键预测因素.
主要成果:
- 随机森林 (RF) 模型表现出卓越的性能,在训练集中达到0.966的AUC,在前数据集中达到0.805.
- 射频模型在训练组中实现了高精度 (0.88) 和精度 (0.87).
- 为了评估高风险患者,开发了一种包含性别,肺溢液,化,MONO和血清ADA的诺莫图.
结论:
- 开发的射频查模型有效地识别了肺癌患者中PTB-LC高风险患者.
- 该模型利用现有的临床数据,为早期检测和管理PTB-LC提供了一个有希望的工具.
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