Quantitative analysis of tumor burden in mouse lung via MRI.
Quantitative analysis of tumor burden in mouse lung via MRI.
复制标题
DOI：
10.1002/mrm.22951
复制
发表时间：
2012-02
影响因子：
3.3
通讯作者：
Nehorai, Arye
中科院分区：
文献类型：
--
作者：
Tidwell, Vanessa K.;Garbow, Joel R.;Krupnick, Alexander S.;Engelbach, John A.;Nehorai, Arye
关键词：
肺癌是美国癌症死亡的主要原因。尽管最近在筛查方案方面取得了进展，但大多数患者仍然存在晚期或播散性疾病。临床前啮齿动物模型为测试靶向肺癌的新型治疗药物提供了独特的机会。呼吸门控MRI是定量测量肺肿瘤负荷和监测原发性和转移性肺癌小鼠模型中个体肿瘤时程进展的关键工具。然而，通过MRI定量分析小鼠肺肿瘤负荷存在重大挑战。在此，描述并验证了一种基于平均肺图像强度测量肿瘤负荷的方法。该方法需要准确的肺分割;其效率和吞吐量将大大有助于自动分割肺的能力。提出了一种在不同肿瘤负荷水平下自动肺分割的技术。该方法包括开发一个新的，2维参数模型的小鼠肺和多方面的成本函数，以最佳地适合每个图像的模型参数。结果表明，与全手动专家分割相比，自动化方法的肿瘤负荷度量与肺重量测量的肿瘤负荷之间具有很强的相关性（0.93）。
Lung cancer is the leading cause of cancer death in the United States. Despite recent advances in screening protocols, the majority of patients still present with advanced or disseminated disease. Pre-clinical rodent models provide a unique opportunity to test novel therapeutic drugs for targeting lung cancer. Respiratory-gated MRI is a key tool for quantitatively measuring lung-tumor burden and monitoring the time-course progression of individual tumors in mouse models of primary and metastatic lung cancer. However, quantitative analysis of lung-tumor burden in mice by MRI presents significant challenges. Herein, a method for measuring tumor burden based upon average lung-image intensity is described and validated. The method requires accurate lung segmentation; its efficiency and throughput would be greatly aided by the ability to automatically segment the lungs. A technique for automated lung segmentation in the presence of varying tumor burden levels is presented. The method includes development of a new, 2-dimensional parametric model of the mouse lungs and a multi-faceted cost function to optimally fit the model parameters to each image. Results demonstrate a strong correlation (0.93), comparable with that of fully-manual expert segmentation, between the automated method’s tumor-burden metric and the tumor burden measured by lung weight.
登录
查看更多内容
有效使用 PI3K 和 MEK 抑制剂治疗突变型 Kras G12D 和 PIK3CA H1047R 小鼠肺癌。
影响因子：
82.9
作者：
通讯作者：
--
影响因子：
4.8
作者：
Boettger, Thomas;Kunert, Tobias;Wolf, Ivo
通讯作者：
Wolf, Ivo
DOI：
10.1152/ajplung.00515.2007
发表时间：
2008-07-01
影响因子：
4.9
作者：
Serkova, Natalie J.;Van Rheen, Zachary;Stringer, Kathleen A.
通讯作者：
Stringer, Kathleen A.
影响因子：
6.4
作者：
Rosen, Yael;Ramniceanu, Gregory;Furman-Haran, Edna
通讯作者：
Furman-Haran, Edna
影响因子：
254.7
作者：
Greenlee, RT;Murray, T;Wingo, PA
通讯作者：
Wingo, PA