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

将放射组学整合到 S0819 和 Lung-MAP、生物标志物驱动的肺癌临床试验中

基本信息

批准号：
10850084
负责人：
Lawrence H Schwartz
金额：
$ 56.61万
依托单位：
SLOAN-KETTERING INST CAN RESEARCH
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-06-01 至 2024-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10850084
关键词：
Integrating Radiomics into S0819 Lung

项目摘要

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.

这项研究的目的是临床翻译我们通过的软件工具定量成像网络并验证其评估临床癌症反应的能力试验。当前的恢复响应标准不足以检测靶向的肿瘤变化分子疗法和免疫疗法，这是药物发现最有前途的两种途径。我们假设响应和进展的创新体积和放射线学特征，使用我们的定量CT成像工具确定，可以将其集成到临床试验工作流程中满足迫切需要恢复标准的替代需求。两个大型多站点试验呈现在用多种治疗选择治疗的一种疾病中检验这一假设的独特机会由组织生物标志物驱动。 S0819是一项完整的III期试验，有1300多名患者和肺 MAP（S1400）是一种持续的II II/III阶段模型，预计将注册多达5,000 使用多药，有针对性筛查方法与子研究患者相匹配的患者根据其独特的肿瘤特征测试研究治疗。 AIM 1测试是否随着时间的推移，肿瘤体积的变化，通过我们的晚期体积分割来衡量算法，优于一维再及时响应标准。 AIM 2相关基因组在S0819中鉴定出的突变和肺图与我们的放射线学特征由我们构建机器学习模型，目的是开发非侵入性，易于重复的虚拟通过CT成像进行活检。 AIM 3使用早期验证了临床结果的预测基于定量CT的放射线特征的反应和进展的生物标志物，假设仅在治疗方案中仅均超过恢复和体积包括化学疗法，靶向分子剂和免疫检查点阻滞。我们的工作具有很大的健康意义，因为验证数量和放射素变化为早期反应或进展的生物标志物将指导临床试验进行药物发现和帮助将患者与个性化治疗相匹配。通过这项研究制定的响应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方式并且可以轻松地将定量图像分析工具合并到现有的流行成像中平台和临床工作流程，减少了放射科医生所需的时间。来自该项目的数据，包括匿名成像数据（所有患者的CT和大子集的PET），临床元独立放射科医生的数据和病变标记将与其他研究人员共享通过TCGA癌症成像档案，延续了广泛的数据共享历史这个团队。