Ogawa, MayukoMayukoOgawaBen, HuiHuiBenOno, YumieYumieOnoWEN-YING LIN2022-11-142022-11-142021-01-0118814379https://scholars.lib.ntu.edu.tw/handle/123456789/625055Pain management is one of the important treatments of cancer patients. This study aims to develop a classifier of chronic cancer pain patients from their brain metabolic activity measured by FDG-PET. We compared FDG-PET brain images of 74 painful and 29 painless cancer patients to clarify the brain activity specific to chronic pain of cancer. Using the detected brain activity pattern, we further developed a classifier that determines the presence or absence of chronic pain from PET brain images by machine learning methods. The painful cancer patients showed significantly increased activity in the amygdala, hippocampus, and decreased activity in the cingulate gyrus and precuneus (p < 0.001, uncorrected). The proposed classifier was able to identify patients with chronic pain with a sensitivity and specificity of 85.1% and 62.1%, respectively. Further research is required to improve the specificity by selecting better regions of interest and classification algorithms.cancer pain | evaluation | functional brain image | machine learning | PET[SDGs]SDG3journal article10.11239/jsmbe.Annual59.7802-s2.0-85135127711https://api.elsevier.com/content/abstract/scopus_id/85135127711