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

项目摘要 胶质母细胞瘤（GBM）是最常见的原发性成人脑肿瘤，发病率为3.2/10万 人由于其异质性的遗传特征，GBM具有令人沮丧的预后，中位数为2.5%。 生存期只有14个月，5年生存率不到10%。目前的护理标准是 最安全的手术切除、放化疗和辅助替莫唑胺。在自然历史中， GBM，肿瘤内存在适应性遗传变化，导致治疗耐药性， 复发，导致患者死亡。虽然可以施用多种治疗来治疗肿瘤复发， 目前对复发性肿瘤的治疗没有共识， 巨大的生存效益。目前治疗策略的主要局限性是临床医生没有 纵向评估肿瘤体积和肿瘤区域遗传特征的可靠方法 在治疗过程中。相反，临床决策是基于手动和可变的两个- 肿瘤负荷的维度测量，肿瘤体积的替代物，以及选择性肿瘤的遗传表征。 在初次手术时进行分子标记。一种可以自动评估肿瘤体积并 纵向的区域遗传特征将显著改善治疗效果的评价， 从而允许更早地转换到替代治疗策略，并且因此允许更个性化地定制患者 在乎因此，迫切需要自动化方法，其非侵入性地评估对患者的治疗功效。 病人对病人的基础上。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。 以预测感兴趣的临床输出。首先，我们将开发一种全自动技术， 肿瘤体积的纵向跟踪。为此，我们将开发新的深度学习架构， 结合最先进的神经网络组件，可以分割整个肿瘤和肿瘤 亚区域（水肿、非增强肿瘤和钆对比增强肿瘤）。证明算法 实用性，我们将自动推导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，用于评估区域 GBM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， 接受手术的GBM患者的基因图谱。一旦经过训练，该算法可以用于非 侵入性地识别克隆群体，并跟踪与临床结果相关的遗传变化， 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，并显着改善结果的毁灭性疾病。

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.