{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T21:53:41Z","timestamp":1791496421621,"version":"4.3.4"},"reference-count":39,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T00:00:00Z","timestamp":1753833600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea (NRF)","doi-asserted-by":"publisher","award":["NRF-2021R1I1A1A01059759"],"award-info":[{"award-number":["NRF-2021R1I1A1A01059759"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Tomography"],"abstract":"<jats:p>Background: Despite the growing demand for amyloid PET quantification, practical challenges remain. As automated software platforms are increasingly adopted to address these limitations, we evaluated the reliability of commercial tools for Centiloid quantification against the original Centiloid Project method. Methods: This retrospective study included 332 amyloid PET scans (165 [18F]Florbetaben; 167 [18F]Flutemetamol) performed for suspected mild cognitive impairments or dementia, paired with T1-weighted MRI within one year. Centiloid values were calculated using three automated software platforms, BTXBrain, MIMneuro, and SCALE PET, and compared with the original Centiloid method. The agreement was assessed using Pearson\u2019s correlation coefficient, the intraclass correlation coefficient (ICC), a Passing\u2013Bablok regression, and Bland\u2013Altman plots. The concordance with the visual interpretation was evaluated using receiver operating characteristic (ROC) curves. Results: BTXBrain (R = 0.993; ICC = 0.986) and SCALE PET (R = 0.992; ICC = 0.991) demonstrated an excellent correlation with the reference, while MIMneuro showed a slightly lower agreement (R = 0.974; ICC = 0.966). BTXBrain exhibited a proportional underestimation (slope = 0.872 [0.860\u20130.885]), MIMneuro showed a significant overestimation (slope = 1.053 [1.026\u20131.081]), and SCALE PET demonstrated a minimal bias (slope = 1.014 [0.999\u20131.029]). The bias pattern was particularly noted for FMM. All platforms maintained their trends for correlations and biases when focusing on subthreshold-to-low-positive ranges (0\u201350 Centiloid units). However, all platforms showed an excellent agreement with the visual interpretation (areas under ROC curves &gt; 0.996 for all). Conclusions: Three automated platforms demonstrated an acceptable reliability for Centiloid quantification, although software-specific biases were observed. These differences did not impair their feasibility in aiding the image interpretation, as supported by the concordance with visual readings. Nevertheless, users should recognize the platform-specific characteristics when applying diagnostic thresholds or interpreting longitudinal changes.<\/jats:p>","DOI":"10.3390\/tomography11080086","type":"journal-article","created":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T16:09:27Z","timestamp":1753891767000},"page":"86","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Reliability of Automated Amyloid PET Quantification: Real-World Validation of Commercial Tools Against Centiloid Project Method"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6001-2140","authenticated-orcid":false,"given":"Yeon-koo","family":"Kang","sequence":"first","affiliation":[{"name":"Division of Nuclear Medicine, Department of Radiology, Seoul St. Mary\u2019s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jae Won","family":"Min","sequence":"additional","affiliation":[{"name":"Division of Nuclear Medicine, Department of Radiology, Seoul St. Mary\u2019s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Soo Jin","family":"Kwon","sequence":"additional","affiliation":[{"name":"Division of Nuclear Medicine, Department of Radiology, Eunpyeong St. Mary\u2019s Hospital, College of Medicine, The Catholic University of Korea, Seoul 03312, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seunggyun","family":"Ha","sequence":"additional","affiliation":[{"name":"Division of Nuclear Medicine, Department of Radiology, Seoul St. Mary\u2019s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1038\/s41582-024-00942-2","article-title":"Biomarker-based staging of Alzheimer disease: Rationale and clinical applications","volume":"20","author":"Therriault","year":"2024","journal-title":"Nat. Rev. Neurol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"13S","DOI":"10.2967\/jnumed.121.263195","article-title":"The Role of Amyloid PET in Imaging Neurodegenerative Disorders: A Review","volume":"63","author":"Chapleau","year":"2022","journal-title":"J. Nucl. Med."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1316","DOI":"10.2967\/jnumed.116.174615","article-title":"SNMMI Procedure Standard\/EANM Practice Guideline for Amyloid PET Imaging of the Brain 1.0","volume":"57","author":"Minoshima","year":"2016","journal-title":"J. Nucl. Med."