Automated quantification of aligned collagen for human breast carcinoma prognosis.
Automated quantification of aligned collagen for human breast carcinoma prognosis.
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DOI：
10.4103/2153-3539.139707
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发表时间：
2014
影响因子：
--
通讯作者：
Eliceiri KW
中科院分区：
文献类型：
--
作者：
Bredfeldt JS;Liu Y;Conklin MW;Keely PJ;Mackie TR;Eliceiri KW
癌症患者的死亡率直接归因于癌细胞转移到远离原发肿瘤的部位的能力。肿瘤细胞的这种迁移始于局部肿瘤微环境的重塑，包括细胞外基质的变化和基质细胞的募集，这两者都促进肿瘤细胞侵入血流。在乳腺癌中，已经提出肿瘤上皮细胞周围的胶原纤维的排列可以作为浸润性导管癌患者存活的基于图像的定量生物标志物。特定类型的胶原蛋白排列已被确定其预后价值，现在这些肿瘤相关的胶原蛋白签名（TACS）是几个临床标本成像试验的核心。在这里，我们实现了这种TACS候选生物标志物的半自动采集和分析，并展示了一种协议，该协议将允许在整个大型患者队列中进行一致的评分。使用大视场高分辨率显微镜技术，图像处理和监督学习方法，我们能够量化和评分功能的胶原纤维排列相对于相邻的肿瘤间质边界。我们的半自动化技术产生的分数与由三名人类观察员组成的小组产生的分数具有统计学显著相关性。此外，我们的系统生成的分类评分准确预测了196名乳腺癌患者的生存率。特征等级分析揭示，TACS阳性纤维彼此更好地对齐，通常具有较低的密度，并且以较大的相互作用角度终止于上皮细胞组内或附近。这些结果证明了监督学习协议用于简化胶原蛋白相对于肿瘤间质边界的对齐分析的实用性。
Mortality in cancer patients is directly attributable to the ability of cancer cells to metastasize to distant sites from the primary tumor. This migration of tumor cells begins with a remodeling of the local tumor microenvironment, including changes to the extracellular matrix and the recruitment of stromal cells, both of which facilitate invasion of tumor cells into the bloodstream. In breast cancer, it has been proposed that the alignment of collagen fibers surrounding tumor epithelial cells can serve as a quantitative image-based biomarker for survival of invasive ductal carcinoma patients. Specific types of collagen alignment have been identified for their prognostic value and now these tumor associated collagen signatures (TACS) are central to several clinical specimen imaging trials. Here, we implement the semi-automated acquisition and analysis of this TACS candidate biomarker and demonstrate a protocol that will allow consistent scoring to be performed throughout large patient cohorts. Using large field of view high resolution microscopy techniques, image processing and supervised learning methods, we are able to quantify and score features of collagen fiber alignment with respect to adjacent tumor-stromal boundaries. Our semi-automated technique produced scores that have statistically significant correlation with scores generated by a panel of three human observers. In addition, our system generated classification scores that accurately predicted survival in a cohort of 196 breast cancer patients. Feature rank analysis reveals that TACS positive fibers are more well-aligned with each other, are of generally lower density, and terminate within or near groups of epithelial cells at larger angles of interaction. These results demonstrate the utility of a supervised learning protocol for streamlining the analysis of collagen alignment with respect to tumor stromal boundaries.