论文标题
发现临床上有意义的形状特征以分析肿瘤病理图像
Discovering Clinically Meaningful Shape Features for the Analysis of Tumor Pathology Images
论文作者
论文摘要
借助先进的成像技术，肿瘤组织幻灯片的数字病理成像正在成为癌症诊断的常规临床程序。该过程产生大量的成像数据，以高分辨率捕获组织学细节。深度学习方法的最新发展使我们能够自动检测和表征大规模病理图像中的肿瘤区域。从每个确定的肿瘤区域中，我们提取了30个定义明确的描述符，以量化其形状，几何形状和拓扑结构。我们证明了来自国家肺筛查试验的肺腺癌患者中这些描述符特征与患者生存结果如何相关（n = 143）。此外，从癌症基因组图书馆计划（n = 318）的独立患者队列中开发并验证了基于描述符的预后模型。这项研究提出了有关肿瘤形状，几何和拓扑特征和患者预后之间关系的新见解。我们在GitHub上以R代码的形式提供软件：https：//github.com/estfernandez/slide_image_segmentation_and_extraction。
With the advanced imaging technology, digital pathology imaging of tumor tissue slides is becoming a routine clinical procedure for cancer diagnosis. This process produces massive imaging data that capture histological details in high resolution. Recent developments in deep-learning methods have enabled us to automatically detect and characterize the tumor regions in pathology images at large scale. From each identified tumor region, we extracted 30 well-defined descriptors that quantify its shape, geometry, and topology. We demonstrated how those descriptor features were associated with patient survival outcome in lung adenocarcinoma patients from the National Lung Screening Trial (n=143). Besides, a descriptor-based prognostic model was developed and validated in an independent patient cohort from The Cancer Genome Atlas Program program (n=318). This study proposes new insights into the relationship between tumor shape, geometrical, and topological features and patient prognosis. We provide software in the form of R code on GitHub: https://github.com/estfernandez/Slide_Image_Segmentation_and_Extraction.
