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

用于分析胶质母细胞瘤患者日常 MRI 的机器学习和放射组学技术

• 批准号: 10751672
• 负责人: Kaylie Cullison
• 金额: $ 5.27万
• 依托单位: UNIVERSITY OF MIAMI SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10751672
• 关键词: 3-Dimensional; Address; Aftercare; Age; Anatomy; Biopsy; Blood specimen; Brain imaging; Cessation of life; Chemotherapy and/or radiation; Clinical; Clinical Medicine; Data; Data Set; Detection; Diffusion; Disease; Disease Progression; Early treatment; Edema; Evolution; Excision; Frustration; Future; Glioblastoma; Goals; Habitats; Image; Immune system; Inflammation; Intervention; Laboratories; Machine Learning; Magnetic Resonance Imaging; Malignant neoplasm of brain; Manuals; Maps; Medial; Methods; Modification; Molecular; Monitor; Necrosis; Neoplasm Circulating Cells; Neuroanatomy; Oncology; Operative Surgical Procedures; Patient Monitoring; Patient-Focused Outcomes; Patients; Performance; Performance Status; Perfusion; Physicians; Physiological; Population; Process; Prognosis; Protocols documentation; Radiation Oncology; Radiation therapy; Research; Resolution; Scanning; Scientist; Statistical Data Interpretation; Surgically-Created Resection Cavity; System; Techniques; Texture; Training; Translating; Treatment outcome; Tumor Volume; aggressive therapy; anatomic imaging; automated segmentation; brain volume; career; chemotherapy; clinical practice; convolutional neural network; deep learning; disability; feature selection; image processing; imaging biomarker; improved; improved outcome; learning network; molecular marker; neovascularization; neuroimaging; novel; novel strategies; predictive modeling; prognostic; quantitative imaging; radiomics; risk stratification; segmentation algorithm; serial imaging; skills; standard care; tool; treatment response; treatment strategy; tumor; tumor growth

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.

项目摘要 胶质母细胞瘤是全球最常见的原发性脑癌。新颖的治疗策略是 由于胶质母细胞瘤几乎普遍致命，因此急需急需的总体生存率仅为1.5-- 2年。胶质母细胞瘤的一个令人沮丧的方面是，大约一半的患者将拥有 在治疗后MRI上看起来是肿瘤的生长，称为进展。虽然，一半的患者 随着进步的结果，将有伪雌性，这是一种不明白的现象 被认为是由免疫系统引起的水肿和炎症，代表了良好的反应 治疗。实际上，伪雌性患者的表现往往比一般的胶质母细胞瘤更好 人口，总体生存期长达3年。另一方面，具有真实的患者 疾病的进展（肿瘤生长和对治疗的不良反应）往往比 一般胶质母细胞瘤人群，培养基的总生存率仅为10个月。令人沮丧的 临床团队和患者本身的一部分是，真正的进展和伪雌性是 在治疗期间，甚至是在初始治疗后成像（1个月）时，彼此之间无法识别 治疗后）。相反，当前的金色标准是区分真实和伪剖面 是“观看和等待” - 继续使用串行成像进行监视，看看患者是否在临床上恶化 或稳定。那是对技术是否需要可靠，准确地确定肿瘤的需求 在治疗过程中发生生长/进展 （真正的进展或伪雌性）患者具有。我的实验室专注于对此做出回应 通过多种方法未满足的需求：串行多参数MRI（解剖，灌注，扩散， 光谱镜等），定量MRI分析，机器学习和分子研究，包括 分析胶质母细胞瘤患者的血液样本，以寻找循环的肿瘤细胞和其他分子 标记。该建议重点是基于机器学习的MRI自动检测（目标 1）和分析解剖学和生理变化（AIM 2）从每日多参数MRI解决 通过创建可以在治疗过程中检测到增加肿瘤并预测的技术来进行此问题 比当前方法早几个月的真实和伪雌性。该提议的目的是发展 识别和监测具有明显解剖和/或生理肿瘤的患者的工具发生了很大变化 比目前的方法早，因此，将来，及时，侵略性和早期治疗适应能够 实施。该项目将直接转化为临床医学实践，并提高 胶质母细胞瘤治疗领域。此外，这将使我能够获得动手技能和专业知识 机器学习，放射素学，MRI，神经影像学，神经解剖学，放射治疗和肿瘤学以及 帮助我准备在辐射肿瘤学领域担任学术物理科学家的职业。