Studying measurement variability in tumor volume and volume change on MDCT

描述(由申请人提供)：30年来，评估患者对治疗的反应的标准方法一直是使用系列CT检查来监测肿瘤直径的变化。然而，研究开始表明，传统方法在评估肿瘤对新的靶向治疗的反应方面存在不足。事实上，我们最近的研究将靶向治疗吉非替尼引起的早期放射反应与表皮生长因子受体突变的存在相关联，表明肿瘤体积变化更适合于检测非小细胞肺癌靶点的生物学活性。尽管CT在反应评估中被广泛使用，但CT数据的当代分析及其在指导临床试验解释方面的作用既没有得到验证，也没有得到优化。随着临床试验开始将体积CT(VCT)纳入新疗法的评估，以及已经/正在开发更多的计算机算法来辅助测量肿瘤体积，进行如此严格的评估现在至关重要。我们的第一个目标是系统地探索体积和体积变化测量的来源和变异性，这些测量在图像采集和肿瘤大小测量中引入，使用的是已知基本事实的FDA胸模(体积)和接受同一天重复CT扫描的体内肺癌肿瘤(目标1和2)。实现这一目标将使我们能够获得关键的见解，了解不同的CT供应商平台和扫描参数、连续CT扫描和图像分割算法如何以及在多大程度上影响肿瘤体积测量和后续反应评估。这些信息将有助于建立CT成像方案的标准，并选择适当的算法，以提高多中心临床试验中体积测量和反应评估的准确性和一致性。即使在准确性和重复性方面有所改善，单凭增强图像量化也不会被肿瘤学社区接受为新疗法的改进生物标记物，而没有显示出VCT与临床结果的强烈相关性。因此，我们的第二个目标是验证VCT作为预测病理反应和无病生存的早期和更准确的生物标志物。我们将在一项前瞻性多中心肺癌试验(CALGB 30803)中这样做，该试验正在评估一种新的新辅助方案(目标3)。由于CT成像技术在全球可用，我们的算法可以广泛分布，在标准计算机上运行，并与图像存档和通信系统集成，我们预计我们的发现将广泛用于药物开发和临床护理。

DESCRIPTION (provided by applicant): For 30 years, the standard way to assess a patient's response to treatment has been to monitor changes in tumor diameter using serial CT exams. However, studies began to show the inadequacy of conventional methods in assessing tumor responses to novel targeted therapies. Indeed, our recent study correlating early radiographic response induced by a targeted therapy, gefitinib, with the presence of epidermal growth factor receptor mutations suggests that tumor volume change is better for detecting biologic activity of the target in non-small cell lung cancer. Despite the widespread use of CT in response assessment, contemporary analysis of CT data and its role in guiding clinical trial interpretation has neither been validated nor optimized. It is now critical that such a rigorous evaluation be undertaken as clinical trials begin incorporating volumetric CT (VCT) into the assessment of new therapies and as more computer algorithms haven been/are being developed for assisting in measuring tumor volumes. Our first goal is to systematically explore sources and variability in volume and volume change measurements that are introduced during image acquisition and tumor size measurement using both an FDA chest phantom with known ground truth (volume) and in vivo lung cancer tumors taken on same-day repeat CT scans (Aims 1 and 2). Achieving this goal will allow us to gain key insights into how and to what extent different CT vendor platforms and scanning parameters, serial CT scans, and image segmentation algorithms affect tumor volume measurement and subsequent response assessment. Such information will be essential for helping establish standards for CT imaging protocols and select appropriate algorithms to improve accuracy, consistency of volume measurements and response assessments in multicenter clinical trials. Even with improvements in accuracy and reproducibility, enhanced image quantification alone will not be accepted by the oncology community as an improved biomarker for novel therapies without showing a robust correlation of VCT with clinical outcomes. Our second goal, therefore, is to validate VCT as an early and more accurate biomarker for predicting pathologic response and disease free survival. We will do this in a prospective multicenter lung cancer trial (CALGB 30803) that is evaluating a novel neoadjuvant regimen (Aim 3). Because CT imaging technology is globally available, our algorithm can be widely distributed, run on standard computers and be integrated with picture archiving and communication systems, we expect that our findings will be broadly useful for both drug discovery and clinical care.