Towards the automation of MR spectroscopic imaging in patients with glioblashoma

胶质母细胞瘤患者磁共振波谱成像的自动化

• 批准号: 9926827
• 负责人: Saumya Gurbani
• 金额: $ 4.78万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-06-24 至 2021-05-15
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9926827
• 关键词: Address; Adoption; Adult; Algorithmic Software; Algorithms; Anatomy; Automation; Benchmarking; Brain; Brain Neoplasms; Cells; Clinical; Clinical Data; Clinical Management; Clinical Trials; Complement; Computational Technique; Computer software; Consensus; Contrast Media; Data; Data Analyses; Data Set; Databases; Detection; Diagnosis; Diagnostic; Diffuse; Diffusion; Digital Imaging and Communications in Medicine; Disease; Effectiveness; Enrollment; Excision; Gaussian model; Glioblastoma; Glioma; Goals; Gold; High Performance Computing; Histologic; Histology; Hour; Image; Infiltration; Intervention; Knowledge; Least-Squares Analysis; Machine Learning; Magnetic Resonance Imaging; Magnetic Resonance Spectroscopy; Malignant Neoplasms; Malignant neoplasm of brain; Maps; Measures; Medical; Medical Imaging; Metabolic; Metabolism; Methods; Modality; Modeling; Monitor; Morphologic artifacts; Nature; Neoplasms; Oncologist; Online Systems; Outcome; Pathologic; Patient imaging; Patients; Performance; Physicians; Play; Population; Primary Brain Neoplasms; Radiation; Radiation Dosage; Radiation therapy; Reader; Recurrence; Role; Scheme; Seeds; Signal Transduction; Software Framework; T2 weighted imaging; Techniques; Time; Tissues; Training; Translating; Universities; Variant; Work; analysis pipeline; automated segmentation; base; chemotherapy; clinical application; clinical imaging; contrast enhanced; deep learning; dosage; expectation; genomic data; image processing; imaging modality; improved; learning classifier; learning network; magnetic resonance spectroscopic imaging; metabolic imaging; molecular imaging; multimodal data; neoplastic; neoplastic cell; neovascularization; neovasculature; novel; outcome forecast; personalized diagnostics; personalized medicine; precision medicine; predictive modeling; prognostic; quantitative imaging; response; software development; spectroscopic data; spectroscopic imaging; success; syntax; tool; treatment planning; tumor; tumor growth; white matter

Glioblastoma is the most common adult primary brain tumor and is highly aggressive in its disease course. Despite advances in neurosurgical resection, radiation targeting, and chemotherapy, the prognosis remains grim with a median survival of just 15 months. The effectiveness of current radiation therapy strategies is severely limited by shortcomings in the imaging modalities used to develop treatment plans. Current radiation therapy planning is mainly based on contrast-enhanced T1-weighted MRI, which identifies high grade tumors that are immediately associated with leaky neovasculature. Although it is an excellent diagnostic tool to identify high grade from low grade tumors, it is unable to signal occult infiltration beyond the core of the tumor. Though many believe GBM to be an incurable disease, we believe we have identified a method for optimizing tumor targeting that will increase the effectiveness of radiation therapy. A significant component of the current problem in GBM therapy is the lack of treatment for non-enhancing regions that are significantly infiltrated by neoplastic glioma cells without neovascularization. This untreated population undoubtedly leads to early recurrence. The proposed study addresses an important step toward translating an advanced quantitative imaging modality that complements the conventional imaging that is capable of reliably revealing glioma- infiltrated regions for precise, personalized treatment targeting. Proton spectroscopic magnetic resonance imaging (sMRI) is an alternative modality able to identify endogenous metabolism within tissue without the need for exogenous contrast, and has been shown to identify the metabolic abnormalities associated with tumor beyond the regions identified by T1-weighted MRI. The clinical integration of sMRI in patient management has been limited due to the computational challenges of analysis of sMRI data. Two key hurdles to be overcome are the insufficiency of filters to remove image artifacts and the necessity of quantification of metabolic levels relative to a patient's baseline metabolism. As a result, sMRI processing requires skilled user intervention and many hours of computational and user time. To automate this pipeline and provide clinically useful information to oncologists, we seek to develop a software framework for the automated and expedient processing of sMRI for use in radiation therapy planning. We will use novel advances in the fields of high performance computing and deep learning, an approach to computation that has shattered benchmarks in many medical and non-medical problems. Specifically, we will develop filters for removing artifacts, algorithms for personalized diagnosis of tumor infiltration, and explore deep learning as a method to synthesize sMRI data with anatomical and clinical metrics in a fully automated fashion. Success in the proposed work will produce a “scanner-to-clinician” platform for quantitative, expedient, and objective analysis methods to integrate sMRI into the clinical radiation therapy planning paradigm. Ultimately, we believe this additional modality in the physician's tool belt will lead to better outcomes in patients suffering from this debilitating disease.

胶质母细胞瘤是最常见的成人原发性脑肿瘤，其病程具有高度侵袭性。 尽管在神经外科切除、放射靶向治疗和化疗方面取得了进展， 生存期中位数只有15个月。目前放射治疗策略的有效性是 严重受限于用于制定治疗计划的成像模式的缺点。当前辐射 治疗计划主要基于对比增强的T1加权MRI，其识别高级别肿瘤 与新生血管渗漏直接相关。虽然它是一个很好的诊断工具， 高级别肿瘤与低级别肿瘤的区别，不能显示肿瘤核心以外的隐匿性浸润。虽然 许多人认为GBM是一种不治之症，我们相信我们已经找到了一种优化肿瘤生长的方法。 这将增加放射治疗的有效性。电流的重要组成部分 GBM治疗的问题是缺乏对非增强区域的治疗， 肿瘤性胶质瘤细胞，无新血管形成。这种未经治疗的人群无疑会导致早期 复发拟议的研究解决了一个重要的一步，翻译一个先进的定量 补充能够可靠地揭示胶质瘤的常规成像的成像方式， 用于精确的个性化治疗靶向。质子波谱磁共振 磁共振成像（sMRI）是一种能够识别组织内内源性代谢的替代方式， 需要外源性造影剂，并已被证明可以识别与以下疾病相关的代谢异常： 肿瘤超出T1加权MRI确定的区域。sMRI在患者中的临床整合 由于sMRI数据分析的计算挑战，管理受到限制。两个关键障碍 要克服的是滤波器去除图像伪影的不足和量化 代谢水平相对于患者的基线代谢。因此，sMRI处理需要熟练的用户 干预和许多小时的计算和用户时间。为了使这条管道自动化， 有用的信息，肿瘤学家，我们寻求开发一个软件框架的自动化和权宜之计 用于放射治疗计划的sMRI处理。我们将利用高新技术领域的新进展， 性能计算和深度学习，这是一种计算方法， 许多医疗和非医疗问题。具体来说，我们将开发用于去除伪影的过滤器、算法 用于肿瘤浸润的个性化诊断，并探索深度学习作为合成sMRI数据的方法 以全自动的方式与解剖学和临床指标相结合。拟议工作的成功将产生 “扫描仪到临床医生”平台，用于整合sMRI的定量、便捷和客观分析方法 临床放射治疗计划范例。最终，我们认为， 医生的工具带将导致更好的结果，患者患有这种衰弱的疾病。