Biophotonics Research Unit, Gloucestershire Hospitals NHS Foundation Trust, Great Western Road, Gloucester, GL1 3NN, UK.
Analyst. 2014 Jan 21;139(2):381-8. doi: 10.1039/c3an01163a.
The application of semi-supervised methodology to improve the classification performance of a Raman spectroscopic probe for the diagnosis of oesophageal cancer is described. It is well known that gold standard histopathology diagnosis can be highly subjective, particularly for diseases which have several stages, such as cancer. A 'consensus' pathology decision can be obtained to ensure a robust gold standard by obtaining a diagnosis from several experts and samples are then only included in standard classification models if they have been assigned the same pathology by all experts. This can result in a significant number of samples that are excluded from the analysis as no consensus was reached. In this work semi-supervised methodology was used to extend Principal Component Analysis followed by Linear Discriminant Analysis (PCA-LDA) to incorporate samples without consensus pathology when discriminating between benign and oesophageal cancer specimens measured using a Raman endoscopic probe ex vivo. We demonstrate that a fully semi-supervised approach improved sensitivity and specificity from 73% and 78% (PCA-LDA) to 78% and 84% (semi-supervised) for discriminating between intestinal metaplasia and dysplasia and from 44% and 66% (PCA-LDA) to 63% and 72% (semi-supervised) when discriminating between intestinal metaplasia and low grade dysplasia.
本文介绍了一种将半监督方法应用于改善用于诊断食管癌的拉曼光谱探针的分类性能的方法。众所周知，金标准组织病理学诊断可能具有高度主观性，特别是对于具有多个阶段的疾病，如癌症。通过从几位专家那里获得诊断，可以获得“共识”病理学决策，以确保稳健的金标准，然后只有当所有专家都对同一样本做出相同的病理诊断时，才将样本纳入标准分类模型。这可能导致大量样本被排除在分析之外，因为没有达成共识。在这项工作中，我们使用半监督方法扩展了主成分分析（PCA）和线性判别分析（LDA），以便在使用拉曼内窥镜探针离体测量良性和食管癌样本时，将没有共识病理学的样本纳入区分。我们证明，对于区分肠上皮化生和异型增生，完全半监督方法将灵敏度和特异性从 PCA-LDA 的 73%和 78%提高到 78%和 84%，对于区分肠上皮化生和低级别异型增生，将灵敏度和特异性从 PCA-LDA 的 44%和 66%提高到 63%和 72%。
