Radiomics of NSCLC

描述(申请人提供)：非小细胞肺癌，NSCLC，是最常见的癌症，具有最高的死亡率之一。因此，预测反应和个体化治疗能力的任何进步都将产生重大影响。NSCLC患者常规接受PET和CT成像，分别用于分期和监测。目前工作的主要假设是，对这些临床图像的定量分析可以预测和预测对特定治疗的反应。如果是真的，这些结果将通过改善护理和结果而具有医学意义。这也将具有社会经济意义，因为它将允许使用标准护理图像来实践先进的循证医学。为了验证这一假设，该项目将从佛罗里达州坦帕市的莫菲特癌症中心和荷兰马斯特里赫特的MAASTRO诊所的两个强大的患者数据库中提取可挖掘的成像数据。这些数据库包含数百名III和IV期非小细胞肺癌患者的图像、基因表达谱和结果数据。将使用开发的商业软件从每幅图像中提取100多个特征。从Moffitt数据集回溯提取的特征将被定量分析，以生成基因表达模式和无进展生存的预测模型。这些模型将在MAASTRO数据集中进行测试，并使用在严格条件下从Moffitt获得的预期数据进行重新测试。这项工作的一个重要成果将定义图像在预测模型中有用所需的精确度和分辨率。有了正确的特征组合，所需的严格性和分辨率在临床环境中可能很容易实现。Capstone实验将在一项诊断试验中增加图像特征提取，该试验将治疗与预测特定治疗反应的两种蛋白质的个体患者表达模式相匹配。需要检验的假设是，图像特征可以在没有分子活检数据的情况下将患者分割到特定的治疗方案。 相关性：这项工作将确定在临床护理标准期间获得的图像的量化分析是否可以用于预测肺癌的预后或预测对特定治疗的反应。如果是真的，这将增加临床成像在这种疾病中的效用，并有可能改善每年多达21.5万名患者的护理，而不一定会增加成本。

DESCRIPTION (provided by applicant): Non small cell lung cancer, NSCLC, is the most prevalent of cancers and has one of the highest mortality rates. Thus, any advance in the ability to predict response and individualize treatment will have great impact. NSCLC patients are routinely imaged with PET and CT for staging and monitoring, respectively. The major hypothesis of the current work is that quantitative analysis of these clinical images can be prognostic and predictive of response to specific therapies. If true, these results would have medical significance through improved care and outcomes. This would also have socioeconomic significance as it would allow advanced, evidence based medicine to be practiced using standard-of-care images. To test this hypothesis, this project will extract mineable imaging data from two powerful patient databases at the Moffitt Cancer Center in Tampa, FL and the MAASTRO clinic in Maastricht, the Netherlands. These databases contain images, gene expression profiling and outcomes data from hundreds of stage III and IV NSCLC patients. Over 100 features will be extracted from each image using developmental commercial software. Features extracted retrospectively from the Moffitt dataset will be quantitatively analyzed to generate predictive models for gene expression patterns and progression-free survival. These models will be tested in the MAASTRO data set and re-tested using prospective data from Moffitt acquired under rigorous conditions. An important outcome of this work will define the rigor and resolution needed for images to be useful in predictive models. With the right combination of features, the needed rigor and resolution may be readily achievable in a clinical setting. A capstone experiment will add image feature extraction to a theragnostic trial that matches therapy to individual patient expression patterns for two proteins that predict response to specific therapies. The hypothesis to be tested is that image features can segment patients to specific therapy regimens without the molecular biopsy data. RELEVANCE: This work will determine if quantitative analysis of images obtained during clinical standards of care can be used to prognose outcome or predict response to specific therapies in lung cancer. If true, this would increase the utility of clinical imaging in this disease and potentially improve the care for up to 215,000 patients annually without necessarily increasing in the cost.