Functional and structural connectome features for machine learning chemo-brain prediction in women treated for breast cancer with chemotherapy

Vincent Chin Hung Chen, Tung Yeh Lin, Dah Cherng Yeh, Jyh Wen Chai, Jun Cheng Weng*

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

9 Scopus citations

Abstract

Breast cancer is the leading cancer among women worldwide, and a high number of breast cancer patients are struggling with psychological and cognitive disorders. In this study, we aim to use machine learning models to discriminate between chemo-brain participants and healthy controls (HCs) using connectomes (connectivity matrices) and topological coefficients. Nineteen female post-chemotherapy breast cancer (BC) survivors and 20 female HCs were recruited for this study. Participants in both groups received resting-state functional magnetic resonance imaging (rs-fMRI) and generalized q-sampling imaging (GQI). Logistic regression (LR), decision tree classifier (CART), and xgboost (XGB) were the models we adopted for classification. In connectome analysis, LR achieved an accuracy of 79.49% with the functional connectomes and an accuracy of 71.05% with the structural connectomes. In the topological coefficient analysis, accuracies of 87.18%, 82.05%, and 83.78% were obtained by the functional global efficiency with CART, the functional global efficiency with XGB, and the structural transitivity with CART, respectively. The areas under the curves (AUCs) were 0.93, 0.94, 0.87, 0.88, and 0.84, respectively. Our study showed the discriminating ability of functional connectomes, structural connectomes, and global efficiency. We hope our findings can contribute to an understanding of the chemo brain and the establishment of a clinical system for tracking chemo brain.

Original languageEnglish
Article number851
Pages (from-to)1-13
Number of pages13
JournalBrain Sciences
Volume10
Issue number11
DOIs
StatePublished - 11 2020

Bibliographical note

Publisher Copyright:
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.

Keywords

  • Breast cancer
  • Chemo brain
  • Connectome
  • Generalized q-sampling imaging
  • Machine learning
  • Resting-state functional magnetic resonance imaging

Fingerprint

Dive into the research topics of 'Functional and structural connectome features for machine learning chemo-brain prediction in women treated for breast cancer with chemotherapy'. Together they form a unique fingerprint.

Cite this