Using Connectomics and Machine Learning to Predict Survival in Diffuse Glioma

使用连接组学和机器学习来预测弥漫性胶质瘤的生存率

• 批准号: 10289350
• 负责人: SHELLI R KESLER
• 金额: $ 9.31万
• 依托单位: UNIVERSITY OF TEXAS AT AUSTIN
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-21 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10289350
• 关键词: Age; Algorithms; Biological; Biological Markers; Brain; Brain scan; Characteristics; Clinical; Consumption; Counseling; Data Set; Decision Making; Diffuse; Disease; Disease Progression; Environment; Evaluation; Excision; Genetic Markers; Genotype; Glioblastoma; Glioma; Histologic; Histology; Histopathologic Grade; Image; Intervention; Isocitrate Dehydrogenase; Life; MGMT gene; MRI Scans; Machine Learning; Magnetic Resonance Imaging; Malignant - descriptor; Manuals; Measures; Medical; Methods; Methylation; Modeling; Molecular; Nervous System Physiology; Neurocognitive; Neurologic; Oligodendroglial Neoplasm; Operative Surgical Procedures; Outcome; Patient-Focused Outcomes; Patients; Performance; Phenotype; Population; Primary Brain Neoplasms; Process; Prognosis; Prognostic Factor; Prognostic Marker; Property; Radiation; Research; Rest; Sample Size; Sampling; Structure; Therapeutic; Time; Validation; Variant; base; brain tissue; chemotherapy; clinical practice; connectome; improved; individual patient; individualized medicine; insight; molecular pathology; neoplastic; neuroimaging; outcome prediction; personalized management; prediction algorithm; predictive modeling; prognostic; promoter; radiomics; risk stratification; standard of care; survival prediction; tumor

ABSTRACT Diffuse gliomas are the most common malignant primary brain tumors. Clinical outcomes, including overall survival, vary significantly across individual patients and are not adequately explained by known prognostic factors such as age, histologic and molecular pathology, and imparted treatments. A deeper understanding of prognostic influences in diffuse glioma can facilitate therapeutic decision making and patient counselling and lend further insight into the biologic underpinnings of the disease. Magnetic resonance imaging (MRI) scans of the brain are part of standard pre-operative evaluation in these patients. As brain structure and function are modulated by biologic and environment factors, MRI-derived metrics often provide sensitive prognostic biomarkers. We have preliminarily shown that connectomics, a method of measuring brain connectivity from MRI, can accurately predict survival in patients with diffuse glioma. We will retrospectively obtain at least 1,150 datasets and attempt to validate our preliminary models (Aim 1). We will also examine connectome phenotypes associated with tumor genotypes to help refine and improve our models (Aim 2). Accurate pre- operative prediction of outcome may ultimately allow for better tailored interventions for the individual patient and assist clinicians in optimizing both tumor control and neurologic function in treatment decision making.

摘要 弥漫性胶质瘤是最常见的恶性原发脑肿瘤。临床结果，包括总体 存活率，在不同患者之间差异很大，并且不能用已知的预后来充分解释 年龄、组织学和分子病理学等因素，以及给予治疗。更深入地了解 弥漫性胶质瘤的预后影响可以促进治疗决策和患者咨询 进一步深入了解这种疾病的生物学基础。磁共振成像(MRI)扫描 脑部是这些患者标准的术前评估的一部分。因为大脑的结构和功能 在生物和环境因素的调节下，MRI衍生的指标通常能提供灵敏的预后 生物标志物。我们已经初步证明，连接学是一种测量大脑连通性的方法 MRI，可以准确预测弥漫性胶质瘤患者的生存期。我们将回溯至少获得 1,150个数据集，并试图验证我们的初步模型(目标1)。我们还将检查连接体 与肿瘤基因类型相关的表型，以帮助改进和改进我们的模型(目标2)。准确的预置 手术结果的预测最终可能会为个别患者提供更好的量身定制的干预措施 并协助临床医生在治疗决策中优化肿瘤控制和神经功能。