PREDICTING HUMAN TUMOR RESPONSE BY 31P MRS

通过 31P MRS 预测人类肿瘤反应

• 批准号: 2882401
• 负责人: SARAH J. NELSON
• 金额: $ 36.33万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 1995
• 资助国家: 美国
• 起止时间: 1995-05-18 至 2001-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/2882401
• 关键词: acidity /alkalinity; breast neoplasms; clinical trials; combination therapy; cooperative study; gadolinium; human subject; human therapy evaluation; magnetic resonance imaging; neoplasm /cancer chemotherapy; neoplasm /cancer radiation therapy; neoplasm /cancer therapy; nuclear magnetic resonance spectroscopy; prognosis; relapse /recurrence; sarcoma; statistics /biometry

DESCRIPTION: This application from the UCSF, is one of eight coordinated applications to support a multi-center clinical trial which will investigate the efficacy of 31-P MRS in predicting and evaluating tumor response to therapy. Although, each of the eight participating institutions will perform their own independent research, the same data acquisition and analysis procedures will be used in all institutions so that comparable, reproducible results are obtained. Each institution will submit the results of their sequential studies on patients with either sarcomas, breast tumors, non- Hodgkins lymphomas or head and neck carcinomas for central statistical analyses. The hypotheses being tested are that pre-treatment pH and T2 can predict of treatment effectiveness and that early changes in 31-P metabolites indicate response to therapy. These hypotheses are based upon preliminary data from numerous research studies. The patients being considered at UCSF have squamous cell carcinoma of the head and neck. Twenty patients per year will be recruited from individuals who are already participating in the multi- center trials RTOG 88-17, RTOG 90-03 or RTOG 91-11. The treatments being compared are combinations of different types of fractionated radiotherapy with, in some cases, additional chemotherapy. MRI and proton decoupled 3 D 31-P localized MRS examinations, will be used to characterize metastatic lymph nodes of the neck pre-treatment, at 2, 8 and 20 days after the start of radiation therapy and at the end of therapy. These will determine whether the 31-P data are able to predict the effectiveness of the radiation therapy, detect lack of response at an early stage or guide in deciding if post- irradiation node dissection is required. MRI and dynamic studies of Gadolinium uptake will also be performed pre-treatment, at the end of radiation therapy and at subsequent 3 monthly follow-ups. These studies will provide pilot data to investigate the hypothesis that changes in rate of Gadolinium uptake may assist in distinguishing active or recurrent tumor from necrosis. If this is the case, MRI follow-up may prove effective in detecting early recurrence. The results of this and the overall analysis of 31-P MRS data will provide valuable information which could have a major impact on the management of patients with a wide variety of different cancers.

描述：此应用程序来自 UCSF，是八个协调的应用程序之一 支持多中心临床试验的申请，该试验将 研究 31-P MRS 在预测和评估肿瘤中的功效 对治疗的反应。 虽然八位参赛者每人 机构将进行自己的独立研究，相同的数据 所有机构都将使用采集和分析程序，以便 获得可比较的、可重复的结果。 各机构 将提交他们对患有以下疾病的患者进行的序贯研究的结果 肉瘤、乳腺肿瘤、非霍奇金淋巴瘤或头颈部肿瘤 用于中央统计分析的癌症。 假设是 经测试，预处理 pH 和 T2 可以预测处理效果 有效性以及 31-P 代谢物的早期变化表明 对治疗的反应。 这些假设基于初步数据 来自大量的研究。 UCSF 正在考虑的患者 患有头颈部鳞状细胞癌。 每人二十名患者 今年将从已经参与的个人中招募 在多中心试验 RTOG 88-17、RTOG 90-03 或 RTOG 91-11 中。 这 所比较的治疗方法是不同类型的组合 分段放疗，在某些情况下还需要进行额外的化疗。 MRI 和质子解耦 3D 31-P 局部 MRS 检查，将 用于表征治疗前颈部转移淋巴结的特征， 放射治疗开始后 2、8 和 20 天以及结束时 的治疗。 这些将决定 31-P 数据是否能够 预测放射治疗的有效性，检测放射治疗的缺乏 早期响应或指导决定是否照射后节点 需要进行解剖。 钆吸收的 MRI 和动态研究 还将在放射治疗结束时进行治疗前和 在随后的 3 个月随访中。 这些研究将提供试点 数据来调查钆比率变化的假设 摄取可能有助于区分活动性或复发性肿瘤 坏死。 如果是这种情况，MRI 随访可能会有效 检测早期复发。 本次结果和总体结果 31-P MRS 数据的分析将提供有价值的信息 对多种患者的治疗产生重大影响 不同的癌症。

项目成果

Assessment of metastatic cervical adenopathy using dynamic contrast-enhanced MR imaging.

使用动态对比增强磁共振成像评估转移性颈部腺病。

• 发表时间: 2003
• 期刊: AJNR. American journal of neuroradiology
• 作者: Fischbein,NancyJ;Noworolski,SusanM;Henry,RolandG;Kaplan,MichaelJ;Dillon,WilliamP;Nelson,SarahJ
• 通讯作者: Nelson,SarahJ