Predicting Long-Term Chemotherapy-Related Cognitive Impairment

摘要 化疗相关的认知障碍(CRCI)对大约60%的患者有负面影响 影响生活质量。目前，还没有确定的方法来预测哪些患者会 发展CRCI。这些信息可以通过帮助临床医生进行治疗来改变做法。 针对个别患者的决策。我们已经证明大脑网络(“连接体”)是 CRCI患者的上述指标有明显改变。因此，我们测量了患者之前的连接体 任何治疗方法，并证明这些连接体特性可与 机器学习预测化疗后1年认知功能障碍的准确率为100%。这个 拟议的项目旨在用一个新的、更大的样本来测试这个初步预测模型 首要目标是验证其在临床实践中的使用。我们将招收100名新诊断的患者 对于计划进行辅助化疗的原发性乳腺癌，谁将在任何 治疗，包括在全身麻醉下手术，化疗后1个月再进行 1年后。我们还将招募匹配的健康女性对照组，她们将在Yoked接受评估 间隔时间。我们将把这些数据与我们在之前的研究中获得的回溯性数据结合起来 每组共150个样本。来自健康对照的数据将被用来确定损害 乳腺癌患者的状况，并提供典型的连接体组织的模板 比较一下。我们假设我们的机器学习模型将准确地预测1年后- 化疗认知障碍，它将比包括患者的模型更准确- 仅与相关和医学变量有关。我们还将研究连接体的纵向变化。 与损害亚型相关的组织(即持续性损害与迟发性损害)以及 特定功能网络的变化(例如，默认模式、突显、执行注意和感觉- 汽车网络)。这一信息将为CRCI的神经机制提供新的见解 也可能帮助我们改进我们的预测模型。

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.