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 胶质母细胞瘤是世界上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普遍致命，中位总生存期仅为1.5- 2年胶质母细胞瘤的一个令人沮丧的方面是，大约一半的患者将有什么 在治疗后的MRI上看起来是肿瘤生长，称为进展。虽然，一半的病人 将有伪进展，这是一个不完全理解的现象 据信是由免疫系统引起的水肿和炎症， 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更好 人群，中位总生存期长达3年。另一方面，真正的患者 疾病进展（肿瘤生长和对治疗反应差/无反应）往往比 一般的胶质母细胞瘤人群，中位总生存期仅为10个月。令人沮丧的 对于临床团队和患者本身来说，真正的进展和假进展是 在治疗期间或甚至在最初的治疗后成像（1个月）上彼此不可辨别 后处理）。相反，目前区分真进展和假进展的黄金标准 是“观察和等待”-继续监测与系列成像，看看病人临床上是否 或稳定下来。因此，对于可靠且准确地确定肿瘤是否存在的技术存在未满足的需求。 在治疗期间发生生长/进展，并预测/确定哪种亚型的进展 (true进展或假进展）。我的实验室致力于对此做出回应 通过各种方法满足未满足的需求：系列多参数MRI（解剖，灌注，弥散， 光谱等），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肿瘤细胞和其他分子 标记。该提案侧重于基于机器学习的MRI肿瘤自动检测（Aim 1)并分析每日多参数MRI的解剖和生理变化（目的2），以解决 这个问题通过创造技术，可以检测在治疗过程中扩大的肿瘤，并预测之间 真和假进展比目前的方法早几个月。该提案的目标是发展 识别和监测具有显著解剖和/或生理肿瘤变化的患者的工具 比目前的方法更早，以便在未来，迅速，积极和早期的治疗适应， 实行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，它将使我获得实践技能和专业知识， 机器学习、放射组学、MRI、神经成像、神经解剖学、放射治疗和肿瘤学，以及 帮助我准备在放射肿瘤学领域作为一名学术医生科学家的职业生涯。