Brain connectomics improve prediction of 1-year decreased quality of life in breast cancer: A multi-voxel pattern analysis

Abstract

PurposeWhether brain connectomics can predict 1-year decreased Quality of Life (QoL) in patients with breast cancer are unclear. A longitudinal study was utilized to explore their prediction abilities with a multi-center sample. Methods232 breast cancer patients were consecutively enrolled and 214 completed the 1-year QoL assessment (92.2%). Resting state functional magnetic resonance imaging was collected before the treatment and a multivoxel pattern analysis (MVPA) was performed to differentiate whole-brain resting-state connectivity patterns. Net Reclassification Improvement (NRI) as well as Integrated Discrimination Improvement (IDI) were calculated to estimate the incremental value of brain connectomics over conventional risk factors. ResultsParacingulate Gyrus, Superior Frontal Gyrus and Frontal Pole were three significant brain areas. Brain connectomics yielded 7.8–17.2% of AUC improvement in predicting 1-year decreased QoL. The NRI and IDI ranged from 20.27 to 54.05%, 13.21–33.34% respectively. ConclusionBrain connectomics contribute to a more accurate prediction of 1-year decreased QoL in breast cancer. Significant brain areas in the prefrontal lobe could be used as potential intervention targets (i.e., Cognitive Behavioral Group Therapy) to improve long-term QoL outcomes in breast cancer.