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1007\/s00259-015-3228-x","article-title":"Diagnostic accuracy of 18F amyloid PET tracers for the diagnosis of Alzheimer\u2019s disease: A systematic review and meta-analysis","volume":"43","author":"Morris","year":"2016","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jalz.2014.07.003","article-title":"The Centiloid Project: Standardizing quantitative amyloid plaque estimation by PET","volume":"11","author":"Klunk","year":"2015","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1186\/s13550-018-0456-7","article-title":"Centiloid scaling for quantification of brain amyloid with [18F]flutemetamol using multiple processing methods","volume":"8","author":"Battle","year":"2018","journal-title":"EJNMMI Res."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2053","DOI":"10.1007\/s00259-017-3749-6","article-title":"18F-Florbetaben PET beta-amyloid binding expressed in Centiloids","volume":"44","author":"Rowe","year":"2017","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1565","DOI":"10.1016\/j.jalz.2018.06.1353","article-title":"Standardization of amyloid quantitation with florbetapir standardized uptake value ratios to the Centiloid scale","volume":"14","author":"Navitsky","year":"2018","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1736","DOI":"10.2967\/jnumed.114.153494","article-title":"A Semiautomated Method for Quantification of F 18 Florbetapir PET Images","volume":"56","author":"Joshi","year":"2015","journal-title":"J. Nucl. Med."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3508","DOI":"10.1007\/s00259-022-05784-y","article-title":"Quantification of amyloid PET for future clinical use: A state-of-the-art review","volume":"49","author":"Pemberton","year":"2022","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"9037","DOI":"10.1002\/alz.14336","article-title":"Centiloid recommendations for clinical context-of-use from the AMYPAD consortium","volume":"20","author":"Collij","year":"2024","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"670","DOI":"10.2967\/jnumed.123.265766","article-title":"Quantitative Brain Amyloid PET","volume":"65","author":"Jagust","year":"2024","journal-title":"J. Nucl. Med."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1056\/NEJMoa2212948","article-title":"Lecanemab in Early Alzheimer\u2019s Disease","volume":"388","author":"Swanson","year":"2023","journal-title":"N. Engl. J. Med."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1001\/jama.2023.13239","article-title":"Donanemab in Early Symptomatic Alzheimer Disease: The TRAILBLAZER-ALZ 2 Randomized Clinical Trial","volume":"330","author":"Sims","year":"2023","journal-title":"JAMA"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1691","DOI":"10.1056\/NEJMoa2100708","article-title":"Donanemab in Early Alzheimer\u2019s Disease","volume":"384","author":"Mintun","year":"2021","journal-title":"N. Engl. J. Med."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"725","DOI":"10.1007\/s00259-015-2988-7","article-title":"Quantification of 18F-florbetapir PET: Comparison of two analysis methods","volume":"42","author":"Hutton","year":"2015","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"682","DOI":"10.1007\/s12149-016-1115-6","article-title":"Automated quantification of amyloid positron emission tomography: A comparison of PMOD and MIMneuro","volume":"30","author":"Choi","year":"2016","journal-title":"Ann. Nucl. Med."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"444","DOI":"10.1002\/alz.12668","article-title":"Quantification of amyloid beta and tau PET without a structural MRI","volume":"19","author":"Landau","year":"2023","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3276","DOI":"10.1007\/s00259-023-06279-0","article-title":"Validation of quantitative assessment of florbetaben PET scans as an adjunct to the visual assessment across 15 software methods","volume":"50","author":"Jovalekic","year":"2023","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Roh, H.W., Son, S.J., Hong, C.H., Moon, S.Y., Lee, S.M., Seo, S.W., Choi, S.H., Kim, E.-J., Cho, S.H., and Kim, B.C. (2023). Comparison of automated quantification of amyloid deposition between PMOD and Heuron. Sci. Rep., 13.","DOI":"10.1038\/s41598-023-36986-5"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Kim, S., Wang, S.-M., Kang, D.W., Um, Y.H., Han, E.J., Park, S.Y., Ha, S., Choe, Y.S., Kim, H.W., and Kim, R.E. (2024). A Comparative Analysis of Two Automated Quantification Methods for Regional Cerebral Amyloid Retention: PET-Only and PET-and-MRI-Based Methods. Int. J. Mol. Sci., 25.","DOI":"10.3390\/ijms25147649"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"460","DOI":"10.1007\/s12149-024-01919-3","article-title":"Comparison of consistency in centiloid scale among different analytical methods in amyloid PET: The CapAIBL, VIZCalc, and Amyquant methods","volume":"38","author":"Shang","year":"2024","journal-title":"Ann. Nucl. Med."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"659","DOI":"10.2967\/jnumed.122.264414","article-title":"Fast and Accurate Amyloid Brain PET Quantification Without MRI Using Deep Neural Networks","volume":"64","author":"Kang","year":"2023","journal-title":"J. Nucl. Med."