Automatic Volumetric Treatment Response Assessment and Determination of Regional Genetic Characteristics in Glioblastoma

自动容量治疗反应评估和胶质母细胞瘤区域遗传特征的确定

• 批准号: 10096345
• 负责人: Ken Chang
• 金额: $ 0.51万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-06-01 至 2021-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10096345
• 关键词: Automatic; Volumetric; Treatment; Response; Assessment

Project Summary Glioblastoma (GBM) is the most common primary adult brain tumor with an incidence rate of 3.2 per 100,000 people. Due to its heterogeneous genetic characteristics, GBM carries a dismal prognosis, with a median survival of only 14 months and five-year survival rates are less than 10%. The current standard of care is maximal safe surgical resection, chemoradiation, and adjuvant temozolomide. Within the natural history of GBM, there are adaptive genetic changes within the tumor that lead to treatment resistance and inevitable recurrence, leading to patient death. While a variety of treatments can be administered for tumor recurrence, there is currently no consensus on therapy for recurrent tumor as none have been proven to provide substantial survival benefit. The major limitation of the current treatment strategy is that clinicians do not have a reliable method of longitudinally assessing tumor volumes and regional genetic characteristics of the tumor during the course of treatment. Rather, clinical decision-making is based on a manual and variable two- dimensional measure of tumor burden, a surrogate of tumor volume, and genetic characterization of select molecular markers at the time of initial surgery. A tool that can automatically assess tumor volumes and regional genetic characteristics longitudinally will substantially improve evaluation of treatment efficacy, allowing for an earlier switch to alternative treatment strategies and thus, more personalized tailoring of patient care. Thus, a critical need exists for automatic methods that non-invasively evaluate treatment efficacy on a patient-to-patient basis. To address this problem, we will develop a novel solution based on deep learning that leverages structural, diffusion, and perfusion information from multi-parametric magnetic resonance imaging. At the core of our solution is a convolutional neural network; a machine learning technique that can be trained on raw image data to predict clinical outputs of interest. Firstly, we will develop a fully automatic technique for longitudinal tracking of tumor volumes. To do this, we will develop novel deep learning architectures through incorporation of state-of-the-art neural network components that can segment both whole tumor and tumor subregions (edema, non-enhancing tumor, and gadolinium contrast-enhancing tumor). To prove algorithm utility, we will automatically derive tumor volumes in a longitudinal patient cohort and correlate volumes with clinical outcomes. Secondly, we will develop a non-invasive, deep learning algorithm for evaluation of regional genetic characteristics of GBM. To train this algorithm, we will acquire imaging-localized surgical biopsies and genetic profiling of GBM patients undergoing surgery. Once trained, the algorithm can be used to non- invasively identify clonal populations and track genetic changes associated with clinical outcomes during the course of treatment. The development of these deep learning algorithms will transform physician’s capacity for clinical decision-making and dramatically improve outcomes for a devastating disease.

项目摘要 胶质母细胞瘤是最常见的成人原发脑肿瘤，发病率为3.2/10万 人民。由于其异质性的遗传特征，GBM预后较差，中位数为 仅14个月存活率和5年存活率都不到10%。目前的护理标准是 最安全的手术切除、放化疗和替莫唑胺辅助治疗。在自然历史中 在肿瘤内有适应性的基因改变，导致治疗抵抗和不可避免的 复发，导致病人死亡。虽然可以使用各种治疗方法来治疗肿瘤复发， 目前对于复发肿瘤的治疗还没有达成共识，因为没有一种方法被证明可以提供 可观的生存福利。目前治疗策略的主要限制是临床医生没有 一种可靠的纵向评估肿瘤体积和肿瘤区域遗传特征的方法 在治疗过程中。相反，临床决策是基于手册和可变的两个- 肿瘤负担的维度测量，肿瘤体积的替代，以及SELECT的基因特征 在最初手术时的分子标记。一种可以自动评估肿瘤体积和 从纵向上看，区域遗传特征将显著改善治疗效果的评估， 允许更早地切换到替代治疗策略，从而更个性化地为患者量身定做 关心。因此，迫切需要自动化的方法，非侵入性地评估治疗效果。 以病人对病人为基础。为了解决这个问题，我们将开发一种基于深度学习的新解决方案 利用来自多参数磁共振成像的结构、扩散和灌注信息。 我们解决方案的核心是卷积神经网络；这是一种可以训练的机器学习技术 在原始图像数据上预测感兴趣的临床输出。首先，我们将开发一种全自动技术，用于 肿瘤体积的纵向跟踪。为此，我们将通过以下方式开发新的深度学习体系结构 融合了最先进的神经网络组件，可以分割整个肿瘤和肿瘤 亚区(水肿、无强化肿瘤和增强造影剂肿瘤)。为了证明算法 实用程序，我们将自动得出纵向患者队列中的肿瘤体积，并将体积与 临床结果。其次，我们将开发一种非侵入性的深度学习算法来评估区域 肾小球基底膜的遗传特征。为了训练这个算法，我们将获取成像定位的手术活检和 接受手术的基底细胞瘤患者的基因图谱。一旦训练好，该算法就可以用于非 侵入性地识别克隆种群并跟踪与临床结果相关的基因变化 疗程。这些深度学习算法的发展将改变医生的能力 临床决策，极大地改善了毁灭性疾病的结局。