Eur Radiol:非小细胞肺癌患者前纵隔淋巴结到底有没有转移?

2021-01-05 shaosai MedSci原创

非小细胞肺癌患者前纵隔淋巴结到底有没有转移?

目的:本研究旨在利用18F-氟脱氧葡萄糖(FDG)正电子发射断层扫描/计算机断层扫描(PET / CT)建立最佳的机器学习(ML)模型,以评价非小细胞肺癌的转移性纵隔淋巴结(MedLNs),并将诊断结果与核医学医师的诊断结果进行比较。

方法:本研究共纳入了1329个MedLN。比较了提升决策树、逻辑回归、支持向量机、神经网络和决策森林模型。将最佳ML模型的诊断效能与医师的诊断效能进行了比较。ML方法分为仅具有定量变量的ML(MLq)和添加临床信息的ML(MLc)。我们根据MedLNs对 18F-FDG的摄取量进行了分析。

结果:与医生相比,提升决策树模型得到了更高的敏感性和阴性预测值,但特异性和阳性预测值却更低。 医师和MLq的准确性之间没有统计学差异(79.8% vs. 76.8%,p = 0.067)。 MLc的准确性显着高于医生(81.0%对76.8%,p = 0.009)。 在18F-FDG摄取低的MedLN中,ML的准确性明显高于医师(70.0% vs. 63.3%,p = 0.018)。

结论:尽管MLq和医师之间的准确性没有显着差异,但MLc的诊断效能优于MLq和医师。ML方法对评价低代谢的MedLN有一定帮助。因此,将临床信息添加到 18F-FDG PET / CT的定量变量中可以改善ML的诊断结果。

原始出处:

Jang Yoo,Miju Cheon,Yong Jin Park,et al. Machine learning-based diagnostic method of pre-therapeutic 18 F-FDG PET/CT for evaluating mediastinal lymph nodes in non-small cell lung cancer. PMID:33241521DOI:10.1007/s00330-020-07523-z

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    2021-01-07 sodoo
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