Quantitative Volume and Density Response Assessment: Sarcoma and HCC as a Model

定量体积和密度响应评估：肉瘤和 HCC 作为模型

• 批准号: 8327118
• 负责人: Lawrence H Schwartz
• 金额: $ 58.37万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8327118
• 关键词: Algorithms; Antineoplastic Agents; BAY 54-9085; Biochemical; Biochemical Markers; Biological Markers; Biology; Blinded; Cancer and Leukemia Group B; Clinical; Clinical Data; Clinical Treatment; Clinical Trials; Companions; Complex; Computer Assisted; Computers; Cytotoxic Chemotherapy; Data; Development; Enrollment; Epidermal Growth Factor Receptor; Evaluation; Future; Goals; Grant; Health; Home environment; Image; Image Analysis; Instruction; Intervention; Lesion; Liver; Lung; Lymph; Malignant Neoplasms; Measurement; Measures; Medical Imaging; Methods; Modeling; Molecular; Mutation; Necrosis; Non-Small-Cell Lung Carcinoma; Oils; Outcome; Palpation; Patients; Phase; Predictive Value; Primary carcinoma of the liver cells; Principal Investigator; Probability; Reader; Reproducibility; Research; Running; Solid Neoplasm; Study models; Technology; Testing; Time; Translating; Tumor Burden; Tumor Volume; Validation; Work; base; cancer therapy; clinical practice; cytotoxic; density; detector; drug development; drug discovery; evidence base; follow-up; imaging modality; improved; lung volume; lymph nodes; novel; phase 2 study; radiologist; research clinical testing; response; sarcoma; soft tissue; tumor

DESCRIPTION (provided by applicant): The goal of this research is to develop new response assessments for cancer treatment based on CT imaging of changes in tumor volume and necrosis fraction. Current RECIST criteria and cut-off values for response assessment are not evidence-based and may fail to detect the tumor changes associated with clinical response to targeted, non-cytotoxic treatments. This study will seek a proof of concept using two types of tumors in which RECIST is known to correlate poorly with tumor response to treatment and clinical outcome. HCC is one of the most common malignancies worldwide, and sarcomas, though rare, carry the same molecular alterations as many other heterogeneous cancers and are the classic cancer studied in drug discovery. Aim 1 will demonstrate that assistance from new automated segmentation algorithms can reduce the variability in radiologists' measurement of lung, liver, and lymph tumors. This aim will use images from 276 patients already collected by SARC 011, a large phase II multicenter clinical trial of sarcoma. Aim 2 will correlate tumor volume and necrosis fraction with clinical outcome in SARC 011 and CALGB 80802, a phase trial of HCC with an estimated enrollment of 480 patients. The proposed research will first develop criteria based on quantitative biomarkers (tumor volume and necrosis fraction) and then compare the predictive value of these criteria to the current clinical standard using a concordance probability estimate. Aim 3 will explore the correlation of these criteria to other biochemical markers, and use a concordance probability estimate to determine whether the combination of imaging biomarkers with biochemical markers affords superior prediction of patient survival as compared to either alone. This aim will use data from three companion biology studies of SARC 011 and CALGB 80802. Development and validation of the new criteria will have substantial health significance because evidence that volume and necrosis changes are early biomarkers of response or progression will guide clinical trials and patient treatment. The new criteria will be widely applicable to clinical practice because CT is the most common imaging modality for cancer, the new algorithms run on popular imaging platforms, and this method will reduce the time required by radiologists. RELEVANCE (See instructions); When new treatments for cancer are being tested, images of the patients' tumor are measured to determine whether the treatment is working. By developing a better way to measure tumor changes caused by treatment, this research will aid the discovery of cancer drugs and help match patients to the treatment that works best for them.

描述（由申请人提供）：这项研究的目的是基于CT成像的肿瘤体积变化和坏死分数的CT成像开发新的反应评估。当前的回收标准和响应评估的截止值不是基于证据的，并且可能无法检测到与临床反应有关的临床反应有关的肿瘤变化。这项研究将使用两种类型的肿瘤寻求概念证明，其中已知恢复与肿瘤对治疗和临床结局的反应较差。 HCC是全球最常见的恶性肿瘤之一，尽管肉瘤虽然很少见，但仍具有与许多其他异质性癌症相同的分子改变，并且是药物发现中研究的经典癌症。 AIM 1将表明，新的自动分割算法的援助可以降低放射科医生对肺，肝脏和淋巴肿瘤的测量的变异性。该目标将使用SARC 011已收集的276名患者的图像，Sarc 011是一项大型II期多中心临床试验。 AIM 2将与SARC 011和CALGB 80802中的肿瘤体积和坏死分数与临床结果相关联，这是HCC的相位试验，估计入学率为480例患者。拟议的研究将首先基于定量生物标志物（肿瘤体积和坏死部分）制定标准，然后使用一致性概率估算值将这些标准的预测值与当前临床标准进行比较。 AIM 3将探讨这些标准与其他生化标志物的相关性，并使用一致性概率估算来确定成像生物标志物与生化标记物的组合是否可以与单独单独使用的患者生存相比，对患者的生存进行了超越的预测。该目标将使用来自SARC 011和CALGB 80802的三个同伴生物学研究的数据。新标准的开发和验证将具有很大的健康意义，因为证据表明数量和坏死变化是反应或进展的早期生物标志物，将指导临床试验和患者治疗。新标准将广泛适用于临床实践，因为CT是癌症最常见的成像方式，新的算法在流行的成像平台上运行，此方法将减少放射学家所需的时间。相关性（请参阅说明）；当测试新的癌症治疗方法时，测量患者肿瘤的图像以确定治疗是否有效。通过开发一种更好的方法来测量由治疗引起的肿瘤变化，这项研究将有助于发现癌症药物，并帮助患者与最适合他们的治疗相匹配。