Predicting Long-Term Chemotherapy-Related Cognitive Impairment

预测长期化疗相关的认知障碍

• 批准号: 10617793
• 负责人: SHELLI R KESLER
• 金额: $ 50.05万
• 依托单位: UNIVERSITY OF TEXAS AT AUSTIN
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10617793
• 关键词: Address; Adjuvant Chemotherapy; Adverse event; Affect; Algorithms; Attention; Brain; Brain Injuries; Breast Cancer therapy; Chemotherapy-Oncologic Procedure; Clinical Management; Cognitive Therapy; Cognitive deficits; Data; Decision Making; Diffuse; Diffusion Magnetic Resonance Imaging; Disease; Enrollment; Female; Functional Magnetic Resonance Imaging; General Anesthesia; Goals; Home; Impaired cognition; Impairment; Incidence; Injury; Life; Machine Learning; Magnetic Resonance Imaging; Malignant Neoplasms; Measurement; Measures; Medical; Memory; Methods; Modeling; Motor; Newly Diagnosed; Occupational; Oncologist; Operative Surgical Procedures; Outcome; Patients; Pattern; Property; Public Health; Quality of life; Research; Risk; Role; Sampling; Schedule; Sensory; Structure; Syndrome; Testing; Thinking; Time; Treatment Protocols; Validation; Woman; Work; cancer therapy; chemobrain; chemotherapy; clinical practice; connectome; disability; experience; falls; improved; individual patient; insight; machine learning model; machine learning prediction algorithm; malignant breast neoplasm; multimodal neuroimaging; neuroimaging; neuromechanism; novel; outcome prediction; prediction algorithm; predictive modeling; social

ABSTRACT Chemotherapy-related cognitive impairment (CRCI) affects an estimated 60% of patients, negatively impacting quality of life. Currently, there is no established method for predicting which patients will develop CRCI. This information could be practice-changing by assisting clinicians with treatment decision-making for individual patients. We have shown that the brain network (“connectome”) is significantly altered in patients with CRCI. Therefore, we measured the connectome is patients prior to any treatment and demonstrated that these connectome properties could be used in combination with machine learning to predict 1 year post-chemotherapy cognitive impairment with 100% accuracy. The proposed project aims to test this preliminary prediction model in a new, larger sample with the overarching goal of validating its use for clinical practice. We will enroll 100 newly diagnosed patients with primary breast cancer scheduled for adjuvant chemotherapy who will be assessed prior to any treatment, including surgery with general anesthesia, 1 month after chemotherapy treatment and again 1 year later. We will also enroll matched healthy female controls who will be assessed at yoked intervals. We will combine these data with retrospective data we obtained during a prior study for a total sample of 150 in each group. Data from healthy controls will be used to determine impairment status in patients with breast cancer and to provide a template of typical connectome organization for comparison. We hypothesize that our machine learning model will accurately predict 1 year post- chemotherapy cognitive impairment and that it will be more accurate than a model that includes patient- related and medical variables alone. We will also examine longitudinal changes in connectome organization associated with impairment subtypes (i.e. persistent vs. late onset impairment) as well as changes in specific functional networks (e.g. default mode, salience, executive-attention and sensory- motor networks). This information will provide novel insights regarding the neural mechanisms of CRCI and may also help us refine our prediction models.

抽象的 化学疗法相关的认知障碍（CRCI）影响约60％的患者，负面影响 影响生活质量。目前，尚无既定方法来预测哪些患者将 开发CRCI。通过协助临床医生进行治疗，这些信息可能会改变 针对个别患者的决策。我们已经证明了大脑网络（“ Connectome”）是 CRCI患者的明显改变。因此，我们测量了连接组是患者 任何处理并证明这些连接特性可以与 机器学习以100％精度预测化学后认知障碍1年。这 拟议的项目旨在在一个新的，更大的样本中测试此初步预测模型 验证其用于临床实践的总体目标。我们将注册100名新诊断的患者 原发性乳腺癌计划进行调整化疗，他们将在任何之前进行评估 治疗，包括全身麻醉的手术，在化学疗法治疗后1个月，再次 1年后。我们还将注册匹配的健康女性对照组，他们将在Yoked中进行评估 间隔。我们将将这些数据与我们在先前研究中获得的回顾性数据相结合 每组的总样本为150。来自健康控制的数据将用于确定损害 乳腺癌患者的状态，并为典型的Connectome组织提供模板 比较。我们假设我们的机器学习模型将在1年后准确预测 化学疗法的认知障碍，并且比包括患者的模型更准确 仅相关和医疗变量。我们还将检查Connectome的纵向变化 与损伤子类型相关的组织（即持续性与晚发性损害）以及 特定功能网络的变化（例如默认模式，显着性，执行注意力和感觉 - 电机网络）。这些信息将提供有关CRCI神经机制的新见解 并且也可以帮助我们完善我们的预测模型。