Statistical analysis of a lung cancer spectral histopathology (SHP) data set.
Statistical analysis of a lung cancer spectral histopathology (SHP) data set.
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肺癌光谱组织病理学 (SHP) 数据集的统计分析。
DOI：
10.1039/c4an01832j
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
Diem,Max
中科院分区：
文献类型：
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作者：
Mu,Xinying;Kon,Mark;Ergin,Ayşegül;Remiszewski,Stan;Akalin,Ali;Thompson,ClayM;Diem,Max
我们报告的红外光谱数据集，包括374例患者的388个肺活检的统计分析结果。将经典结果与光谱结果相关联并分析所得数据的方法在过去被称为光谱组织病理学（SHP）。在这里，我们表明，标准的生物统计程序，如严格分离的训练和盲法测试集，结果在一个平衡的准确性优于95%的正常，坏死和癌组织的区别，并优于90%的小细胞，鳞状细胞和腺癌的分类平衡的准确性。初步结果表明，一旦收集到足够大的数据集，腺癌的进一步亚分类应该是可行的，具有类似的准确性。
We report results on a statistical analysis of an infrared spectral dataset comprising a total of 388 lung biopsies from 374 patients. The method of correlating classical and spectral results and analyzing the resulting data has been referred to as spectral histopathology (SHP) in the past. Here, we show that standard bio-statistical procedures, such as strict separation of training and blinded test sets, result in a balanced accuracy of better than 95% for the distinction of normal, necrotic and cancerous tissues, and better than 90% balanced accuracy for the classification of small cell, squamous cell and adenocarcinomas. Preliminary results indicate that further sub-classification of adenocarcinomas should be feasible with similar accuracy once sufficiently large datasets have been collected.