Department of Neuropathology, University Hospital Heidelberg, Heidelberg, Germany.
Clinical Cooperation Unit Neuropathology, German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany.
Nature. 2018 Mar 22;555(7697):469-474. doi: 10.1038/nature26000. Epub 2018 Mar 14.
Accurate pathological diagnosis is crucial for optimal management of patients with cancer. For the approximately 100 known tumour types of the central nervous system, standardization of the diagnostic process has been shown to be particularly challenging-with substantial inter-observer variability in the histopathological diagnosis of many tumour types. Here we present a comprehensive approach for the DNA methylation-based classification of central nervous system tumours across all entities and age groups, and demonstrate its application in a routine diagnostic setting. We show that the availability of this method may have a substantial impact on diagnostic precision compared to standard methods, resulting in a change of diagnosis in up to 12% of prospective cases. For broader accessibility, we have designed a free online classifier tool, the use of which does not require any additional onsite data processing. Our results provide a blueprint for the generation of machine-learning-based tumour classifiers across other cancer entities, with the potential to fundamentally transform tumour pathology.
准确的病理诊断对于癌症患者的最佳治疗至关重要。对于中枢神经系统中约 100 种已知的肿瘤类型,已经证明标准化的诊断过程特别具有挑战性——许多肿瘤类型的组织病理学诊断存在很大的观察者间差异。在这里,我们提出了一种基于 DNA 甲基化的中枢神经系统肿瘤分类的综合方法,并展示了其在常规诊断环境中的应用。我们表明,与标准方法相比,该方法的可用性可能会对诊断精度产生重大影响,导致多达 12%的前瞻性病例的诊断发生改变。为了更广泛的可及性,我们设计了一个免费的在线分类器工具,使用该工具不需要任何额外的现场数据处理。我们的研究结果为其他癌症实体的基于机器学习的肿瘤分类器的生成提供了蓝图,具有从根本上改变肿瘤病理学的潜力。