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

作者全名："Liang, Mu Zi; Tang, Ying; Chen, Peng; Tang, Xiao Na; Knobf, M. Tish; Hu, Guang Yun; Sun, Zhe; Liu, Mei Ling; Yu, Yuan Liang; Ye, Zeng Jie"

作者地址："[Liang, Mu Zi] Guangdong Acad Populat Dev, Guangzhou, Peoples R China; [Tang, Ying] Guangzhou Univ Chinese Med, Inst Tumor, Guangzhou, Peoples R China; [Chen, Peng] Guizhou Univ Tradit Chinese Med, Sch Basic Med, Guiyang, Peoples R China; [Tang, Xiao Na] Guangzhou Univ Chinese Med, Shenzhen Baoan Tradit Chinese Med Hosp, Guangzhou, Peoples R China; [Knobf, M. Tish] Yale Univ, Sch Nursing, Orange, CT USA; [Hu, Guang Yun] Army Med Univ, Chongqing, Peoples R China; [Sun, Zhe] Guangzhou Univ Chinese Med, Affiliated Hosp 1, Guangzhou, Peoples R China; [Liu, Mei Ling] Sun Yat Sen Univ, Collaborat Innovat Ctr Canc Med, State Key Lab Oncol South China, Canc Ctr, Guangzhou, Peoples R China; [Yu, Yuan Liang] South China Univ Technol, Guangzhou, Peoples R China; [Ye, Zeng Jie] Guangzhou Med Univ, Sch Nursing, Guangzhou, Guangdong, Peoples R China"

通信作者："Ye, ZJ (通讯作者)，Guangzhou Med Univ, Sch Nursing, Guangzhou, Guangdong, Peoples R China."

来源：EUROPEAN JOURNAL OF ONCOLOGY NURSING

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:001159185900001

JCR分区：Q1

影响因子：2.7

年份：2024

卷号：68

期号：　

开始页：　

结束页：　

文献类型：Article

关键词：Brain connectomics; rs-fMRI; Multivoxel pattern analysis; Prediction; Quality of life; Breast cancer; Be resilient to breast cancer

摘要："Purpose Whether 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. Methods 232 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. Results Paracingulate 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. Conclusion Brain 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."

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