Persistent Homology for the Quantitative Evaluation of Architectural Features in Prostate Cancer Histology
Persistent Homology for the Quantitative Evaluation of Architectural Features in Prostate Cancer Histology
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DOI：
10.1038/s41598-018-36798-y
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发表时间：
2019-02-04
影响因子：
4.6
通讯作者：
Wenk, Carola
中科院分区：
文献类型：
--
作者：
Lawson, Peter;Sholl, Andrew B.;Wenk, Carola
目前用于评估前列腺癌架构的系统是格里森分级系统，该系统将癌症的形态划分为五种不同的架构模式，根据癌症侵袭性的增加程度标记为1到5，并通过将两种最主要的模式的标签相加来生成分数。Gleason评分目前是预测患者预后最有效的指标；然而，由于病理学家之间观察者内和观察者间的高度可变性，Gleason评分在重复性和一致性方面存在问题。此外，格里森系统缺乏解决格里森模式之外的潜在预测架构特性的粒度。我们使用应用于前列腺癌腺体结构的拓扑数据分析技术来评估前列腺癌的结构亚型。在这项工作中，我们演示了如何使用持久同调来捕获独立于Gleason模式的体系结构特性。具体地说，使用持久同调，我们计算了Gleason模式3、4和5的纯分级前列腺癌组织病理学图像的拓扑表示，并表明持久同调能够通过分级的持久向量将前列腺癌组织聚类为架构组。我们的结果表明，持久同源性能够将前列腺癌组织病理图像聚集成独特的组，其主导结构模式与格里森模式的连续体一致。此外，值得特别关注的是，持久同源性对识别单个Gleason模式中特定的亚结构群的敏感性，表明持久同源性可能代表着一种稳健的前列腺癌结构的量化方法，其粒度高于现有的半定量方法。这些拓扑表示按体系结构分离前列腺癌的能力使它们成为未来机器学习方法的理想输入，目的是用拓扑特征来增强传统方法，以改善诊断和预后。
The current system for evaluating prostate cancer architecture is the Gleason grading system which divides the morphology of cancer into five distinct architectural patterns, labeled 1 to 5 in increasing levels of cancer aggressiveness, and generates a score by summing the labels of the two most dominant patterns. The Gleason score is currently the most powerful prognostic predictor of patient outcomes; however, it suffers from problems in reproducibility and consistency due to the high intra-observer and inter-observer variability amongst pathologists. In addition, the Gleason system lacks the granularity to address potentially prognostic architectural features beyond Gleason patterns. We evaluate prostate cancer for architectural subtypes using techniques from topological data analysis applied to prostate cancer glandular architecture. In this work we demonstrate the use of persistent homology to capture architectural features independently of Gleason patterns. Specifically, using persistent homology, we compute topological representations of purely graded prostate cancer histopathology images of Gleason patterns 3,4 and 5, and show that persistent homology is capable of clustering prostate cancer histology into architectural groups through a ranked persistence vector. Our results indicate the ability of persistent homology to cluster prostate cancer histopathology images into unique groups with dominant architectural patterns consistent with the continuum of Gleason patterns. In addition, of particular interest, is the sensitivity of persistent homology to identify specific sub-architectural groups within single Gleason patterns, suggesting that persistent homology could represent a robust quantification method for prostate cancer architecture with higher granularity than the existing semiquantitative measures. The capability of these topological representations to segregate prostate cancer by architecture makes them an ideal candidate for use as inputs to future machine learning approaches with the intent of augmenting traditional approaches with topological features for improved diagnosis and prognosis.