Machine Learning and Radiomics Techniques for Analysis of Daily MRI in Glioblastoma Patients

项目总结 胶质母细胞瘤是世界范围内最常见的原发性脑癌。新的治疗策略是 由于胶质母细胞瘤几乎是普遍致命的，总体存活率的中位数只有1.5- 2年。胶质母细胞瘤的一个令人沮丧的方面是，大约一半的患者将患有 治疗后的核磁共振显示为肿瘤生长，称为进展。尽管，有一半的患者 随着级数的增加，会出现伪级数，这是一种尚未完全理解的现象 被认为是由免疫系统引起的水肿和炎症，代表着良好的反应 去接受治疗。事实上，假性进展的患者往往比一般的胶质母细胞瘤表现更好。 患者的总体生存时间中位数最长可达3年。另一方面，患有真实疾病的患者 疾病的进展(肿瘤生长和治疗效果差/无反应)往往比 而一般胶质母细胞瘤患者的中位总生存期只有10个月。令人沮丧的 对于临床团队和患者本身来说，部分是真进展和假进展是 在治疗期间，甚至在最初的治疗后成像(1个月)时，彼此无法区分 治疗后)。相反，目前区分真假级数的黄金标准 就是“观察和等待”--继续用连续成像进行监测，看看患者的临床情况是否恶化 或者稳定下来。因此，对可靠和准确地确定肿瘤是否 在治疗过程中发生生长/进展，并预测/确定进展的哪一子类型 (真进展或假进展)病人有。我的实验室专注于对此做出回应 通过各种方法未满足的需求：系列多参数MRI(解剖、灌注、扩散、 光谱等)、定量核磁共振分析、机器学习和分子研究，包括 分析胶质母细胞瘤患者血液样本寻找循环中的肿瘤细胞和其他分子 记号笔。该方案主要研究基于机器学习(AIM)的MRI肿瘤自动检测 1)和分析解剖和生理变化(目标2)从日常多参数磁共振成像到地址 这一问题是通过创造技术来检测治疗过程中肿瘤的增大并预测 真进展和假进展比目前的方法早了几个月。这项提议的目标是发展 识别和监测具有重大解剖和/或生理性肿瘤的患者的工具有很大变化 比目前的方法更早，以便在未来，及时、积极和早期的治疗适应可以 将会得到实施。该项目将直接转化为临床医学实践，并推动 胶质母细胞瘤治疗领域。此外，它将使我在以下方面获得实践技能和专业知识 机器学习、放射组学、核磁共振、神经成像、神经解剖学、放射治疗和肿瘤学，以及 帮助我为自己的职业生涯做准备，成为放射肿瘤学领域的学术内科医生和科学家。

PROJECT SUMMARY Glioblastoma is the most common primary brain cancer worldwide. Novel treatment strategies are urgently needed since glioblastoma is nearly universally fatal with a median overall survival of only 1.5- 2 years. A frustrating aspect of glioblastoma is that approximately half of all patients will have what looks to be tumor growth on their post-treatment MRI, termed progression. Although, half of patients with progression will turn out to have pseudoprogression, which is a not-fully understood phenomenon believed to be edema and inflammation caused by the immune system and represents a good response to treatment. In fact, patients with pseudoprogression tend to do better than the general glioblastoma population and have a median overall survival of up to 3 years. On the other hand, patients with true progression of disease (tumor growth and poor/nonresponse to treatment) tend to do worse than the general glioblastoma population and have a medial overall survival of only 10 months. The frustrating part for clinical team, and the patients themselves, is that true progression and pseudoprogression are not discernable from one another during treatment, or even on initial post-treatment imaging (1-month post-treatment). Instead, the current gold-standard to distinguish between true and pseudoprogression is to “watch and wait” – continue monitoring with serial imaging and see if the patient clinically worsens or stabilizes. Thus, there is an unmet need for techniques that reliably and accurately determine if tumor growth/progression is occurring during treatment and predict/determine which sub-type of progression (true progression or pseudoprogression) a patient has. My laboratory focuses on responding to this unmet need through a variety of methods: serial multiparametric MRI (anatomic, perfusion, diffusion, spectroscopic, etc.), quantitative MRI analysis, machine learning, and molecular research including analyzing blood samples of glioblastoma patients to look for circulating tumor cells and other molecular markers. This proposal focuses on auto-detection of tumors on MRI based on machine learning (Aim 1) and analysis of anatomic and physiologic changes (Aim 2) from daily multiparametric MRI to address this issue by creating techniques that can detect enlarging tumors during treatment and predict between true and pseudoprogression months earlier than current methods. The goal of this proposal is to develop tools that identify and monitor patients with significant anatomic and/or physiologic tumor changes much earlier than current methods, so that in the future, prompt, aggressive, and early therapy adaption can be implemented. This project will translate directly to the practice of clinical medicine and advance the field of glioblastoma treatment. Additionally, it will allow me to gain hands-on skills and expertise in machine learning, radiomics, MRI, neuroimaging, neuro-anatomy, radiation therapy, and oncology, and aid in preparing me for a career as an academic physician scientist in the field of radiation oncology.