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Article

Classification and Visualization of Chemotherapy-Induced Cognitive Impairment in Volumetric Convolutional Neural Networks

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Department of Medical Imaging and Radiological Sciences, Bachelor Program in Artificial Intelligence, Chang Gung University, Taoyuan 33302, Taiwan
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School of Medicine, Chang Gung University, Taoyuan 33302, Taiwan
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Department of Psychiatry, Chang Gung Memorial Hospital, Chiayi 61363, Taiwan
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Department of Diagnostic Radiology, Chang Gung Memorial Hospital, Chiayi 61363, Taiwan
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Mood Disorder Psychopharmacology Unit, University Health Network, Department of Psychiatry, University of Toronto, Toronto, ON M5S, Canada
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Institute of Medical Science, University of Toronto, Toronto, ON M5S, Canada
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Departments of Psychiatry and Pharmacology, University of Toronto, Toronto, ON M5S, Canada
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Medical Imaging Research Center, Institute for Radiological Research, Chang Gung University and Chang Gung Memorial Hospital at Linkou, Taoyuan 33302, Taiwan
*
Author to whom correspondence should be addressed.
Contributed equally.
Academic Editor: Pierluigi Maria Rinaldi
J. Pers. Med. 2021, 11(10), 1025; https://doi.org/10.3390/jpm11101025
Received: 2 September 2021 / Revised: 29 September 2021 / Accepted: 11 October 2021 / Published: 14 October 2021
Breast cancer is the most common female cancer worldwide, and breast cancer accounts for 30% of female cancers. Of all the treatment modalities, breast cancer survivors who have undergone chemotherapy might complain about cognitive impairment during and after cancer treatment. This phenomenon, chemo-brain, is used to describe the alterations in cognitive functions after receiving systemic chemotherapy. Few reports detect the chemotherapy-induced cognitive impairment (CICI) by performing functional MRI (fMRI) and a deep learning analysis. In this study, we recruited 55 postchemotherapy breast cancer survivors (C+ group) and 65 healthy controls (HC group) and extracted mean fractional amplitudes of low-frequency fluctuations (mfALFF) from resting-state fMRI as our input feature. Two state-of-the-art deep learning architectures, ResNet-50 and DenseNet-121, were transformed to 3D, embedded with squeeze and excitation (SE) blocks and then trained to differentiate cerebral alterations based on the effect of chemotherapy. An integrated gradient was applied to visualize the pattern that was recognized by our model. The average performance of SE-ResNet-50 models was an accuracy of 80%, precision of 78% and recall of 70%; on the other hand, the SE-DenseNet-121 model reached identical results with an average of 80% accuracy, 86% precision and 80% recall. The regions with the greatest contributions highlighted by the integrated gradients algorithm for differentiating chemo-brain were the frontal, temporal, parietal and occipital lobe. These regions were consistent with other studies and strongly associated with the default mode and dorsal attention networks. We constructed two volumetric state-of-the-art models and visualized the patterns that are critical for identifying chemo-brains from normal brains. We hope that these results will be helpful in clinically tracking chemo-brain in the future. View Full-Text
Keywords: deep learning; chemotherapy-induced cognitive impairment (CICI); residual neural network; densely connected convolutional networks deep learning; chemotherapy-induced cognitive impairment (CICI); residual neural network; densely connected convolutional networks
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MDPI and ACS Style

Lin, K.-Y.; Chen, V.C.-H.; Tsai, Y.-H.; McIntyre, R.S.; Weng, J.-C. Classification and Visualization of Chemotherapy-Induced Cognitive Impairment in Volumetric Convolutional Neural Networks. J. Pers. Med. 2021, 11, 1025. https://doi.org/10.3390/jpm11101025

AMA Style

Lin K-Y, Chen VC-H, Tsai Y-H, McIntyre RS, Weng J-C. Classification and Visualization of Chemotherapy-Induced Cognitive Impairment in Volumetric Convolutional Neural Networks. Journal of Personalized Medicine. 2021; 11(10):1025. https://doi.org/10.3390/jpm11101025

Chicago/Turabian Style

Lin, Kai-Yi, Vincent C.-H. Chen, Yuan-Hsiung Tsai, Roger S. McIntyre, and Jun-Cheng Weng. 2021. "Classification and Visualization of Chemotherapy-Induced Cognitive Impairment in Volumetric Convolutional Neural Networks" Journal of Personalized Medicine 11, no. 10: 1025. https://doi.org/10.3390/jpm11101025

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