Jae Myeong Kang1, Jun-Young Lee2,3, Yu Kyeong Kim4,5, Bo Kyung Sohn6, Min Soo Byun7,8, Ji Eun Choi9, Soo Kyung Son9, Hyung-Jun Im10, Jae-Hoon Lee11, Young Hoon Ryu11, Dong Young Lee7,8. 1. Department of Psychiatry, Gil Medical Center, Gachon University College of Medicine, Incheon, 21565, Republic of Korea. 2. Department of Psychiatry, Seoul National University College of Medicine, Seoul, 03080, Republic of Korea. benji@snu.ac.kr. 3. Department of Psychiatry and Behavioral Science, SMG-SNU Boramae Medical Center, Boramae-Ro 5-Gil, Shindaebang-dong, Dongjak-gu, Seoul, 07061, Republic of Korea. benji@snu.ac.kr. 4. Department of Nuclear Medicine, SMG-SNU Boramae Medical Center, Boramae-Ro 5-Gil, Shindaebang-dong, Dongjak-gu, Seoul, 07061, Republic of Korea. yk3181@snu.ac.kr. 5. Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, 03080, Republic of Korea. yk3181@snu.ac.kr. 6. Department of Psychiatry, Sanggye Paik Hospital, Inje University College of Medicine, Seoul, 01757, Republic of Korea. 7. Department of Psychiatry, Seoul National University College of Medicine, Seoul, 03080, Republic of Korea. 8. Institute of Human Behavioral Medicine, Medical Research Center, Seoul National University, Seoul, 03080, Republic of Korea. 9. Division for Healthcare Technology Assessment Research, National Evidence-based Healthcare Collaborating Agency, Seoul, 04554, Republic of Korea. 10. Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, 03080, Republic of Korea. 11. Department of Nuclear Medicine, Yonsei University College of Medicine, Gangnam Severance Hospital, Seoul, 06273, Republic of Korea.
Abstract
BACKGROUND: Fluorodeoxyglucose (FDG) positron emission tomography (PET) is useful to predict Alzheimer's disease (AD) conversion in patients with mild cognitive impairment (MCI). However, few studies have examined the extent to which FDG PET alone can predict AD conversion and compared the efficacy between visual and computer-assisted analysis directly. OBJECTIVE: The current study aimed to evaluate the value of FDG PET in predicting the conversion to AD in patients with MCI and to compare the predictive values of visual reading and computer-assisted analysis. METHODS AND MATERIALS: A total of 54 patients with MCI were evaluated with FDG PET and followed-up for 2 years with final diagnostic evaluation. FDG PET images were evaluated by (1) traditional visual rating, (2) composite score of visual rating of the brain cortices, and (3) composite score of computer-assisted analysis. Receiver operating characteristics (ROC) curves were compared to analyze predictive values. RESULTS: Nineteen patients (35.2%) converted to AD from MCI. The area under the curve (AUC) of the ROC curve of the traditional visual rating, composite score of visual rating, and computer-assisted analysis were 0.67, 0.76, and 0.79, respectively. ROC curves of the composite scores of the visual rating and computer-assisted analysis were comparable (Z = 0.463, p = 0.643). CONCLUSIONS: Visual rating and computer-assisted analysis of FDG PET scans were analogously accurate in predicting AD conversion in patients with MCI. Therefore, FDG PET may be a useful tool for screening AD conversion in patients with MCI, when using composite score, regardless of the method of interpretation.
BACKGROUND:Fluorodeoxyglucose (FDG) positron emission tomography (PET) is useful to predict Alzheimer's disease (AD) conversion in patients with mild cognitive impairment (MCI). However, few studies have examined the extent to which FDG PET alone can predict AD conversion and compared the efficacy between visual and computer-assisted analysis directly. OBJECTIVE: The current study aimed to evaluate the value of FDG PET in predicting the conversion to AD in patients with MCI and to compare the predictive values of visual reading and computer-assisted analysis. METHODS AND MATERIALS: A total of 54 patients with MCI were evaluated with FDG PET and followed-up for 2 years with final diagnostic evaluation. FDG PET images were evaluated by (1) traditional visual rating, (2) composite score of visual rating of the brain cortices, and (3) composite score of computer-assisted analysis. Receiver operating characteristics (ROC) curves were compared to analyze predictive values. RESULTS: Nineteen patients (35.2%) converted to AD from MCI. The area under the curve (AUC) of the ROC curve of the traditional visual rating, composite score of visual rating, and computer-assisted analysis were 0.67, 0.76, and 0.79, respectively. ROC curves of the composite scores of the visual rating and computer-assisted analysis were comparable (Z = 0.463, p = 0.643). CONCLUSIONS: Visual rating and computer-assisted analysis of FDG PET scans were analogously accurate in predicting AD conversion in patients with MCI. Therefore, FDG PET may be a useful tool for screening AD conversion in patients with MCI, when using composite score, regardless of the method of interpretation.
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