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

Kai-Yi Lin; Vincent Chin-Hung Chen; Yuan-Hsiung Tsai; Roger S McIntyre; Jun-Cheng Weng

doi:10.3390/jpm11101025

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

J Pers Med. 2021 Oct 14;11(10):1025. doi: 10.3390/jpm11101025.

Authors

Kai-Yi Lin¹, Vincent Chin-Hung Chen^{2

3}, Yuan-Hsiung Tsai^{2

4}, Roger S McIntyre^{5

6

7}, Jun-Cheng Weng^{1

3

8}

Affiliations

¹ Department of Medical Imaging and Radiological Sciences, Bachelor Program in Artificial Intelligence, Chang Gung University, Taoyuan 33302, Taiwan.
² School of Medicine, Chang Gung University, Taoyuan 33302, Taiwan.
³ Department of Psychiatry, Chang Gung Memorial Hospital, Chiayi 61363, Taiwan.
⁴ Department of Diagnostic Radiology, Chang Gung Memorial Hospital, Chiayi 61363, Taiwan.
⁵ Mood Disorder Psychopharmacology Unit, University Health Network, Department of Psychiatry, University of Toronto, Toronto, ON M5S, Canada.
⁶ Institute of Medical Science, University of Toronto, Toronto, ON M5S, Canada.
⁷ Departments of Psychiatry and Pharmacology, University of Toronto, Toronto, ON M5S, Canada.
⁸ Medical Imaging Research Center, Institute for Radiological Research, Chang Gung University and Chang Gung Memorial Hospital at Linkou, Taoyuan 33302, Taiwan.

Abstract

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.

Keywords: chemotherapy-induced cognitive impairment (CICI); deep learning; densely connected convolutional networks; residual neural network.

Abstract

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