Development and Validation of Prognostic Radiomic Markers of Response and Recurrence for Patients with Colorectal Liver Metastases

结直肠肝转移患者反应和复发的预后放射学标志物的开发和验证

• 批准号: 9761718
• 负责人: Yun Shin Chun
• 金额: $ 74.41万
• 依托单位: SLOAN-KETTERING INST CAN RESEARCH
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-03-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9761718
• 关键词: Address; Affect; Algorithms; Biological Markers; Biometry; Cancer Center; Cancer Etiology; Cessation of life; Clinical; Clinical Trials; Collaborations; Colorectal; Colorectal Cancer; Computer software; Data; Data Set; Decision Making; Development; Diagnostic; Disease; Disease Progression; Ensure; Excision; Eye; Goals; Hepatic; Human; Image; Image Analysis; Infusion procedures; Institutes; Institution; Link; Malignant Neoplasms; Malignant neoplasm of liver; Manufacturer Name; Medical Oncology; Medical center; Metastatic Neoplasm to the Liver; Methods; Modeling; Network-based; Operative Surgical Procedures; Outcome; Pathology; Pathway interactions; Patient Selection; Patients; Pattern; Performance; Phase; Phase I/II Trial; Prognostic Marker; Prospective Studies; Publications; Radiology Specialty; Recurrence; Regional Chemotherapy; Reproducibility; Research; Resolution; Scanning; Series; Specificity; Standardization; System; Technology; Testing; Therapeutic; Training; United States; University of Texas M D Anderson Cancer Center; Update; Validation; Venous; Western World; X-Ray Computed Tomography; base; cancer care; cancer diagnosis; cancer imaging; chemotherapy; clinical care; colon cancer patients; comparative; contrast enhanced; convolutional neural network; cost; design; efficacy trial; experience; high risk; image archival system; imaging biomarker; imaging modality; improved; indexing; individualized medicine; innovation; mortality; novel; novel marker; personalized cancer therapy; personalized medicine; phantom model; precision medicine; predicting response; prediction algorithm; prevent; prognostic; programs; prospective; quantitative imaging; radiomics; reconstruction; response; response biomarker; specific biomarkers; tool

SUMMARY Colorectal cancer is the second leading cause of cancer-related mortality in the United States. More than 50% of patients with colorectal cancer will develop liver metastases in their lifetime with a dismal <10% surviving past three years. A major therapeutic problem in this disease is that no markers prognostic of hepatic recurrence or predictive of response prior to treatment are known. The goal of this research is to fill this gap by providing non-invasive and objective prognostic quantitative imaging markers for personalized treatment of colorectal liver metastases (CRLM). Our single-institution data support that quantitative imaging features extracted from routine CT scans predict volumetric response to systemic and regional chemotherapy and identify patients at high risk of hepatic recurrence and poor survival. Progress in developing these novel markers is limited by a lack of optimization, standardization, and validation, all critical barriers to clinical use. The objectives of this application are to develop and validate robust imaging features by standardizing image acquisition, to improve automated tools for clinical trial use, and to validate the predictive power of imaging features with external data. We have partnered with University of Texas MD Anderson Cancer Center, Rensselaer Polytechnic Institute, and GE Research, facilitating the widespread integration of the proposed technology into medical centers worldwide. Our central hypothesis is that quantitative CT-based imaging features provide novel and robust indices for predicting response, hepatic recurrence, and survival in CRLM patients. Specifically, we will (1) validate predictive and prognostic imaging features with external data, (2) prospectively assess repeatability and reproducibility of contrast-enhanced CT imaging features, and (3) develop an integrated rawdiomics pipeline by fully utilizing sinogram data. We have assembled a critical mass of experts in surgery, medical oncology, pathology, radiology, biostatistics, and image analysis. Combined with the largest clinical experience in CRLM in the western world, this application is a unique and unrivaled opportunity to define radiomics of CRLM. Integration into existing clinical workflows means that small medical centers without highly specialized radiology groups would benefit from predictive algorithms developed at two high-volume centers via a low-cost software update. Successful completion of our aims will provide validated prognostic imaging markers with a pathway to routine clinical use, which are of paramount importance to improving patient survival of this deadly disease.

概括 结直肠癌是美国癌症相关死亡的第二大原因。超过50% 的结直肠癌患者在其一生中会发生肝转移，存活率低至<10% 过去三年。该疾病的一个主要治疗问题是没有肝病预后标志物 治疗前复发或反应的预测是已知的。本研究的目标是通过以下方式填补这一空白 为个体化治疗提供非侵入性、客观的预后定量成像标志物 结直肠肝转移（CRLM）。我们的单一机构数据支持定量成像特征 从常规 CT 扫描中提取的数据可预测对全身和区域化疗的体积反应， 识别肝复发高风险和生存率低的患者。这些小说的开发进展 标记物因缺乏优化、标准化和验证而受到限制，这些都是临床使用的关键障碍。 该应用程序的目标是通过标准化图像来开发和验证强大的成像功能 采集，改进临床试验使用的自动化工具，并验证成像的预测能力 具有外部数据的功能。我们与德克萨斯大学 MD 安德森癌症中心合作， 伦斯勒理工学院和 GE 研究中心，促进拟议的广泛整合 技术进入全球医疗中心。我们的中心假设是基于 CT 的定量成像 这些特征为预测 CRLM 的反应、肝复发和生存提供了新颖且可靠的指标 患者。具体来说，我们将 (1) 使用外部数据验证预测和预后成像特征，(2) 前瞻性评估对比增强 CT 成像特征的可重复性和再现性，以及 (3) 通过充分利用正弦图数据开发集成的原始组学管道。我们已经聚集了足够多的人 由外科、肿瘤内科、病理学、放射学、生物统计学和图像分析方面的专家组成。结合 西方世界最大规模的 CRLM 临床经验，该应用是独一无二的、无与伦比的 定义 CRLM 放射组学的机会。集成到现有的临床工作流程意味着小型医疗 没有高度专业化放射学小组的中心将受益于两个项目开发的预测算法 通过低成本的软件更新来实现高容量中心。成功完成我们的目标将提供经过验证的 具有常规临床使用途径的预后影像标记物，这对于 提高这种致命疾病患者的生存率。