使用PET/CT放射学在源不明的发烧中区分淋巴瘤与良性淋巴结疾病
Xinchao Zhang1,2, Fenglian Jing3, Yujing Hu2
1Hebei Medical University, Shijiazhuang, 050017, Hebei, China.
EJNMMI research
|November 13, 2024
概括
这项研究表明,PET/CT放射学可以有效地区分淋巴瘤和良性淋巴结病变在患有源不明发烧 (FUO) 的患者中. 随机森林 (RF) 模型实现了最高的准确性,提高了诊断能力.
科学领域:
- 医疗成像医学成像
- 无线电学 (Radiomics) 是一种辐射学.
- 机器学习在瘤学中
背景情况:
- 不知源发烧 (FUO) 与淋巴腺病变经常涉及淋巴瘤,感染或类风湿性疾病.
- 淋巴瘤是FUO与淋巴腺病变的普遍原因.
- 区分良性和恶性淋巴结病变是临床上具有挑战性的,即使使用像PET/CT这样的常规成像.
研究的目的:
- 在FUO患者中利用PET/CT放射学来区分淋巴瘤与良性淋巴结病变.
- 为了提高FUO与淋巴腺病变病例的诊断准确度.
主要方法:
- 收集了204名患者 (114名淋巴瘤,90名良性) 的数据,他们接受了18F-FDG PET/CT用于FUO.
- 应用了最少绝对收缩和选择操作员 (LASSO) 算法来提取15个有效的放射性特征.
- 开发和评估了后勤回归 (LR),支持向量机 (SVM),随机森林 (RF) 和 k-最近邻居 (KNN) 机器学习模型.
主要成果:
- 随机森林 (RF) 模型在训练组中获得了最高的AUC (0.998),并在测试组中表现出强的表现 (0.915).
- 射频模型在统计学上显示出明显的优异性能,而不是训练组中的其他模型.
- 决策曲线分析 (DCA) 表明RF模型在训练和测试数据集中的性能优越.
结论:
- 在FUO中,PET/CT放射学显示出区分淋巴瘤与良性淋巴结病变的显著潜力.
- 随机森林 (RF) 模型是这种歧视的高效工具,提供了更好的诊断准确性.
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