Functional connectome biotypes of chemotherapy-related cognitive impairment

Shelli R. Kesler, Melissa L. Petersen, Vikram Rao, Rebecca A. Harrison, Oxana Palesh

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)483-493
Number of pages11
JournalJournal of Cancer Survivorship
Volume14
Issue number4
DOIs
StatePublished - Aug 1 2020

Keywords

  • Breast Cancer
  • Cognition
  • Connectome
  • MRI
  • Machine Learning

ASJC Scopus subject areas

  • Oncology
  • Oncology(nursing)

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