Change Detection in Serial MRIs of Patients with Brain Gliomas

脑胶质瘤患者系列 MRI 变化检测

• 批准号: 7196960
• 负责人: BRADLEY J. ERICKSON
• 金额: $ 11.84万
• 依托单位: MAYO CLINIC ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-03-01 至 2009-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7196960
• 关键词: Accounting; Agreement; Algorithms; Brain; Brain Neoplasms; Complex; Computers; Computing Methodologies; Conduct Clinical Trials; Detection; Disease; Disease regression; Effectiveness; FarGo; Future; Glioma; Human; Image; Image Analysis; Invasive; Lesion; Magnetic Resonance Imaging; Measurement; Measures; Methods; Metric; Morbidity - disease rate; Operative Surgical Procedures; Patients; Positioning Attribute; Predictive Value; Purpose; Reader; Side; Signal Transduction; Site; Techniques; Testing; Time; Tumor Tissue; Tumor Volume; base; cost; detector; experience; image processing; image registration; improved; response; tumor; tumor progression

DESCRIPTION (provided by applicant): The purpose of this study is to determine which of several image processing and display methods is the most reliable for assessing progression of brain tumors, based on magnetic resonance imaging (MRI). Accurate assessment of MRI studies is crucial to the efficient conduct of clinical trials because it is one of the few metrics that is non-invasive, and that can be easily conducted prior to, during, and after therapy. The seemingly easy task of image evaluation can be difficult because variance in how the images are acquired (e.g. patient position) and complex signal within the lesion can obscure important findings. Experienced observers often disagree about the presence of, and the meaning of, changes seen on MRI. Using an automated algorithm to determine changes may improve the ability to assess response to therapy. Increased sensitivity can allow earlier determination of the effectiveness of new treatments. Stopping ineffective therapies can reduce patient morbidity and reduce costs. An algorithmic method also permits easier comparison across sites and methods, as the variable of how and who assessed the images are removed or reduced. Conventional measurements of tumors, such as RECIST or Macdonald methods, poorly estimate the total amount of tumor tissue (and therefore, changes in tumor volume), particularly for brain tumors that are frequently irregular, particularly after surgery. Computer volume measurement may more accurately measure tumor volumes, but make important assumptions that may not hold, for instance, that all of a tumor enhances, and only tumor enhances. This may account for the poor predictive value of volume measurement of brain tumors. Computer methods have been developed that may be helpful in reducing the variability of raters, and for increasing sensitivity to change. These include methods for automatically aligning old and new studies. If images are aligned, subtraction may further help to elucidate changes. In this study, we also test a method we have developed which is a multi-parametric change detector. We also will study the effects on how images are presented (side-by-side versus in-place or flicker mode). While this study focuses on brain tumors, we believe the results may be applicable to many other tumor types (and in fact, other diseases). It is our hope that in the future, a measurement method superior to or as an adjunct to human observation will become available, though this study will not attempt to go that far.

描述（由申请人提供）：基于磁共振成像（MRI），本研究的目的是确定几种图像处理和显示方法中的哪种是评估脑肿瘤进展的最可靠性。对MRI研究的准确评估对于有效进行临床试验至关重要，因为它是少数非侵入性的指标之一，并且可以在治疗之前，期间和之后很容易进行。看似简单的图像评估任务可能很困难，因为对图像的获取方式（例如患者位置）和病变中的复杂信号的差异可能会掩盖重要的发现。经验丰富的观察者通常不同意MRI上看到的变化的存在和含义。使用自动化算法来确定变化可以提高评估对治疗反应的能力。提高敏感性可以允许更早确定新疗法的有效性。停止无效疗法可以降低患者的发病率并降低成本。算法方法还允许在站点和方法之间进行更轻松的比较，因为如何删除或减少了如何评估图像的方式和谁评估图像的变量。肿瘤的常规测量，例如再生或麦克唐纳方法，估计肿瘤组织的总量很差（因此，肿瘤体积的变化），特别是对于经常不规则的脑肿瘤，尤其是在手术后。计算机量测量可能更准确地测量肿瘤体积，但要做出重要的假设，例如，所有肿瘤都可以增强，并且只有肿瘤会增强。这可能解释了脑肿瘤体积测量的预测值差。已经开发了计算机方法，这可能有助于降低评估者的变异性，并有助于提高对变化的敏感性。这些包括自动对齐旧研究的方法。如果图像对齐，则减法可能进一步有助于阐明变化。在这项研究中，我们还测试了一种我们开发的方法，该方法是一种多参数变更检测器。我们还将研究对图像的呈现的影响（并排与本地或闪烁模式）。尽管这项研究的重点是脑瘤，但我们认为结果可能适用于许多其他肿瘤类型（实际上是其他疾病）。我们希望将来，一种高于或作为人类观察的辅助方法的测量方法将获得可用，尽管这项研究不会尝试走那么远。