论文标题
使用持续的同源拓扑特征来表征医学图像：肺和脑癌的案例研究
Using Persistent Homology Topological Features to Characterize Medical Images: Case Studies on Lung and Brain Cancers
论文作者
论文摘要
肿瘤形状是影响肿瘤生长和转移的关键因素。本文提出了由持续同源性计算出的拓扑特征，以表征来自数字病理学和放射学图像的肿瘤进展，并检查其对事件时间数据的影响。所提出的拓扑特征是扩展比例扩展转换的不变性，并且可以总结各种肿瘤形状模式。拓扑特征在功能空间中表示，并用作功能性COX比例危害模型中的功能预测变量。提出的模型可以对拓扑形状特征与生存风险之间的关联进行可解释的推论。使用连续的133个肺癌和77名脑肿瘤患者进行了两项案例研究。两项研究的结果都表明，拓扑特征可以预测调整临床变量后的生存预后，并且预测的高风险基团的生存结果比低风险基团更差。同样，发现与生存危害正相关的拓扑形状特征是不规则和异质形状模式，已知与肿瘤进展有关。
Tumor shape is a key factor that affects tumor growth and metastasis. This paper proposes a topological feature computed by persistent homology to characterize tumor progression from digital pathology and radiology images and examines its effect on the time-to-event data. The proposed topological features are invariant to scale-preserving transformation and can summarize various tumor shape patterns. The topological features are represented in functional space and used as functional predictors in a functional Cox proportional hazards model. The proposed model enables interpretable inference about the association between topological shape features and survival risks. Two case studies are conducted using consecutive 133 lung cancer and 77 brain tumor patients. The results of both studies show that the topological features predict survival prognosis after adjusting clinical variables, and the predicted high-risk groups have worse survival outcomes than the low-risk groups. Also, the topological shape features found to be positively associated with survival hazards are irregular and heterogeneous shape patterns, which are known to be related to tumor progression.
