Predicting Long-Term Chemotherapy-Related Cognitive Impairment

预测长期化疗相关的认知障碍

• 批准号: 10402797
• 负责人: SHELLI R KESLER
• 金额: $ 53.53万
• 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/10402797
• 关键词: Address; Adjuvant Chemotherapy; Adverse event; Affect; Algorithms; Attention; Brain; Brain Injuries; Chemotherapy-Oncologic Procedure; Clinical Management; Cognitive; 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; base; chemobrain; chemotherapy; clinical practice; connectome; disability; experience; falls; improved; individual patient; insight; machine learning algorithm; machine learning model; 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.

摘要