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：
10177883
负责人：
Lawrence H Schwartz
金额：
$ 60.95万
依托单位：
COLUMBIA UNIVERSITY HEALTH SCIENCES
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 2023-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10177883
关键词：
Antineoplastic Agents Biological Markers Cancer Etiology Carboplatin Cetuximab Clinical Clinical Trials Communities DNA Sequence Alteration Data Data Set Decision Making Development Disease Drug Targeting Enrollment Environment Epidermal Growth Factor Receptor Future Gene Mutation Genomics Goals Health Histologic Image Image Analysis Imaging Device Immunotherapy Investigation Investigational Therapies Knowledge Lesion Lung Machine Learning Malignant Neoplasms Malignant neoplasm of lung Measurement Measures Metadata Modeling Molecular Target Multi-Institutional Clinical Trial Mutation Neoplasm Metastasis Non-Small-Cell Lung Carcinoma Online Systems Outcome Paclitaxel Patient-Focused Outcomes Patients Phase Phenotype Positron-Emission Tomography Predictive Value Progression-Free Survivals Protocols documentation Recording of previous events Recurrence Reporting Research Research Personnel Site Software Tools Testing The Cancer Genome Atlas The Cancer Imaging Archive Therapeutic Therapeutic Agents Therapeutic Effect Time Translating Translational Research Tumor Burden Tumor Volume Validation Work X-Ray Computed Tomography arm base bevacizumab biomarker-driven cancer clinical trial cancer imaging cancer type chemotherapy clinical decision support clinical practice data sharing drug discovery early detection biomarkers follow-up genomic signature imaging modality imaging platform immune checkpoint blockade improved innovation machine learning method member molecular targeted therapies mortality multi-site trial mutant mutational status novel strategies novel therapeutics personalized medicine phase III trial predict clinical outcome predictive modeling primary endpoint prognostic value quantitative imaging radiologist radiomics response response biomarker screening segmentation algorithm success tissue biomarkers tool tumor tumor growth tumor heterogeneity virtual biopsy

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.

这项研究的目标是将我们通过以下方式开发的软件工具进行临床翻译：定量成像网络并验证其评估临床癌症反应的能力试验。目前的 RECIST 反应标准不足以检测靶向的肿瘤变化分子疗法和免疫疗法是药物发现的两种最有前途的途径。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 成像工具识别的，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。两项大型多站点试验提出了在采用多种治疗方案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 是一项已完成的 III 期试验，有 1300 多名患者和肺 - MAP (S1400) 是一款正在进行中的同类首个 II/III 期模型，预计将招募多达 5,000 名患者使用多药物、有针对性的筛选方法将患者与子研究相匹配的患者根据其独特的肿瘤特征测试研究治疗方法。目标 1 测试是否通过我们先进的体积分割测量肿瘤体积随时间的变化算法，优于一维 RECIST 1.1 响应标准。目标 2 关联基因组 S0819 和 Lung-MAP 中鉴定的突变，具有由我们构建的放射组学特征机器学习模型，目标是开发一种非侵入性、易于重复的虚拟模型通过 CT 成像进行活检。目标 3 使用早期方法验证临床结果的预测基于定量 CT 放射组学特征的反应和进展生物标志物，假设在治疗方案中优于 RECIST 和单独的容量测定包括化疗、靶向分子制剂和免疫检查点封锁。我们的工作具有重大的健康意义，因为体积和放射组学变化的验证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并提供帮助让患者接受个性化治疗。通过本研究制定的响应标准将是由于 CT 是最常见的癌症成像方式，因此广泛适用于临床实践并且定量图像分析工具可以轻松地融入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。该项目的数据，包括匿名影像数据（所有患者的 CT 数据和大部分患者的 PET 数据）、临床荟萃分析独立放射科医生的数据和病变标记将被共享以供其他研究人员使用通过 TCGA 癌症影像档案，继续广泛的数据共享历史这支球队。