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*

*此作品的通信作者

研究成果: 期刊稿件文章同行評審

9 引文 斯高帕斯(Scopus)

摘要

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.

原文英語
文章編號851
頁(從 - 到)1-13
頁數13
期刊Brain Sciences
10
發行號11
DOIs
出版狀態已出版 - 11 2020

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

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