Purpose: Cancer-related cognitive impairment (CRCI) is a common neurotoxicity among patients with breast and other cancers. Neuroimaging studies have demonstrated measurable biomarkers of CRCI but have largely neglected the potential heterogeneity of the syndrome.
Methods: We used retrospective functional MRI data from 80 chemotherapy-treated breast cancer survivors to examine neurophysiologic subtypes or "biotypes" of CRCI. The breast cancer group consisted of training (N = 57) and validation (N = 23) samples.
Results: An unsupervised clustering approach using connectomes from the training sample identified three distinct biotypes. Cognitive performance (p < 0.05, corrected) and regional connectome organization (p < 0.001, corrected) differed significantly between the biotypes and also from 103 healthy female controls. We then built a random forest classifier using connectome features to distinguish between the biotypes (accuracy = 91%) and applied this to the validation sample to predict biotype assignment. Cognitive performance (p < 0.05, corrected) and regional connectome organization (p < 0.005, corrected) differed significantly between the predicted biotypes and healthy controls. Biotypes were also characterized by divergent clinical and demographic factors as well as patient reported outcomes.
Conclusions: Neurophysiologic biotypes may help characterize the heterogeneity associated with CRCI in a data-driven manner based on neuroimaging biomarkers.
Implications for cancer survivors: Our novel findings provide a foundation for detecting potential risk and resilience factors that warrant further study. With further investigation, biotypes might be used to personalize assessments of and interventions for CRCI.
Keywords: Breast Cancer; Cognition; Connectome; MRI; Machine Learning.