一种用于高精度评估肿瘤对治疗反应变化的新方法。
A new method for the high-precision assessment of tumor changes in response to treatment.
机构信息
Division of Informatics, Imaging and Data Science, Manchester Pharmacy School, Manchester, UK.
Division of Pharmacy and Optometry, Manchester Pharmacy School, Manchester, UK.
出版信息
Bioinformatics. 2018 Aug 1;34(15):2625-2633. doi: 10.1093/bioinformatics/bty115.
MOTIVATION
Imaging demonstrates that preclinical and human tumors are heterogeneous, i.e. a single tumor can exhibit multiple regions that behave differently during both development and also in response to treatment. The large variations observed in control group, tumors can obscure detection of significant therapeutic effects due to the ambiguity in attributing causes of change. This can hinder development of effective therapies due to limitations in experimental design rather than due to therapeutic failure. An improved method to model biological variation and heterogeneity in imaging signals is described. Specifically, linear Poisson modeling (LPM) evaluates changes in apparent diffusion co-efficient between baseline and 72 h after radiotherapy, in two xenograft models of colorectal cancer. The statistical significance of measured changes is compared to those attainable using a conventional t-test analysis on basic apparent diffusion co-efficient distribution parameters.
RESULTS
When LPMs were applied to treated tumors, the LPMs detected highly significant changes. The analyses were significant for all tumors, equating to a gain in power of 4-fold (i.e. equivalent to having a sample size 16 times larger), compared with the conventional approach. In contrast, highly significant changes are only detected at a cohort level using t-tests, restricting their potential use within personalized medicine and increasing the number of animals required during testing. Furthermore, LPM enabled the relative volumes of responding and non-responding tissue to be estimated for each xenograft model. Leave-one-out analysis of the treated xenografts provided quality control and identified potential outliers, raising confidence in LPM data at clinically relevant sample sizes.
AVAILABILITY AND IMPLEMENTATION
TINA Vision open source software is available from www.tina-vision.net.
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
动机
影像学研究表明，临床前和人体肿瘤是异质的，即单个肿瘤在发育过程中以及对治疗的反应中都可能表现出多个不同的区域。由于变化原因的不确定性，对照组中观察到的巨大差异可能会掩盖对治疗效果的显著影响的检测。由于实验设计的限制而不是治疗失败，这可能会阻碍有效的治疗方法的发展。本文描述了一种改进的成像信号中生物变异和异质性的建模方法。具体来说，线性泊松模型（LPM）评估了两种结直肠癌异种移植模型中放疗后 72 小时与基线相比表观扩散系数的变化。与基本表观扩散系数分布参数的传统 t 检验分析相比，比较了测量变化的统计显著性。
结果
当 LPM 应用于治疗后的肿瘤时，LPM 检测到了高度显著的变化。所有肿瘤的分析均具有统计学意义，与传统方法相比，其功效提高了 4 倍（即等效于样本量增加了 16 倍）。相比之下，仅在使用 t 检验时在队列水平上检测到高度显著的变化，这限制了它们在个性化医疗中的潜在用途，并增加了测试过程中所需的动物数量。此外，LPM 能够估计每个异种移植模型中反应性和非反应性组织的相对体积。处理后的异种移植体的留一法分析提供了质量控制，并识别了潜在的异常值，从而在临床相关样本量下提高了对 LPM 数据的信心。
可用性和实施
TINA Vision 开源软件可从 www.tina-vision.net 获得。
补充信息
补充数据可在 Bioinformatics 在线获得。
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