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机器学习分类器用于预测非小细胞肺癌中中枢淋巴结转移的机器学习分类器的独立验证,使用常规获得的FDG-PET/CT参数.

Agata Wdowiak1,2, Julian M M Rogasch1,3, Georg L Baumgärtner4

  • 1Department of Nuclear Medicine, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 13353 Berlin, Germany.

Current oncology (Toronto, Ont.)
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概括

机器学习 (ML) 分类器提高了非小细胞肺癌 (NSCLC) 患者的淋巴结分期精度. 这种经过验证的ML工具为检测晚期淋巴结转移提供了比标准PET/CT标准更高的特异性.

关键词:
在FDG-PET/CT中使用.在NSCLC的放射基因组学.美国中央情报局淋巴结阶段化 淋巴结阶段化机器学习是机器学习.非小细胞肺癌的肺癌.

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科学领域:

  • 在瘤学瘤学.
  • 放射学 放射学是指放射学
  • 医疗成像医学成像

背景情况:

  • 在非小细胞肺癌 (NSCLC) 中的FDG-PET/CT成像容易导致错误阳性淋巴结 (LN) 阶段.
  • 之前的研究表明,机器学习 (ML) 可以提高诊断准确度,而不是视觉评估.
  • 需要对ML分类器在NSCLC中进行LN分期的独立验证.

研究的目的:

  • 独立验证先前开发的ML分类器用于NSCLC的淋巴结分期.
  • 将ML分类器的诊断性能与标准PET/CT标准进行比较.
  • 在独立的患者队列中评估ML分类器的特异性和敏感性.

主要方法:

  • 使用常规[18F]FDG-PET/CT和临床数据的ML分类器应用于两个独立的NSCLC队列.
  • 第1组 (Charité) 包括87名患者;第2组 (TCIA) 包括124名患者.
  • 性能与标准标准进行了比较 (中的LN吸收>中和/或短轴>10毫米),基因组作为参考标准.

主要成果:

  • 与标准标准相比,ML分类器在TCIA队列中表现出显著更高的特异性 (90%与70%,p < 0.001).
  • 在Charité队列中,ML和标准标准之间的特异性相似 (65%对60%,p = 0.5).
  • 对于晚期淋巴结转移的敏感性 (pN2/3) 在两个队列中的ML和标准标准之间是可比的 (Charité: 97%,TCIA: 27-33%).

结论:

  • ML分类器的诊断性能,特别是其优异的特异性,在两个独立的NSCLC队列中成功验证.
  • 这种ML方法有望提高NSCLC中淋巴结分期的准确性.
  • 经过验证的ML分类器可以提高诊断准确度,超出FDG-PET/CT扫描的传统视觉评估.