Radiomics of NSCLC

描述（由申请人提供）：非小细胞肺癌（NSCLC）是最常见的癌症，也是死亡率最高的癌症之一。因此，预测反应和个体化治疗能力的任何进步都将产生巨大影响。 NSCLC 患者通常使用 PET 和 CT 进行成像，分别用于分期和监测。当前工作的主要假设是，这些临床图像的定量分析可以预测和预测对特定疗法的反应。如果属实，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、基于证据的医学。 为了检验这一假设，该项目将从佛罗里达州坦帕莫菲特癌症中心和荷兰马斯特里赫特 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。这些数据库包含来自数百名 III 期和 IV 期 NSCLC 患者的图像、基因表达谱和结果数据。使用开发商业软件从每张图像中提取 100 多个特征。从 Moffitt 数据集中回顾性提取的特征将进行定量分析，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获得的 Moffitt 前瞻性数据进行重新测试。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严格性和分辨率。通过正确的功能组合，在临床环境中可以轻松实现所需的严格性和分辨率。 顶点实验将在治疗诊断试验中添加图像特征提取，将治疗与个体患者的两种蛋白质表达模式相匹配，从而预测对特定治疗的反应。要测试的假设是，图像特征可以在没有分子活检数据的情况下将患者划分为特定的治疗方案。 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定量分析是否可用于预后结果或预测对肺癌特定疗法的反应。如果属实，这将提高临床成像在该疾病中的实用性，并有可能改善每年多达 215,000 名患者的护理，而不必增加成本。

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.