组织阵列图像的统计方法-算法评分和协同训练
Statistical methods for tissue array images - algorithmic scoring and co-training
摘要：
组织微阵列技术的最新进展使免疫组织化学成为一种强大的中高通量分析工具，特别是用于诊断和预后生物标志物的验证。然而，随着研究规模的增长，这些检测的手动评估成为一个令人望而却步的限制;它大大降低了吞吐量，大大增加了可变性和费用。我们提出了一种算法-组织阵列共现矩阵分析（TACOMA）-用于量化的基础上总结的局部像素间关系的纹理规律的细胞表型。该算法可以很容易地训练任何染色模式，是缺乏敏感的调整参数，并有能力报告显着像素的图像，有助于其得分。病理学家通过信息训练补丁的输入是算法的一个重要方面，它允许训练任何特定的标志物或细胞类型。通过协同训练，对于非常小的训练样本（例如，尺寸30）。当特征之间存在“足够”冗余时，我们通过在高维环境中细化特征集来成功地进行协同训练。TACOMA是灵活、透明的，并提供了一个可以清晰和自信地进行评估的评分过程。在一项基于雌激素受体（ER）标记物的研究中，我们表明TACOMA在准确性和可重复性方面与病理学家的表现相当或优于病理学家的表现。
英文摘要：
Recent advances in tissue microarray technology have allowed immunohistochemistry to become a powerful medium-to-high throughput analysis tool, particularly for the validation of diagnostic and prognostic biomarkers. However, as study size grows, the manual evaluation of these assays becomes a prohibitive limitation; it vastly reduces throughput and greatly increases variability and expense. We propose an algorithm - Tissue Array Co-Occurrence Matrix Analysis (TACOMA) - for quantifying cellular phenotypes based on textural regularity summarized by local inter-pixel relationships. The algorithm can be easily trained for any staining pattern, is absent of sensitive tuning parameters and has the ability to report salient pixels in an image that contribute to its score. Pathologists' input via informative training patches is an important aspect of the algorithm that allows the training for any specific marker or cell type. With co-training, the error rate of TACOMA can be reduced substantially for a very small training sample (e.g., with size 30). We give theoretical insights into the success of co-training via thinning of the feature set in a high-dimensional setting when there is "sufficient" redundancy among the features. TACOMA is flexible, transparent and provides a scoring process that can be evaluated with clarity and confidence. In a study based on an estrogen receptor (ER) marker, we show that TACOMA is comparable to, or outperforms, pathologists' performance in terms of accuracy and repeatability.
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