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• Clinical Article •
Classification of chemotherapy related cognitive impairment in breast cancer based on resting brain activity and functional connectivity features
FENG Yun  GUO Lili  BAO Yiwen  HUANG Wei  BAI Genji 

Cite this article as: FENG Y, GUO L L, BAO Y W, et al. Classification of chemotherapy related cognitive impairment in breast cancer based on resting brain activity and functional connectivity features[J]. Chin J Magn Reson Imaging, 2024, 15(11): 83-89. DOI:10.12015/issn.1674-8034.2024.11.013.


[Abstract] Objective The aim of this study was to investigate the diagnostic value of resting brain activity and functional connectivity characteristics in classifying chemotherapy-related cognitive impairment in breast cancer.Materials and Methods A total of 40 patients with breast cancer (BC) treated with chemotherapy at baseline (P0), 33 survivors assessed one week following treatment (P1), 19 survivors assessed six months after treatment completion (P2), and 44 female volunteers as the healthy control (HC) group were recruited in this study and underwent resting-state functional magnetic resonance imaging (rs-fMRI) examination and neuropsychological test. After data processing by DPARSF and PRoNTo software, 4 types of rs-fMRI measurements, including low-frequency fluctuations (fALFF), regional homogeneity (ReHo), and hippocampal functional connectivity were obtained. Using the PRoNTo 2.1 toolbox, the four data features were used as inputs to the machine learning algorithm, and the binary classification method was used for modeling. Independent sample t test was used for comparison of clinical data indicators at baseline, and single factor ANOVA was used between groupsResults Compared with P0, the BC group showed significantly statistical significance in auditory verbal learning test, self-rating depression scale (SDS) and self-rating anxiety scale at P1 (P<0.05). The fALFF feature gave the highest accuracy in classifing chemotherapy related cognitive impairment among these groups. The specific results demonstrated the highest accuracy of classification was between P0 and HC groups (accuracy 86.90%, P<0.001). Among the group P0, P1, and P2, the classification accuracy between the P0 and P1 groups (accuracy 76.27%, P<0.001) was higher than that of other classifications. In all classifications, the regions showing high feature importance calculated by the decision function within the algorithm largely overlapped with those showing significant differences during the comparisons between fALFF maps in t-tests.Conclusions The machine learning algorithm based on multiple types of rs-fMRI measurements can effectively identify breast cancer patients with chemotherapy-related cognitive impairment and provide imaging reference for early diagnosis.
[Keywords] breast cancer；chemotherapy；cognitive impairment；support vector machine；functional connectivity；magnetic resonance imaging

FENG Yun   GUO Lili   BAO Yiwen   HUANG Wei   BAI Genji*  

Department of Medical Imaging, Huai'an First Hospital Affiliated of Nanjing Medical University, Huai'an223300, China

Corresponding author: BAI G J, E-mail: hybgj0451@163.com

Conflicts of interest   None.

Received  2024-04-26
Accepted  2024-11-05
DOI: 10.12015/issn.1674-8034.2024.11.013
Cite this article as: FENG Y, GUO L L, BAO Y W, et al. Classification of chemotherapy related cognitive impairment in breast cancer based on resting brain activity and functional connectivity features[J]. Chin J Magn Reson Imaging, 2024, 15(11): 83-89. DOI:10.12015/issn.1674-8034.2024.11.013.

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