Department of Physics & Munich School of Bioengineering, Technical University of Munich, 85748, Garching, Germany.
Institute of Physics, Martin-Luther-University Halle-Wittenberg, 06120, Halle (Saale), Germany.
Sci Rep. 2019 Feb 4;9(1):1325. doi: 10.1038/s41598-018-37394-w.
Tumor volume is a parameter used to evaluate the performance of new therapies in lung cancer research. Conventional methods that are used to estimate tumor size in mouse models fail to provide fast and reliable volumetric data for tumors grown non-subcutaneously. Here, we evaluated the use of iodine-staining combined with micro-computed tomography (micro-CT) to estimate the tumor volume of ex vivo tumor-burdened lungs. We obtained fast high spatial resolution three-dimensional information of the lungs, and we demonstrated that iodine-staining highlights tumors and unhealthy tissue. We processed iodine-stained lungs for histopathological analysis with routine hematoxylin and eosin (H&E) staining. We compared the traditional tumor burden estimation performed manually with H&E histological slices with a semi-automated method using micro-CT datasets. In mouse models that develop lung tumors with well precise boundaries, the method that we describe here enables to perform a quick estimation of tumorous tissue volume in micro-CT images. Our method overestimates the tumor burden in tumors surrounded by abnormal tissue, while traditional histopathological analysis underestimates tumor volume. We propose to embed micro-CT imaging to the traditional workflow of tumorous lung analyses in preclinical cancer research as a strategy to obtain a more accurate estimation of the total lung tumor burden.
肿瘤体积是评估肺癌研究中新疗法性能的一个参数。用于估计小鼠模型中肿瘤大小的常规方法无法为非皮下生长的肿瘤提供快速可靠的体积数据。在这里,我们评估了碘染色结合微计算机断层扫描(micro-CT)用于估计离体肿瘤负荷肺的肿瘤体积的用途。我们获得了肺部快速高空间分辨率的三维信息,并证明了碘染色突出显示了肿瘤和不健康的组织。我们用常规苏木精和伊红(H&E)染色对碘染肺进行了组织病理学分析。我们将手动进行的传统肿瘤负担估计与使用 micro-CT 数据集的半自动方法进行了比较。在具有精确边界的肺部肿瘤形成的小鼠模型中,我们在这里描述的方法能够快速估计 micro-CT 图像中肿瘤组织的体积。我们的方法高估了被异常组织包围的肿瘤的肿瘤负担,而传统的组织病理学分析则低估了肿瘤体积。我们建议将 micro-CT 成像嵌入到临床前癌症研究中肿瘤肺分析的传统工作流程中,作为获得更准确的总肺肿瘤负担估计的策略。