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Quantitative imaging tools to derive DW-MRI oncological biomarkers

Quantitative imaging tools to derive DW-MRI oncological biomarkers
用于导出 DW-MRI 肿瘤生物标志物的定量成像工具
批准号:
10186706
负责人:
AMITA DAVE
金额:
$62.0万
依托单位国家:
美国
项目类别:
财政年份:
2017
资助国家:
美国
项目状态:
已结题
起止时间:
2017-07-01 至 2023-06-30

项目摘要

项目成果

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中文摘要
翻译
摘要: 我们建议开发、优化和验证新的DW-MRI采集和建模方法,该方法 通过扩散峰度成像和非高斯水扩散和灌流效应研究非高斯水扩散和灌注效应 高斯体素内非相干运动成像,并提供更具体的组织结构和 生物学。此外,我们将开发和实施先进的图像处理工具,以最大限度地提高生物 来自由成像数据提供的肿瘤/组织的信息。我们及时提出的建议的精髓就在于此 是首个将定量成像生物标记物识别为早期反应的多中心成像试验 治疗指标,根据NCI的中心任务询问肿瘤生物学 定量成像网络。它将解决复发/转移临床试验中未得到满足的迫切需求。 (R/M)头颈部癌症。这项UO1提案是对PAR-14-116的回应,并概述了具体目标 目标1:发展和标准化多b值缩小视场(RFOV)。 用于肿瘤学应用的DW-MRI采集方法和非单指数建模DW-MRI;目标2: 开发和实施具有高级图像分割和图像特征的最佳模型方法 对HN地区R/M恶性肿瘤患者的肿瘤学应用进行分析;目标3:建立 新一代DW-MRI生物标志物在实验性治疗中作为治疗早期反应指标的研究 以R/M HN鳞状细胞癌(SCC)作为原理模型的验证。我们假设这种成像 从新方法得出的指标可以作为早期评估的定量成像生物标记物 R/M HNSCC的治疗效果。识别稳健、可靠和定量成像的原则 来自DW-MRI和图像特征分析的生物标记物保持相似,并且这些成像方案在 适当的适应,可以有更广泛的临床应用,包括用于治疗其他实体肿瘤。
英文摘要
Abstract: We propose to develop, optimize and validate novel DW-MRI acquisition and modeling methods, which address non-Gaussian water diffusion and perfusion effects through diffusion kurtosis imaging and non- Gaussian intravoxel incoherent motion imaging and provide more specific measures of tissue structure and biology. Additionally, we will develop and implement advanced image processing tools to maximize the biologic information from the tumor/tissue provided by the imaging data. The essence of our timely proposal lies in it being the first multi-center, imaging trial to identify quantitative imaging biomarkers as early response to therapy indicators, which interrogate tumor biology in accordance with the central mission of the NCI Quantitative Imaging Network. It will address an urgent, unmet need in clinical trials for recurrent/metastatic (R/M) head and neck cancers. This UO1 proposal is in response to PAR-14-116 and the specific aims outlined in the proposal are as follows: Aim 1: To develop and standardize a multi b-value reduced field of view (rFOV) DW-MRI acquisition method and non-mono exponential modeling DW-MRI for oncology applications; Aim 2: To develop and implement optimal model methodology with advanced image segmentation and image feature analysis in patients with R/M malignancies in the HN region for oncology applications; and Aim 3: To establish the next generation DW-MRI biomarkers as early response to therapy indicators in experimental therapies using R/M HN squamous cell carcinoma (SCC) as a proof of principle model. We hypothesize that imaging metrics derived from newer methods can be used as quantitative imaging biomarkers for assessing early therapeutic efficacy in R/M HNSCC. The principles of identifying robust, reliable and quantitative imaging biomarkers derived from DW-MRI and image feature analysis remain similar and such imaging protocols, after appropriate adaptation, can have a wider clinical application, including their use in treating other solid tumors.
期刊论文(14)
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会议论文
DOI: 10.3390/cancers15092573
发表时间: 2023-04-30
期刊: CANCERS
影响因子: 5.2
作者: [Paudyal, Ramesh, Shah, Akash D., Akin, Oguz, Do, Richard K. G., Konar, Amaresha Shridhar, Hatzoglou, Vaios, Mahmood, Usman, Lee, Nancy, Wong, Richard J., Banerjee, Suchandrima, Shin, Jaemin, Veeraraghavan, Harini, Shukla-Dave, Amita]
通讯作者: Shukla-Dave, Amita
Test-retest repeatability of ADC in prostate using the multi b-Value VERDICT acquisition.
使用多B-Value判决获取的前列腺中ADC的测试重复性重复性。
DOI: 10.1016/j.ejrad.2023.110782
发表时间: 2023-05
期刊: EUROPEAN JOURNAL OF RADIOLOGY
影响因子: 3.3
作者: [Rogers, Harriet J., Singh, Saurabh, Barnes, Anna, Obuchowski, Nancy A., Margolis, Daniel J., Malyarenko, Dariya I., Chenevert, Thomas L., Shukla-Dave, Amita, Boss, Michael A., Punwani, Shonit]
通讯作者: Punwani, Shonit
Quantitative imaging metrics derived from magnetic resonance fingerprinting using ISMRM/NIST MRI system phantom: An international multicenter repeatability and reproducibility study.
使用ISMRM/NIST MRI系统幻象源自磁共振指纹的定量成像指标:国际多中心可重复性和可重复性研究。
DOI: 10.1002/mp.14833
发表时间: 2021-05
期刊: Medical physics
影响因子: 3.8
作者: []
通讯作者:
DOI: 10.3390/cancers14153624
发表时间: 2022-07-26
期刊: CANCERS
影响因子: 5.2
作者: [Konar, Amaresha Shridhar, Paudyal, Ramesh, Shah, Akash Deelip, Fung, Maggie, Banerjee, Suchandrima, Dave, Abhay, Lee, Nancy, Hatzoglou, Vaios, Shukla-Dave, Amita]
通讯作者: Shukla-Dave, Amita
7
    Quantitative imaging tools to derive DW-MRI oncological biomarkers
    Predictive MRI metrics for tumor aggressiveness in micropapillary thyroid cancer
    MR Dynamic Imaging and Spectroscopy in Head & Neck Cancers
    MR Dynamic Imaging and Spectroscopy in Head & Neck Cancers
    海外基金