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Validation of a Quantifier-Based Fuzzy Classification System for Breast Cancer Patients on External Independent Cohorts

机译：基于乳腺癌患者的乳腺癌患者对乳腺癌患者的验证

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Recent studies in breast cancer domains have identified seven distinct clinical phenotypes (groups) using immunohistochemical analysis and a variety of unsupervised learning techniques. Consensus among the clustering algorithms has been used to categorise patients into these specific groups, but often at the expenses of not classifying all patients. It is known that fuzzy methodologies can provide linguistic based classification rules to ease those from consensus clustering. The objective of this study is to present the validation of a recently developed extension of a fuzzy quantification subsethood-based algorithm on three sets of newly available breast cancer data. Results show that our algorithm is able to reproduce the seven biological classes previously identified, preserving their characterisation in terms of marker distributions and therefore their clinical meaning. Moreover, because our algorithm constitutes the fundamental basis of the newly developed Nottingham Prognostic Index Plus (NPI+), our findings demonstrate that this new medical decision making tool can help moving towards a more tailored care in breast cancer.
机译：最近在乳腺癌结构域的研究已经确定了七种不同的临床表型（组）使用免疫组化分析和各种无监督的学习技术。聚类算法中的共识已被用于将患者分类为这些特定群体，但通常在不对所有患者进行分类的费用。众所周知，模糊方法可以提供基于语言的分类规则，以简化共识聚类的分类规则。本研究的目的是在三组新可用的乳腺癌数据上展示最近开发的基于模糊量化替代算法的验证。结果表明，我们的算法能够再现先前识别的七种生物类，在标记分布方面保持其特征，因此它们的临床意义。此外，由于我们的算法构成了新开发的诺丁汉预测指数加（NPI +）的基础，所以我们的研究结果表明，这一新的医学决策工具可以帮助在乳腺癌中更定制的护理。

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