Automated imaging analysis tools to guide clinical decision making in brain tumor patients

项目摘要 胶质母细胞瘤(GBM)是一种常见的侵袭性脑癌，新发病例多达20,000例。 美国每年都有病人。标准护理治疗包括立体定向手术切除、放射治疗 治疗和辅助化疗。在最初的治疗之后，患者的监测由标准的磁共振成像来指导。 按常规的时间间隔表演。尽管采取了这些严格的治疗方法，但目前的中位生存期只有15个月。 成像是脑肿瘤治疗的中心部分，但脑肿瘤患者的MRI表现可能是具有挑战性的 由于解释的可变性，这一点更加混乱。对成像的准确解释尤其是 在病人护理的治疗后阶段很重要，因为它可以帮助临床医生主动管理疾病 通过早期、准确地描述真正的肿瘤复发。针对疾病的结构化报告系统 试图通过实施明确定义的成像标准和标准化来减少成像结果的可变性 语言。由埃默里大学开发的脑瘤报告和数据系统(BT-RADS)就是这样一个例子 简化了临床工作流程的框架，并包括量化标准，以更客观地评估 后续影像检查。虽然BT-RADS在临床可接受性方面取得了成功，但它仍然面临着减少障碍 观察者之间的可变性和改进疾病状态的客观分类。 这项拟议的研究解决了一个尚未满足的需求，即对稳健、客观的无偏量化指标的需求 肾小球基底膜病随访影像解读。以前的评估方法使用的是二维的， 对于有代表性的MRI切片，为了评估肿瘤的范围，我们提出了一种深度学习分割方法 工具，精确计算手术切除后的肿瘤体积，并从我们庞大的数据库中进行训练 由放射肿瘤学家手动绘制等高线的患者数据。此外，我们建议发展 用于预测疾病进展的计算先进软件，其基础是临床开发的BT- RADS标准。这样的工具将帮助医生通过治疗后护理脑瘤患者 监测和指导未来的临床决策。此外，我们相信这些量化指标将 在临床试验环境中有无限的潜力，在那里可能很难评估新的疗效 GBM的治疗学。因此，这些辅助工具将在临床试验数据上进行测试，以确定它们是否 优于单独使用传统测量方法。 这项提议的一个基本目标是确保我们开发的量化工具适用于 临床医生在他们的日常生活中。因此，我们将努力与临床医生广泛合作。 这些算法在流畅、直观的软件中供医生使用。通过多学科环境，高 性能计算，以及我们可用的临床资源，我们相信这项建议将在 开发临床辅助工具，在临床环境和环境中对GBM患者进行公正、客观的监测 在研究环境中对新疗法的评估。

Project Summary Glioblastoma (GBM) is a common and aggressive form of brain cancer affecting up to 20,000 new patients in the US every year. Standard of care therapies include stereotactic surgical resection, radiation therapy, and adjuvant chemotherapy. After initial treatment, patient monitoring is guided by standard MR imaging performed at routine intervals. Despite these rigorous therapies, current median survival is only 15 months. Imaging is a central part of brain tumor management, but MRI findings in brain tumor patients can be challenging to interpret and is further confounded by interpretation variability. Accurate interpretation of imaging is particularly important during the post-treatment phase of patient care as it can help clinicians proactively manage disease through early, precise characterization of true tumor recurrence. Disease-specific structured reporting systems attempt to reduce variability in imaging results by implementing well-defined imaging criteria and standardized language. The Brain Tumor Reporting and Data System (BT-RADS), developed at Emory University, is one such framework streamlined for clinical workflows and includes quantitative criteria for more objective evaluation of follow-up imaging. While BT-RADS has had success with clinical adoptability, it still faces hurdles reducing interobserver variability and improving objective classification of disease state. This proposed study addresses an unmet need for unbiased, quantitative metrics for robust, objective interpretation of follow-up imaging for GBM patients. Where previous evaluative methods used two-dimensional, representative MRI slices for evaluating the extent of tumor, we propose to develop a deep learning segmentation tool, accurately calculating volumes of tumor after surgical resection, and trained from our expansive database of patient data with contours manually drawn by radiation oncologists. Further, we propose to develop computationally advanced software for predicting disease progression building upon the clinically developed BT- RADS criteria. Such tools would assist physicians in caring for brain tumor patients through post-treatment surveillance and guide future clinical decision making. In addition, we believe these quantitative metrics would have unbounded potential in clinical trial settings where it may be difficult to evaluate the efficacy of novel therapeutics for GBM. Therefore, these assistive tools will be tested on clinical trial data to determine if they are superior to conventional measurements alone. A fundamental goal of this proposal is ensuring the quantitative tools we develop are applicable for clinicians in their daily lives. Therefore, an effort will be made to collaborate extensively with clinicians to house the algorithms in sleek, intuitive software for physicians to utilize. Through the multidisciplinary environment, high performance computing, and clinical resources we have available, we believe this proposal will be successful in developing clinically assistive tools for unbiased, objective monitoring of GBM patients in clinical settings and evaluation of novel therapies in research settings.