Integrating Radiomics into S0819 and Lung-MAP, Biomarker Driven Clinical Trials for Lung Cancer

这项研究的目标是将我们通过 定量成像网络和验证其评估癌症临床反应的能力 审判。目前的RECIST反应标准不足以检测靶向肿瘤的变化 分子治疗和免疫治疗是药物发现最有希望的两个途径。 我们假设，反应和进展的创新体积和放射组学信号， 使用我们的定量CT成像工具进行识别，可以集成到临床试验工作流程中 满足对RECIST标准替代品的迫切需求。两个大型多站点试验提供了一个 在一种疾病中验证这一假设的独特机会，可采用多种治疗方案 由组织生物标记物驱动。S0819是一项完成的第三阶段试验，有1300多名患者和肺- MAP(S1400)是正在进行的同类第一阶段II/III模式，预计可容纳多达5,000人 患者使用多药、有针对性的筛查方法将患者与子研究相匹配 根据他们独特的肿瘤特征测试研究治疗方法。AIM 1测试 通过我们先进的体积分割测量的肿瘤体积随时间的变化 算法，性能优于一维RECIST 1.1响应标准。目标2与基因组相关 用我们构建的放射组学特征鉴定S0819和肺MAP的突变 机器学习模型，目标是开发一种非侵入性的、易于重复的虚拟 通过CT成像进行活组织检查。目标3验证使用早期预测的临床结果 基于定量CT放射组学特征的反应和进展的生物标志物， 假设在所有治疗方案中都优于RECIST和容量测量 包括化疗、靶向分子制剂和免疫检查站封锁。我们的 这项工作具有实质性的健康意义，因为对体积和放射学变化的验证 早期反应或进展的生物标志物将指导药物发现的临床试验并帮助 将患者与个性化治疗相匹配。通过这项研究制定的反应标准将是 广泛应用于临床，因为CT是最常见的肿瘤成像方式 定量图像分析工具可以很容易地整合到现有的流行成像中 平台和临床工作流程，减少放射科医生所需的时间。来自这个项目的数据， 包括匿名成像数据(所有患者的CT和较大子集的PET)、临床元数据 独立放射科医生的数据和病变标记将被共享，供其他研究人员使用 通过TCGA癌症影像档案，延续了广泛的数据共享历史 这支队伍。

The goal of this research is to clinically translate software tools we developed through the Quantitative Imaging Network and validate their ability to assess the response of cancer in clinical trials. Current RECIST response criteria are inadequate to detect tumor changes in targeted molecular therapy and immunotherapies, two of the most promising avenues for drug discovery. We hypothesize that innovative volumetric and radiomics signatures of response and progression, identified using our quantitative CT imaging tools, can be integrated into clinical trial workflow to meet the urgent need for alternatives to RECIST criteria. Two large multi-site trials present a unique opportunity to test this hypothesis in one disease treated with multiple therapeutic options driven by tissue biomarkers. S0819 is a completed Phase III trial with 1300+ patients and Lung- MAP (S1400) is an ongoing first-of-its-kind Phase II/III model projected to enroll up to 5,000 patients using a multi-drug, targeted screening approach to match patients with sub-studies testing investigational treatments based on their unique tumor profiles. Aim 1 tests whether change in tumor volume over time, measured by our advanced volumetric segmentation algorithms, outperforms unidimensional RECIST 1.1 response criteria. Aim 2 correlates genomic mutations identified in S0819 and Lung-MAP with radiomics signatures constructed by our machine learning models, with the goal of developing a non-invasive, easily repeatable virtual biopsy through CT imaging. Aim 3 validates the prediction of clinical outcomes using early biomarkers of response and progression based on quantitative CT-based radiomic features, hypothesized to outperform both RECIST and volumetrics alone across therapeutic options including chemotherapies, targeted molecular agents, and immune checkpoint blockade. Our work has substantial health significance because validation of volume and radiomic changes as early biomarkers of response or progression will guide clinical trials for drug discovery and help match patients to personalized treatment. Response criteria developed through this study will be widely applicable to clinical practice because CT is the most common cancer imaging modality and the quantitative image analysis tools can easily be incorporated into existing popular imaging platforms and clinical workflow, reducing the time required by radiologists. Data from this project, including anonymized imaging data (CT for all patients and PET for a large subset), clinical meta- data, and lesion mark-ups by independent radiologists, will be shared for use by other researchers through the TCGA Cancer Imaging Archive, continuing an extensive history of data sharing by this team.