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1001\/jama.2010.2008","article-title":"Use of Florbetapir-PET for Imaging \u03b2-Amyloid Pathology","volume":"305","author":"Clark","year":"2011","journal-title":"JAMA"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Lee, J., Ha, S., Kim, R.E.Y., Lee, M., Kim, D., and Lim, H.K. (2022). Development of Amyloid PET Analysis Pipeline Using Deep Learning-Based Brain MRI Segmentation\u2014A Comparative Validation Study. Diagnostics, 12.","DOI":"10.3390\/diagnostics12030623"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1233","DOI":"10.2967\/jnumed.115.171595","article-title":"Standardized Expression of 18F-NAV4694 and 11C-PiB \u03b2-Amyloid PET Results with the Centiloid Scale","volume":"57","author":"Rowe","year":"2016","journal-title":"J. Nucl. Med."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2397","DOI":"10.1002\/alz.12886","article-title":"Quantification of [18F]florbetaben amyloid-PET imaging in a mixed memory clinic population: The ABIDE project","volume":"19","author":"Collij","year":"2023","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2169","DOI":"10.1007\/s00259-020-05174-2","article-title":"Visual assessment of [18F]flutemetamol PET images can detect early amyloid pathology and grade its extent","volume":"48","author":"Collij","year":"2021","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"e619","DOI":"10.1212\/WNL.0000000000011214","article-title":"Defining the Lowest Threshold for Amyloid-PET to Predict Future Cognitive Decline and Amyloid Accumulation","volume":"96","author":"Farrell","year":"2021","journal-title":"Neurology"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3429","DOI":"10.1002\/alz.13761","article-title":"Investigating reliable amyloid accumulation in Centiloids: Results from the AMYPAD Prognostic and Natural History Study","volume":"20","author":"Bollack","year":"2024","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1862","DOI":"10.1056\/NEJMoa2304430","article-title":"Two Phase 3 Trials of Gantenerumab in Early Alzheimer\u2019s Disease","volume":"389","author":"Bateman","year":"2023","journal-title":"N. Engl. J. Med."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"197","DOI":"10.14283\/jpad.2022.30","article-title":"Two Randomized Phase 3 Studies of Aducanumab in Early Alzheimer\u2019s Disease","volume":"9","author":"Aisen","year":"2022","journal-title":"J. Prev. Alzheimer\u2019s Dis."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"5143","DOI":"10.1002\/alz.13859","article-title":"Revised criteria for diagnosis and staging of Alzheimer\u2019s disease: Alzheimer\u2019s Association Workgroup","volume":"20","author":"Jack","year":"2024","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"e14338","DOI":"10.1002\/alz.14338","article-title":"Updated appropriate use criteria for amyloid and tau PET: A report from the Alzheimer\u2019s Association and Society for Nuclear Medicine and Molecular Imaging Workgroup","volume":"21","author":"Rabinovici","year":"2025","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1007\/s13139-024-00848-3","article-title":"Anti-amyloid Antibody Therapies for Alzheimer\u2019s Disease","volume":"58","author":"Park","year":"2024","journal-title":"Nucl. Med. Mol. Imaging"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1007\/s13139-024-00861-6","article-title":"Clinical Performance Evaluation of an Artificial Intelligence-Powered Amyloid Brain PET Quantification Method","volume":"58","author":"Kang","year":"2024","journal-title":"Nucl. Med. Mol. Imaging"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1007\/s00259-020-04942-4","article-title":"Defining a Centiloid scale threshold predicting long-term progression to dementia in patients attending the memory clinic: An [18F] flutemetamol amyloid PET study","volume":"48","author":"Hanseeuw","year":"2021","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1186\/s13195-021-00836-1","article-title":"Validation of amyloid PET positivity thresholds in centiloids: A multisite PET study approach","volume":"13","author":"Royse","year":"2021","journal-title":"Alzheimers Res. Ther."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"968","DOI":"10.1016\/j.neuroimage.2006.01.021","article-title":"An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest","volume":"31","author":"Desikan","year":"2006","journal-title":"Neuroimage"}],"container-title":["Tomography"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2379-139X\/11\/8\/86\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:19:22Z","timestamp":1760033962000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2379-139X\/11\/8\/86"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,30]]},"references-count":39,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2025,8]]}},"alternative-id":["tomography11080086"],"URL":"https:\/\/doi.org\/10.3390\/tomography11080086","relation":{},"ISSN":["2379-139X"],"issn-type":[{"value":"2379-139X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,30]]}}}