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）：本研究的目标是基于肿瘤体积和坏死分数变化的 CT 成像来开发新的癌症治疗反应评估。目前的 RECIST 标准和疗效评估的临界值不是基于证据的，可能无法检测与靶向非细胞毒性治疗的临床反应相关的肿瘤变化。这项研究将寻求使用两种类型的肿瘤进行概念验证，已知 RECIST 与肿瘤对治疗的反应和临床结果的相关性较差。 HCC 是全世界最常见的恶性肿瘤之一，肉瘤虽然罕见，但与许多其他异质性癌症具有相同的分子改变，是药物发现中研究的经典癌症。目标 1 将证明新的自动分割算法的帮助可以减少放射科医生测量肺、肝和淋巴肿瘤的变异性。该目标将使用 SARC 011（一项大型肉瘤 II 期多中心临床试验）已收集的 276 名患者的图像。目标 2 将肿瘤体积和坏死分数与 SARC 011 和 CALGB 80802 的临床结果相关联，这是一项 HCC 阶段试验，预计入组 480 名患者。拟议的研究将首先制定基于定量生物标志物（肿瘤体积和坏死分数）的标准，然后使用一致性概率估计将这些标准的预测值与当前的临床标准进行比较。目标 3 将探索这些标准与其他生化标记物的相关性，并使用一致性概率估计来确定成像生物标记物与生化标记物的组合是否比单独使用任何一种标记物能够更好地预测患者生存。该目标将使用 SARC 011 和 CALGB 80802 的三项伴随生物学研究的数据。新标准的制定和验证将具有重大的健康意义，因为体积和坏死变化是反应或进展的早期生物标志物的证据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是最常见的癌症成像方式，新算法在流行的成像平台上运行，这种方法将减少放射科医生所需的时间。相关性（参见说明）；当测试新的癌症治疗方法时，会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，并帮助患者匹配最适合他们的治疗方法。