在患有肺恶性瘤的患者中,微波切除后肺胸部或肺流的风险预测模型
Zihang Wang1,2, Yufan Liu1,2, Xiaowen Cao1,2
1Department of Thoracic Surgery, Affiliated Hospital of Nantong University, Nantong, China.
Heliyon
|October 21, 2024
概括
机器学习模型可以预测肺部恶性瘤的微波切除 (MWA) 后的肺胸和肺. 这有助于为接受MWA治疗的患者及时进行干预.
科学领域:
- 在瘤学瘤学.
- 医疗成像医学成像
- 数据科学数据科学数据科学
背景情况:
- 微波除 (MWA) 对肺癌 (LM) 有效.
- 预测MWA后的肺胸部和胸腔溢出仍然具有挑战性.
- 及时干预对于管理这些并发症至关重要.
研究的目的:
- 开发和评估机器学习 (ML) 模型,用于预测LM患者在MWA后的肺胸部和胸腔溢出.
- 确定这些术后并发症的关键预测因素.
主要方法:
- 对398名接受MWA治疗的LM患者 (2013年1月 - 2023年9月) 的回顾性研究.
- 数据分为培训 (n=340) 和测试 (n=58) 集.
- 六个ML算法用于构建四个预测模型;使用ROC分析和AUC进行评估.
主要成果:
- 肺胸发生在23.37%的患者中,肺发生在33.42%的患者中.
- 逻辑回归 (LR) 在测试组中显示了最佳的预测性能.
- 在不同模型中,AUC值从0.727到0.895不等.
结论:
- ML模型在预测MWA后肺胸部和多流量方面表现出有效性.
- 这些模型可以帮助临床医生积极主动地管理患者.
- 进一步验证ML模型的临床应用是有必要的.
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