Neuroimaging-based brainage prediction in breast cancer after neoadjuvant chemotherapy
Poster No:
878
Submission Type:
Abstract Submission
Authors:
Jing Yang1, Daihong Liu1, Xiaoyu Zhou1, Yu Tang1, Yixin Hu1, Meng Lin2, Jiuquan Zhang2
Institutions:
1Chongqing University Cancer Hospital, Chongqing, chongqing, 2Chongqing University Cancer Hospital, chongqing, chongqing
First Author:
Co-Author(s):
Introduction:
Chemotherapy in cancer patients often results in acute injury to the central nervous system, leading to chemotherapy-related cognitive impairment (Cauli, 2021). Some evidence suggests that this cognitive decline may contribute to gray matter atrophy in the brain, thereby accelerating the aging process (Koppelmans et al., 2012). However, the precise relationship between chemotherapy and brain aging in cancer patients, as well as the trajectory of brain aging across different chemotherapy cycles, remains unclear.
Methods:
This study enrolled 367 female breast cancer (BC) patients who had not undergone neoadjuvant chemotherapy (NAC, TP0), 181 BC patients after the first NAC cycle (TP1), 155 BC patients after completing NAC cycle (TP2), and 154 healthy female controls, covering the adult lifespan. Participants underwent clinical assessments and 3D T1-weighted MRI scans. The MRI data were preprocessed using FreeSurfer to identify various brain region characteristics. Subsequently, we utilized a recent multi-modal cortical parcellation scheme (Glasser et al., 2016) to extract cortical thickness, area and volume for 180 regions of interest per hemisphere. Additionally, we extracted the classic set of cerebellar/subcortical and cortical summary statistics (Fischl et al., 2002). This yielded a total set of 1118 structural brain imaging features (360/360/360/38 for cortical thickness/area/volume as well as cerebellar/subcortical and cortical summary statistics, respectively). Brainage was estimated using these 1118 structural brain regional features through an established brainage prediction model (Kaufmann et al., 2019). Perturbation analysis was employed to assess the impact of input image features on the brainage predictions, and independent component analysis was used to explore the network attribution of these features. Further investigation was carried out to examine the correlation between abnormal brainage and clinical assessments. Figure 1 provides a detailed overview of the methodology.
Results:
At T0, BC patients exhibited no notable difference between their predicted brainage and chronological age. However, significant abnormal brain aging was observed in BC patients during TP1 and TP2, with TP1 showing the most pronounced effects. Specifically, brainage in TP1 was negatively correlated with PCA (Figure 2). At TP1, regional features contributing to an increase in brainage included the volume of the left parietal area, thickness of the left posterior inferior temporal area, volume of the left superior temporal visual area, thickness of the right middle temporal area, and volume of the right hippocampus. Conversely, a decrease in brainage was associated with the thickness of the left middle insula region. At TP2, the regional features causing a positive shift in brainage were the thickness of the left parahippocampal region 2, thickness of the right middle temporal region, and volume of the right caudate nucleus. A negative shift in brainage was observed with the thickness of the left posterior insular area.
Conclusions:
The alterations in brainage observed in BC patients are closely linked to chemotherapy. Notably, the acute damage inflicted by the initial NAC cycle has a more pronounced impact on cognitive function, manifesting as a more accelerated aging of the brain.
Emotion, Motivation and Social Neuroscience:
Social Cognition 2
Lifespan Development:
Aging 1
Modeling and Analysis Methods:
Motion Correction and Preprocessing
Segmentation and Parcellation
Novel Imaging Acquisition Methods:
Anatomical MRI
Keywords:
Aging
Cognition
Data analysis
Development
Psychiatric
1|2Indicates the priority used for review
Abstract Information
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Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was this research conducted in the United States?
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Were any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.
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Provide references using APA citation style.
Fischl, B. (2002). Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron, 33(3), 341-355.
Glasser, M. F. (2016). A multi-modal parcellation of human cerebral cortex. Nature, 536(7615), 171-178.
Kaufmann, T. (2019). Common brain disorders are associated with heritable patterns of apparent aging of the brain. Nat Neurosci, 22(10), 1617-1623.
Koppelmans, V. (2012). Global and focal brain volume in long-term breast cancer survivors exposed to adjuvant chemotherapy. Breast Cancer Res Treat, 132(3), 1099-1106.