机器学习用于区分肺状细胞癌与腺癌,使用临床代谢特征和18F-FDG PET/CT放射学
Yalin Zhang1,2, Huiling Liu3, Cheng Chang4
1Department of Radiation Oncology, The Third Affillated Teaching Hospital of Xinjiang Medical University, Affilated Cancer Hospital, Urumuqi, China.
PloS one
|April 3, 2024
概括
一种新的放射学模型可以非侵入性地区分肺癌亚型,帮助那些无法接受侵入性手术的患者. 组合模型在分类状细胞癌和腺癌方面表现优越.
科学领域:
- 医疗成像医学成像
- 在瘤学瘤学.
- 机器学习 机器学习
背景情况:
- 非小细胞肺癌 (NSCLC) 的诊断通常需要侵入性手术.
- 不侵入性地区分状细胞癌 (SCC) 和腺癌 (ADC) 亚型具有临床意义.
研究的目的:
- 评估基于PET/CT的放射学模型,用于NSCLC亚型的非侵入性分化 (SCC与ADC).
- 为了比较四种机器学习技术的预测性能,用于亚型分类.
主要方法:
- 对255名NSCLC患者的回顾性分析,分为培训和验证组.
- 放射学特征采用LASSO方法进行提取和选择.
- 模型开发使用四种机器学习技术,包括后勤回归和使用AUC,精度,灵敏度和特异性的性能评估.
主要成果:
- 后勤回归分类器在验证队列中表现出卓越的预测能力.
- 组合模型实现了最高的预测功率 (AUC 0.870),超过了临床模型 (AUC 0.848) 和放射学模型 (AUC 0.774).
结论:
- 结合PET/CT放射学和临床模型,利用后勤回归,有效地分类NSCLC组织学亚型.
- 这种非侵入性方法为不适合侵入性诊断程序的患者提供了一个有希望的替代方案.
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