Soo-Jong Kim1,2,3,4, Hongki Ham1,2,4,5, Yu Hyun Park1,2,3,4, Yeong Sim Choe1,2,3,4, Young Ju Kim1,2, Hyemin Jang1,2, Duk L Na1,2,3,6, Hee Jin Kim1,2, Seung Hwan Moon7, Sang Won Seo8,9,10,11,12. 1. Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea. 2. Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea. 3. Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea. 4. Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Republic of Korea. 5. Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea. 6. Stem Cell and Regenerative Medicine Institute, Samsung Medical Center, Seoul, Republic of Korea. 7. Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea. 8. Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea. sw72.seo@samsung.com. 9. Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea. sw72.seo@samsung.com. 10. Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Republic of Korea. sw72.seo@samsung.com. 11. Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea. sw72.seo@samsung.com. 12. Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea. sw72.seo@samsung.com.
Abstract
BACKGROUND: The standard Centiloid (CL) method was proposed to harmonize and quantify global 18F-labeled amyloid beta (Aβ) PET ligands using MRI as an anatomical reference. However, there is need for harmonizing and quantifying regional Aβ uptakes between ligands using CT as an anatomical reference. In the present study, we developed and validated a CT-based regional direct comparison of 18F-florbetaben (FBB) and 18F-flutemetamol (FMM) Centiloid (rdcCL). METHODS: For development of MRI-based or CT-based rdcCLs, the cohort consisted of 63 subjects (20 young controls (YC) and 18 old controls (OC), and 25 participants with Alzheimer's disease dementia (ADD)). We performed a direct comparison of the FMM-FBB rdcCL method using MRI and CT images to define a common target region and the six regional VOIs of frontal, temporal, parietal, posterior cingulate, occipital, and striatal regions. Global and regional rdcCL scales were compared between MRI-based and CT-based methods. For clinical validation, the cohort consisted of 2245 subjects (627 CN, 933 MCI, and 685 ADD). RESULTS: Both MRI-based and CT-based rdcCL scales showed that FMM and FBB were highly correlated with each other, globally and regionally (R2 = 0.96~0.99). Both FMM and FBB showed that CT-based rdcCL scales were highly correlated with MRI-based rdcCL scales (R2 = 0.97~0.99). Regarding the absolute difference of rdcCLs between FMM and FBB, the CT-based method was not different from the MRI-based method, globally or regionally (p value = 0.07~0.95). In our clinical validation study, the global negative group showed that the regional positive subgroup had worse neuropsychological performance than the regional negative subgroup (p < 0.05). The global positive group also showed that the striatal positive subgroup had worse neuropsychological performance than the striatal negative subgroup (p < 0.05). CONCLUSIONS: Our findings suggest that it is feasible to convert regional FMM or FBB rdcSUVR values into rdcCL scales without additional MRI scans. This allows a more easily accessible method for researchers that can be applicable to a variety of different conditions.
BACKGROUND: The standard Centiloid (CL) method was proposed to harmonize and quantify global 18F-labeled amyloid beta (Aβ) PET ligands using MRI as an anatomical reference. However, there is need for harmonizing and quantifying regional Aβ uptakes between ligands using CT as an anatomical reference. In the present study, we developed and validated a CT-based regional direct comparison of 18F-florbetaben (FBB) and 18F-flutemetamol (FMM) Centiloid (rdcCL). METHODS: For development of MRI-based or CT-based rdcCLs, the cohort consisted of 63 subjects (20 young controls (YC) and 18 old controls (OC), and 25 participants with Alzheimer's disease dementia (ADD)). We performed a direct comparison of the FMM-FBB rdcCL method using MRI and CT images to define a common target region and the six regional VOIs of frontal, temporal, parietal, posterior cingulate, occipital, and striatal regions. Global and regional rdcCL scales were compared between MRI-based and CT-based methods. For clinical validation, the cohort consisted of 2245 subjects (627 CN, 933 MCI, and 685 ADD). RESULTS: Both MRI-based and CT-based rdcCL scales showed that FMM and FBB were highly correlated with each other, globally and regionally (R2 = 0.96~0.99). Both FMM and FBB showed that CT-based rdcCL scales were highly correlated with MRI-based rdcCL scales (R2 = 0.97~0.99). Regarding the absolute difference of rdcCLs between FMM and FBB, the CT-based method was not different from the MRI-based method, globally or regionally (p value = 0.07~0.95). In our clinical validation study, the global negative group showed that the regional positive subgroup had worse neuropsychological performance than the regional negative subgroup (p < 0.05). The global positive group also showed that the striatal positive subgroup had worse neuropsychological performance than the striatal negative subgroup (p < 0.05). CONCLUSIONS: Our findings suggest that it is feasible to convert regional FMM or FBB rdcSUVR values into rdcCL scales without additional MRI scans. This allows a more easily accessible method for researchers that can be applicable to a variety of different conditions.
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Authors: Robert J Russo; Heather S Costa; Patricia D Silva; Jeffrey L Anderson; Aysha Arshad; Robert W W Biederman; Noel G Boyle; Jennifer V Frabizzio; Ulrika Birgersdotter-Green; Steven L Higgins; Rachel Lampert; Christian E Machado; Edward T Martin; Andrew L Rivard; Jason C Rubenstein; Raymond H M Schaerf; Jennifer D Schwartz; Dipan J Shah; Gery F Tomassoni; Gail T Tominaga; Allison E Tonkin; Seth Uretsky; Steven D Wolff Journal: N Engl J Med Date: 2017-02-23 Impact factor: 91.245
Authors: Mark R Battle; Lovena Chedumbarum Pillay; Val J Lowe; David Knopman; Bradley Kemp; Christopher C Rowe; Vincent Doré; Victor L Villemagne; Christopher J Buckley Journal: EJNMMI Res Date: 2018-12-05 Impact factor: 3.138
Authors: Sung Hoon Kang; Yu Hyun Park; Daun Lee; Jun Pyo Kim; Juhee Chin; Yisuh Ahn; Seong Beom Park; Hee Jin Kim; Hyemin Jang; Young Hee Jung; Jaeho Kim; Jongmin Lee; Ji-Sun Kim; Bo Kyoung Cheon; Alice Hahn; Hyejoo Lee; Duk L Na; Young Ju Kim; Sang Won Seo Journal: Dement Neurocogn Disord Date: 2019-10-02