Case Western Reserve University School of Medicine, Cleveland, Ohio, USA.
University of Colorado Boulder, Department of Applied Mathematics, Boulder, Colorado, USA.
Med Phys. 2021 Nov;48(11):7043-7051. doi: 10.1002/mp.15255. Epub 2021 Oct 11.
Radiomics, the objective study of nonvisual features in clinical imaging, has been useful in informing decisions in clinical oncology. However, radiomics currently lacks the ability to characterize the overall topological structure of the data. This niche can be filled by persistent homology, a form of topological data analysis that analyzes high-level structure. We hypothesized that persistent homology features quantified using cubical complexes could be extracted from lung tumor scans and related to survival.
We obtained segmented computed tomography (CT) lung scans (n = 565) from the NSCLC-Radiomics and NSCLC-Radiogenomics datasets in The Cancer Imaging Archive. These scans are three-dimensional images whose pixel intensity corresponds to a number of Hounsfield units. Cubical complexes are a topological image analysis method that effectively analyzes the number of topological features in an image as the image is thresholded at different intensities. We calculated a novel output called a feature curve by plotting the number of zero-dimensional (0D) topological features counted from the cubical complex filtration against each Hounsfield value. This curve's first moment of distribution was utilized as a summary statistic to show association with survival in a Cox proportional hazards model. We hypothesized that persistent homology features quantified using cubical complexes could be extracted from lung tumor scans and related to survival.
After controlling for tumor image size, age, and stage, the first moment of the 0D topological feature curve was associated with poorer survival (HR = 1.118; 95% CI = 1.026-1.218; p = 0.01). The patients in our study with the lowest first moment scores had significantly better survival (1238 days; 95% CI = 936-1599) compared to the patients with the highest first moment scores (429 days; 95% CI = 326-601; p = 0.0015).
We have shown that persistent homology can generate useful clinical correlates from tumor CT scans. Our 0D topological feature curve statistic predicts survival in lung cancer patients. This novel statistic may be used in tandem with standard radiomics variables to better inform clinical oncology decisions.
放射组学是对临床影像中非视觉特征的客观研究，它在临床肿瘤学决策中已得到广泛应用。然而，放射组学目前缺乏对数据整体拓扑结构进行特征描述的能力。这个空缺可以由持久性同调来填补，持久性同调是一种拓扑数据分析形式，可以分析高级结构。我们假设可以从肺部肿瘤扫描中提取使用立体复合物量化的持久性同调特征，并将其与生存相关联。
我们从癌症影像学档案中的 NSCLC-Radiomics 和 NSCLC-Radiogenomics 数据集获得了分割的计算机断层扫描（CT）肺部扫描（n = 565）。这些扫描是三维图像，其像素强度对应于一定数量的亨氏单位。立体复合物是一种拓扑图像分析方法，可以有效地分析图像中拓扑特征的数量，因为图像会在不同的强度下进行阈值处理。我们通过绘制从立体复合物过滤中计算出的零维（0D）拓扑特征数量与每个亨氏值的关系来计算一个新的输出，称为特征曲线。该曲线分布的第一矩被用作生存的汇总统计量，以在 Cox 比例风险模型中显示与生存的关联。我们假设可以从肺部肿瘤扫描中提取使用立体复合物量化的持久性同调特征，并将其与生存相关联。
在控制肿瘤图像大小、年龄和阶段后，0D 拓扑特征曲线的第一矩与较差的生存相关（HR = 1.118；95%CI = 1.026-1.218；p = 0.01）。与第一矩得分最高的患者相比，研究中第一矩得分最低的患者的生存显著改善（1238 天；95%CI = 936-1599），而第一矩得分最高的患者的生存时间为 429 天（95%CI = 326-601；p = 0.0015）。
我们已经证明，持久性同调可以从肿瘤 CT 扫描中生成有用的临床相关性。我们的 0D 拓扑特征曲线统计量预测了肺癌患者的生存。这种新的统计量可以与标准的放射组学变量一起使用，以更好地为临床肿瘤学决策提供信息。
