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

这项研究的目标是临床翻译软件工具，我们通过开发， 定量成像网络，并验证他们的能力，以评估癌症的反应，在临床上 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，这是药物发现的两个最有前途的途径。 我们假设，反应和进展的创新体积和放射组学特征， 使用我们的定量CT成像工具识别，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。两项大型多中心试验提供了 在一种疾病中用多种治疗方案来检验这一假设的独特机会 由组织生物标志物驱动。S 0819是一项已完成的III期试验，有1300多名患者和肺- MAP（S1400）是一个正在进行的同类第二/第三阶段模型，预计将招募多达5000人 使用多种药物、靶向筛选方法将患者与子研究匹配的患者 根据他们独特的肿瘤特征测试研究性治疗。目标1测试是否 肿瘤体积随时间的变化，通过我们的高级体积分割测量 算法，优于一维RECIST 1.1响应标准。Aim 2相关基因组 在S 0819和Lung-MAP中鉴定的突变与我们构建的放射组学特征 机器学习模型，目标是开发非侵入性、易于重复的虚拟 通过CT成像进行活检。目的3验证了使用早期 基于定量CT放射组学特征的缓解和进展生物标志物， 假设在治疗方案中优于RECIST和单独的容量测定 包括化学疗法、靶向分子试剂和免疫检查点阻断。我们 工作具有实质性的健康意义，因为验证体积和放射性变化， 反应或进展的早期生物标志物将指导药物发现的临床试验， 为患者提供个性化治疗。通过本研究制定的缓解标准将是 广泛适用于临床实践，因为CT是最常见的癌症成像模式 并且定量图像分析工具可以容易地结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。从这个项目的数据， 包括匿名成像数据（所有患者的CT和大子集的PET），临床Meta分析， 数据和独立放射科医生的病变标记将被其他研究人员共享 通过TCGA癌症成像档案，继续广泛的数据共享历史， 这个团队

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.