Großerueschkamp Frederik, Kallenbach-Thieltges Angela, Behrens Thomas, Brüning Thomas, Altmayer Matthias, Stamatis Georgios, Theegarten Dirk, Gerwert Klaus
Protein Research Unit Ruhr within Europe (PURE), Department of Biophysics, Ruhr University Bochum, Germany.
Analyst. 2015 Apr 7;140(7):2114-20. doi: 10.1039/c4an01978d.
By integration of FTIR imaging and a novel trained random forest classifier, lung tumour classes and subtypes of adenocarcinoma are identified in fresh-frozen tissue slides automated and marker-free. The tissue slices are collected under standard operation procedures within our consortium and characterized by current gold standards in histopathology. In addition, meta data of the patients are taken. The improved standards on sample collection and characterization results in higher accuracy and reproducibility as compared to former studies and allows here for the first time the identification of adenocarcinoma subtypes by this approach. The differentiation of subtypes is especially important for prognosis and therapeutic decision.
通过傅里叶变换红外光谱成像（FTIR）与一种新训练的随机森林分类器相结合，可在新鲜冷冻组织切片中自动且无标记地识别肺肿瘤类别和腺癌亚型。组织切片是在我们联盟的标准操作程序下收集的，并通过组织病理学的当前金标准进行表征。此外，还获取了患者的元数据。与以前的研究相比，样本采集和表征方面改进的标准提高了准确性和可重复性，并首次允许通过这种方法识别腺癌亚型。亚型的区分对于预后和治疗决策尤为重要。
