||5R01CA226080-02 Interpret this number
||University Of Texas, Austin
||Predicting Long-Term Chemotherapy-Related Cognitive Impairment
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.
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